Sequential recommendation aims to model users' evolving preferences based on their historical interactions. Recent advances leverage Transformer-based architectures to capture global dependencies, but existing methods often suffer from high computational overhead, primarily due to discontinuous memory access in temporal encoding and dense attention over long sequences. To address these limitations, we propose FuXi-γ, a novel sequential recommendation framework that improves both effectiveness and efficiency through principled architectural design. FuXi-γ adopts a decoder-only Transformer structure and introduces two key innovations: (1) An exponential-power temporal encoder that encodes relative temporal intervals using a tunable exponential decay function inspired by the Ebbinghaus forgetting curve. This encoder enables flexible modeling of both short-term and long-term preferences while maintaining high efficiency through continuous memory access and pure matrix operations. (2) A diagonal-sparse positional mechanism that prunes low-contribution attention blocks using a diagonal-sliding strategy guided by the persymmetry of Toeplitz matrix. Extensive experiments on four real-world datasets demonstrate that FuXi-γ achieves state-of-the-art performance in recommendation quality, while accelerating training by up to 4.74× and inference by up to 6.18×, making it a practical and scalable solution for long-sequence recommendation. Code: https://github.com/Yeedzhi/FuXi-gamma.

As an emerging advertising pricing method, optimized cost-per-click (OCPC) has attracted much research interest. In OCPC, the platform employs intelligent bidding strategies to optimize the advertising performance. Although widely adopted, existing bidding strategies overlook the delayed conversion phenomenon in OCPC, that is, the platform needs to wait for a period to receive the corresponding conversion signal after a click. Ignoring such delayed conversions causes the bidding strategies to overestimate the cost-per-action (average cost of a conversion, CPA), bid low, and finally hurt the platform's revenue. Moreover, the characteristics of the OCPC scenario make estimating the conversion probabilities for the delayed conversions more difficult. To address these issues, this paper proposes SurvBid (bidding with Survival Analysis) which aims to predict the convert probabilities for those delayed conversions in OCPC scenario. The CPA can then be accurately estimated and used to guide existing bidding methods to make more accurate bids. To meet the needs of different advertising platforms, we provide two versions of SurvBid, SurvBid-M (SurvBid with multitask survival model) and SurvBid-C (SurvBid with Cox survival model) with theoretical results to guide the model selection. Both online and offline experiments demonstrate that SurvBid can improve the platform's revenue and advertisers' conversions.

Large language models have given rise to interactive recommendation agents (IRAs). Through proactive clarification, tool invocation, and dynamic dialogue, IRAs shift recommender systems from passive prediction to interactive, proactive intelligence. For training IRAs, agentic reinforcement learning offers a natural pathway, as it enables models to learn interactive capabilities directly from environmental feedback without requiring costly annotated data. However, this process faces three key challenges: credit assignment in multi-turn interactions, efficient exploration in large action spaces, and coordinated learning of multiple interactive skills.

Large-scale online marketplaces and recommender systems are crucial technological foundations for the development of e-commerce. In industrial recommender systems, features play a vital role as they carry essential information for downstream models. Accurate estimation of feature importance is critical, as it helps identify the most useful feature subsets from thousands of candidates for online services. Such a selection enables optimization of online performance while reducing computational burden. To address the feature selection challenges in deep learning, trainable gate-based and sensitivity-based methods have been proposed and proven effective in the industry. However, by analyzing real-world examples, we identified three bias issues that cause feature importance estimation to rely on partial model layers, samples, or gradients, ultimately leading to inaccurate feature importance estimates. We refer to these biases as layer bias, baseline bias, and approximation bias. To mitigate these biases, we propose FairFS, a fair and accurate feature selection algorithm. On one hand, FairFS directly regularizes feature importance estimation across all non-linear transformational layers to avoid layer bias. On the other hand, it employs a smooth baseline feature close to the classifier's decision boundary and an aggregated approximation method to mitigate bias issues. Extensive experiments demonstrate how FairFS mitigates these three biases and achieves state-of-the-art feature selection results.

With the rise of generative paradigms, generative recommendation has garnered increasing attention. The core component is the item code , generally derived by quantizing collaborative or semantic representations to serve as candidate items identifiers in the context. However, existing methods typically construct separate codes for each modality, leading to higher computational and storage costs and hindering the integration of their complementary strengths. Considering this limitation, we seek to integrate two different modalities into a unified code , fully unleashing the potential of complementary nature among modalities. Nevertheless, the integration remains challenging: the integrated embedding obtained by the common concatenation method would lead to underutilization of collaborative knowledge, thereby resulting in limited effectiveness. To address this, we propose a novel method, named UNGER, which integrates semantic and collaborative knowledge into a UNified code for Generative Recommendation . Specifically, we propose to adaptively learn an integrated embedding through the joint optimization of cross-modality knowledge alignment and next-item prediction tasks. Subsequently, to mitigate the information loss caused by the quantization process, we introduce an intra-modality knowledge distillation task, using the integrated embeddings as supervised signals to compensate. Extensive experiments on three widely used benchmarks demonstrate the superiority of our approach compared to existing methods.

In multi-stakeholder recommender systems (RS), users and providers operate as two crucial and interdependent roles, whose interests must be well-balanced. Prior research has demonstrated the importance of guaranteeing both provider fairness and user accuracy to meet their interests. However, when balancing the two objectives, another critical factor emerges: individual fairness, which manifests as a significant disparity in individual recommendation accuracy, with some users receiving high accuracy while others are left with notably low accuracy. This oversight severely harms the interests of users and exacerbates social polarization. How to guarantee individual fairness while ensuring user accuracy and provider fairness remains an unsolved problem. To bridge this gap, this article proposes a method called BankFair+, which extends BankFair with two steps: (1) introducing a non-linear function from regret theory to ensure individual fairness while enhancing user accuracy; (2) formulating the re-ranking process as a regret-aware fuzzy programming problem to meet the interests of both individual users and providers, therefore balancing the tradeoff between individual fairness and provider fairness. Experiments on two real-world recommendation datasets demonstrate that BankFair+ outperforms all baselines regarding individual fairness, user accuracy, and provider fairness, indicating its ability to guarantee two-sided fairness and accuracy in RS.

Efficient and effective modeling of feature interactions is key to large-scale Click-Through Rate (CTR) prediction. Although existing feature interaction methods have improved the model accuracy, their computational consumption still increase exponentially with the number of feature fields and become severe efficiency bottleneck in real-world industrial scenarios. To address the issues, we propose an E fficient and E ffective NET work for large-scale CTR prediction named EENet . EENet presents a new alternating stacking architecture of implicit and explicit interaction layers, and each implicit layer in EENet can reduce both local computational and parameter load remarkably. EENet also designs a unified explicit interaction operation which can only use simple matrix multiplication to capture field-wise patterns. Moreover, the order of multiplications in EENet is rearranged to further decrease the computational complexity from quadratic to linear with respect to the number of feature fields. EENet thus can support the high efficiency in real-practice industrial scenarios with hundreds of feature fields. A set of extensive experiments is performed on two public datasets and one industrial dataset for effectiveness evaluation, and five larger-scale synthetic datasets for efficiency evaluation. The results highlight that our EENet can significantly outperform the state-of-the-art models in terms of both efficiency and scalability, while also maintaining superior effectiveness. Compared with DCNv2 and FiBiNet, EENet achieves 8.06 \(\times\) and 36.72 \(\times\) efficiency improvements in training, and 2.02 \(\times\) and 48.88 \(\times\) improvements in inference, respectively. Our solution and source code are available at https://github.com/Yeedzhi/EENet .

Large Language Models (LLMs) have exhibited significant promise in recommender systems by empowering user profiles with their extensive world knowledge and superior reasoning capabilities. However, LLMs face challenges like unstable instruction compliance, modality gaps, and high inference latency, leading to textual noise and limiting their effectiveness in recommender systems. To address these challenges, we propose UserIP-Tuning, which uses prompt-tuning to infer user profiles. It integrates the causal relationship between user profiles and behavior sequences into LLMs' prompts. It employs Expectation Maximization (EM) to infer the embedded latent profile, minimizing textual noise by fixing the prompt template. Furthermore, a profile quantization codebook bridges the modality gap by categorizing profile embeddings into collaborative IDs pre-stored for online deployment. This improves time efficiency and reduces memory usage. Experiments show that UserIP-Tuning outperforms state-of-the-art recommendation algorithms. An industry application confirms its effectiveness, robustness, and transferability. The presented solution has been deployed in Huawei AppGallery's Explore page since May 2025, serving 2 million daily active users, delivering significant improvements in real-world recommendation scenarios. The code is publicly available for replication at https://github.com/Applied-Machine-Learning-Lab/UserIP-Tuning.

Recent advances in retrieval-augmented generation (RAG) furnish large language models (LLMs) with iterative retrievals of relevant information to handle complex multi-hop questions. These methods typically alternate between LLM reasoning and retrieval to accumulate external information into the LLM's context. However, the ever-growing context inherently imposes an increasing burden on the LLM to perceive connections among critical information pieces, with futile reasoning steps further exacerbating this overload issue. In this paper, we present KnowTrace, an elegant RAG framework to (1) mitigate the context overload and (2) bootstrap higher-quality multi-step reasoning. Instead of simply piling the retrieved contents, KnowTrace autonomously traces out desired knowledge triplets to organize a specific knowledge graph relevant to the input question. Such a structured workflow not only empowers the LLM with an intelligible context for inference, but also naturally inspires a reflective mechanism of knowledge backtracing to identify contributive LLM generations as process supervision data for self-bootstrapping. Extensive experiments show that KnowTrace consistently surpasses existing methods across three multi-hop question answering benchmarks, and the bootstrapped version further amplifies the gains.

Feature interaction modeling is crucial for deep recommendation models. A common and effective approach is to construct explicit feature combinations to enhance model performance. However, in practice, only a small fraction of these combinations are truly informative. Thus it is essential to select useful feature combinations to reduce noise and manage memory consumption. While feature selection methods have been extensively studied, they are typically limited to selecting individual features. Extending these methods for high-order feature combination selection presents a significant challenge due to the exponential growth in time complexity when evaluating feature combinations one by one. In this paper, we propose TayFCS, a lightweight feature combination selection method that significantly improves model performance. Specifically, we propose the Taylor Expansion Scorer (TayScorer) module for field-wise Taylor expansion on the base model. Instead of evaluating all potential feature combinations' importance by repeatedly running experiments with feature adding and removal, this scorer only needs to approximate them based on their sub-components' gradients. They can be simply computed with one backward pass based on a trained recommendation model. To further reduce information redundancy between feature combinations and their sub-components, we introduce Logistic Regression Elimination (LRE) that estimates the information gain of feature combinations over their sub-components based on the above importance scores. Experimental results on three benchmark datasets validate both the effectiveness and efficiency of our approach. Furthermore, online A/B test results demonstrate its practical applicability and commercial value.

Recommender systems are essential for information access, allowing users to present their content for recommendation. With the rise of large language models (LLMs), AI-generated content (AIGC), primarily in the form of text, has become a central part of the content ecosystem. As AIGC becomes increasingly prevalent, it is important to understand how it affects the performance and dynamics of recommender systems. To this end, we construct an environment that incorporates AIGC to explore its short-term impact. The results from popular sequential recommendation models reveal that AIGC are ranked higher in the recommender system, reflecting the phenomenon of source. To further explore the long-term impact of AIGC, we introduce a feedback loop with realistic simulators. The results show that the model's preference for AIGC increases as the user clicks on AIGC rises and the model trains on simulated click data. This leads to two issues: In the short term, bias toward AIGC encourages LLM-based content creation, increasing AIGC content, and causing unfair traffic distribution. From a long-term perspective, our experiments also show that when AIGC dominates the content ecosystem after a feedback loop, it can lead to a decline in recommendation performance. To address these issues, we propose a debiasing method based on L1-loss optimization to maintain long-term content ecosystem balance. In a real-world environment with AIGC generated by mainstream LLMs, our method ensures a balance between AIGC and human-generated content in the ecosystem. The code and dataset are available at https://github.com/Yuqi-Zhou/Rec_SourceBias.

Maintaining the long-term sustainability of recommender systems (RS) is crucial.Traditional RS evaluation methods primarily focus on the user's immediate feedback (e.g., click), however, they often overlook the long-term effect involved by the content creators.In the real world, content creators can strategically create and upload new items to the platform by analyzing users' feedback and preference trends.Although previous studies have attempted to model creator behaviors, they often overlook that such behaviors are under conditions of information asymmetry.This asymmetry arises because creators mainly access the user feedback on the items they produce, while the platform has access to the full spectrum of feedback data.However, existing RS simulators often fail to consider such a condition, making the long-term RS evaluation inaccurate.To bridge this gap, we propose a Large Language Model (LLM)empowered creator simulation agent named CreAgent.By utilizing the belief mechanism from game theory and the fast-and-slow thinking framework, we can simulate the creator's behaviors well under information asymmetry.Furthermore, to enhance CreAgent's simulation ability, we utilize Proximal Policy Optimization to fine-tune CreAgent.Our credibility validation experiments demonstrate that our simulation environment effectively aligns with the behaviors of real-world platforms and creators, thereby enhancing the reliability of long-term evaluations in RS.Furthermore, leveraging this simulator, we can examine whether RS algorithms, such as fairnessand diversity-aware methods, contribute to improving long-term performance for different stakeholders.

With the rapid advancements of large language models (LLMs), information retrieval (IR) systems, such as search engines and recommender systems, have undergone a paradigm shift due to their integration. However, integrating LLMs into the IR pipelines has also introduced new challenges, particularly in the form of biases and unfairness that may disrupt the information ecosystem. This tutorial will offer a comprehensive overview of emerging and pressing bias and unfairness issues associated with integrating LLMs into IR systems. Specifically, this tutorial first unifies bias and unfairness issues as problems of distribution mismatch and further categorizes the mitigation strategies under the umbrella of distribution alignment. Then, we summarize several types of bias and unfairness issues emerging from three critical stages of LLM integration into IR systems: data collection, model development, and result evaluation. We will systematically review and analyze their definitions, characteristics, and corresponding mitigation strategies in recent literature. Finally, we will highlight some open problems and future research directions. We hope this tutorial can raise the awareness of researchers and stakeholders in the IR field and beyond regarding bias and unfairness issues in this LLM era.

In online advertising, uncertainty calibration aims to adjust a ranking model's probability predictions to better approximate the true likelihood of an event, e.g., a click or a conversion. However, existing calibration approaches may lack the ability to effectively model complex nonlinear relations, consider context features, and achieve balanced performance across different data subsets. To tackle these challenges, we introduce a novel model called Monotonic Calibration Networks, featuring three key designs: a monotonic calibration function (MCF), an order-preserving regularizer, and a field-balance regularizer. The nonlinear MCF is capable of naturally modeling and universally approximating the intricate relations between uncalibrated predictions and the posterior probabilities, thus being much more expressive than existing methods. MCF can also integrate context features using a flexible model architecture, thereby achieving context awareness. The order-preserving and field-balance regularizers promote the monotonic relationship between adjacent bins and the balanced calibration performance on data subsets, respectively. Experimental results on both public and industrial datasets demonstrate the superior performance of our method in generating well-calibrated probability predictions.

Large language models (LLMs) are increasingly leveraged as foundational backbones in the development of advanced recommender systems, offering enhanced capabilities through their extensive knowledge and reasoning.Existing llm-based recommender systems (RSs) often face challenges due to the significant differences between the linguistic semantics of pre-trained LLMs and the collaborative semantics essential for RSs.These systems use pre-trained linguistic semantics but learn collaborative semantics from scratch via the llm-Backbone.However, LLMs are not designed for recommendations, leading to inefficient collaborative learning, weak result correlations, and poor integration of traditional RS features.To address these challenges, we propose EAGER-LLM, a decoder-only llm-based generative recommendation framework that integrates endogenous and exogenous behavioral and semantic information in a non-intrusive manner.Specifically, we propose 1) dual-source knowledge-rich item indices that integrates indexing sequences * Both authors contributed equally to this research.

Large language model (LLM)-based agents have recently attracted much attention from the research and industry communities. Compared with original LLMs, LLM-based agents are featured in their self-evolving capability, which is the basis for solving real-world problems that need long-term and complex agent-environment interactions. The key component to support agent-environment interactions is the memory of the agents. While previous studies have proposed many promising memory mechanisms, they are scattered in different papers, and there lacks a systematical review to summarize and compare these works from a holistic perspective, failing to abstract common and effective designing patterns for inspiring future studies. To bridge this gap, in this article, we propose a comprehensive survey on the memory mechanism of LLM-based agents. In specific, we first discuss “what is” and “why do we need” the memory in LLM-based agents. Then, we systematically review previous studies on how to design and evaluate the memory module. In addition, we also present many agent applications, where the memory module plays an important role. At last, we analyze the limitations of existing work and show important future directions. To keep up with the latest advances in this field, we create a repository at https://github.com/nuster1128/LLM_Agent_Memory_Survey .

Nowadays, optimized cost-per-click (OCPC) has been widely adopted in online advertising. In OCPC, the advertiser sets an expected cost-per-conversion and pays per click, while the platform automatically adjusts the bid on each click to meet advertiser’s constraint. Existing bidding methods are based on feedback control, adjusting bids to keep the current cost-per-conversion close to the expected cost-per-conversion to avoid compensation. However, they overlook the conversion lag phenomenon: There always exists a time interval between the ad’s click time and conversion time. This interval makes existing methods overestimate the cost-per-conversion and results in over conservative bidding policies which finally hurts the revenue. To address the issue, this article proposes a novel bidding method, Bidding with Delayed Conversions (Bid-DC) which predicts the conversion probability of the clicked ads and used it to adjust the cost-per-conversion values. To ensure the bidding model can satisfy the advertiser’s constraint, constrained Markov decision process (CMDP) is adapted to automatically learn the optimal parameters from the log data. Both online and offline experiments demonstrate that Bid-DC outperforms the state-of-the-art baselines in terms of improving revenue. Empirical analysis also showed Bid-DC can accurately estimate the cost-per-conversion and make more stable bids.

Learning high-quality item embeddings is crucial for recommendation tasks such as matching and ranking. However, existing methods often rely on ID-based item embeddings learned end-to-end with downstream recommendation models, which may suffer from overfitting and limited generalizability. In this paper, we aim to learn universal item embeddings (dubbed UniEmbedding) that capture multi-modal semantics, generalize across multiple domains, and serve different downstream tasks. To achieve this goal, we introduce the UniEmbedding pretraining framework, which includes three modules: a domain-aware multi-modal adapter, a user-view projection module, and contrastive learning objectives across domains. Compared to naive ID embeddings, UniEmbedding provides rich semantic information that generalizes more effectively across domains. Unlike multi-modal embeddings directly extracted from off-the-shelf pretrained models, UniEmbedding achieves better alignment between content semantics and behaviors. We evaluated UniEmbedding on both public and industrial datasets, demonstrating its effectiveness in matching and ranking tasks. Furthermore, UniEmbedding has been deployed in multiple recommendation applications at Huawei, resulting in significant gains in user engagement metrics.

Recent studies highlight the potential of large language models (LLMs) to enhance content integration in recommender systems by leveraging their semantic understanding capabilities. However, directly incorporating LLMs into an online inference pipeline significantly increases computation costs for large-scale deployment, posing a practical challenge in balancing their benefits and costs. In this work, we propose the EASE framework, which enriches and aligns semantic feature embeddings using LLMs during the training phase while establishing a lightweight inference pipeline that does not directly involve LLMs. Specifically, we train a semantic adapter to align item features with LLMs and simultaneously enrich semantic embeddings through reconstruction tasks from LLMs. During inference, we retain only the item feature encoder and lightweight semantic adapter, thereby eliminating the computation overhead of resource-intensive LLMs. Our EASE framework is flexible, supporting not only text and visual features but also other pre-processed embedding features. Extensive experiments on both public and industrial datasets demonstrate that enriching semantic feature embeddings with our EASE framework yields consistent improvements in downstream click-through rate prediction tasks.

Out of sustainable and economical considerations, two-sided recommendation platforms must satisfy the needs of both users and providers. Previous studies often show that the two sides' needs show different urgency: providers need a relatively long-term exposure demand while users want more short-term and accurate service. However, our empirical study reveals that previous methods for trading off fairness-accuracy often fail to guarantee long-term fairness and short-term accuracy simultaneously in real applications of fluctuating user traffic. Especially, when user traffic is low, the user experience often drops a lot. Our theoretical analysis also confirms that user traffic is a key factor in such a trade-off problem. How to guarantee accuracy and fairness under fluctuating user traffic remains a problem. Inspired by the bankruptcy problem in economics, we propose a novel fairness-aware re-ranking approach named BankFair. Intuitively, BankFair employs the Talmud rule to leverage periods of abundant user traffic to offset periods of user traffic scarcity, ensuring consistent user service at every period while upholding long-term fairness. Specifically, BankFair consists of two modules: (1) employing the Talmud rule to determine the required fairness degree under varying periods of user traffic; and (2) conducting an online re-ranking algorithm based on the fairness degree determined by the Talmud rule. Experiments on two real-world recommendation datasets show that BankFair outperforms all baselines regarding accuracy and provider fairness.

Accurately predicting the probabilities of user feedback, such as clicks and conversions, is critical for advertisement ranking and bidding. However, there often exist unwanted mismatches between predicted probabilities and true likelihoods due to the rapid shift of data distributions and intrinsic model biases. Calibration aims to address this issue by post-processing model predictions, and field-aware calibration can adjust model output on different feature field values to satisfy fine-grained advertising demands. Unfortunately, the observed samples corresponding to certain field values can be seriously limited to make confident calibrations, which may yield bias amplification and online disturbance. In this paper, we propose a confidence-aware multi-field calibration method, which adaptively adjusts the calibration intensity based on confidence levels derived from sample statistics. It also utilizes multiple fields for joint model calibration according to their importance to mitigate the impact of data sparsity on a single field. Extensive offline and online experiments show the superiority of our method in boosting advertising performance and reducing prediction deviations.

With the popularity of online music services, personalized music recommendation has garnered much research interest. Recommendation models are typically trained on datasets constructed from user feedback, which includes both the active feedback (e.g., clicking the Like or Skip buttons) and passive feedback (e.g., auto-play), with passive feedback comprising the majority. Due to the unavailability of user attention, the massive amount of passive feedback is unreliable, significantly compromising the quality of the training data. How to estimate the user's attention on the target music has become a critical problem in music recommendation. Heuristic methods such as exponential decay and negative sampling have been proposed. However, they either neglect the sequential dependencies between feedback actions or utilize only a small fraction of passive samples, leading to inaccurate and biased attention estimation. In this paper, we naturally propose modeling user attention prediction as a positive-unlabeled (PU) learning problem, where active feedback is treated as positive samples and passive feedback is treated as unlabeled samples, as we can only ensure that the user's attention is focused when she provides active feedback. Then we propose an extended PU-learning model with sequential dependencies, called UAE, which contains an unbiased user attention estimator and an unbiased propensity estimator. Subsequently, a joint learning algorithm is developed in which the attention and propensity estimators are optimized in alternating fashion. Theoretical analysis shows the unbiasedness and variance of the attention estimator and the propensity estimator. Extensive experiments on two large-scale datasets demonstrate the proposed UAE's effectiveness and generality in enhancing downstream music recommendation. One week online A/B testing on Huawei Music App manifests that UAE can significantly increase the users' play count and time over 2%, further demonstrating the effectiveness of UAE in real-world music recommendation products.

With the rapid advancements of large language models (LLMs), information retrieval (IR) systems, such as search engines and recommender systems, have undergone a significant paradigm shift. This evolution, while heralding new opportunities, introduces emerging challenges, particularly in terms of biases and unfairness, which may threaten the information ecosystem. In this paper, we present a comprehensive survey of existing works on emerging and pressing bias and unfairness issues in IR systems when the integration of LLMs. We first unify bias and unfairness issues as distribution mismatch problems, providing a groundwork for categorizing various mitigation strategies through distribution alignment. Subsequently, we systematically delve into the specific bias and unfairness issues arising from three critical stages of LLMs integration into IR systems: data collection, model development, and result evaluation. In doing so, we meticulously review and analyze recent literature, focusing on the definitions, characteristics, and corresponding mitigation strategies associated with these issues. Finally, we identify and highlight some open problems and challenges for future work, aiming to inspire researchers and stakeholders in the IR field and beyond to better understand and mitigate bias and unfairness issues of IR in this LLM era. We also consistently maintain a GitHub repository for the relevant papers and resources in this rising direction at https://github.com/KID-22/LLM-IR-Bias-Fairness-Survey.

Personalized recommendation serves as a ubiquitous channel for users to discover information tailored to their interests. However, traditional recommendation models primarily rely on unique IDs and categorical features for user-item matching, potentially overlooking the nuanced essence of raw item contents across multiple modalities such as text, image, audio, and video. This underutilization of multimodal data poses a limitation to recommender systems, especially in multimedia services like news, music, and short-video platforms. The recent advancements in large multimodal models offer new opportunities and challenges in developing content-aware recommender systems. This survey seeks to provide a comprehensive exploration of the latest advancements and future trajectories in multimodal pretraining, adaptation, and generation techniques, as well as their applications in enhancing recommender systems. Furthermore, we discuss current open challenges and opportunities for future research in this dynamic domain. We believe that this survey, alongside the curated resources, will provide valuable insights to inspire further advancements in this evolving landscape.

Generative retrieval has recently emerged as a promising approach to sequential recommendation, framing candidate item retrieval as an autoregressive sequence generation problem. However, existing generative methods typically focus solely on either behavioral or semantic aspects of item information, neglecting their complementary nature and thus resulting in limited effectiveness. To address this limitation, we introduce EAGER, a novel generative recommendation framework that seamlessly integrates both behavioral and semantic information. Specifically, we identify three key challenges in combining these two types of information: a unified generative architecture capable of handling two feature types, ensuring sufficient and independent learning for each type, and fostering subtle interactions that enhance collaborative information utilization. To achieve these goals, we propose (1) a two-stream generation architecture leveraging a shared encoder and two separate decoders to decode behavior tokens and semantic tokens with a confidence-based ranking strategy; (2) a global contrastive task with summary tokens to achieve discriminative decoding for each type of information; and (3) a semantic-guided transfer task designed to implicitly promote cross-interactions through reconstruction and estimation objectives. We validate the effectiveness of EAGER on four public benchmarks, demonstrating its superior performance compared to existing methods. Our source code will be publicly available on PapersWithCode.com.

In video recommendation, an ongoing effort is to satisfy users' personalized information needs by leveraging their logged watch time. However, watch time prediction suffers from duration bias, hindering its ability to reflect users' interests accurately. Existing label-correction approaches attempt to uncover user interests through grouping and normalizing observed watch time according to video duration. Although effective to some extent, we found that these approaches regard completely played records (i.e., a user watches the entire video) as equally high interest, which deviates from what we observed on real datasets: users have varied explicit feedback proportion when completely playing videos. In this paper, we introduce the counterfactual watch time (CWT), the potential watch time a user would spend on the video if its duration is sufficiently long. Analysis shows that the duration bias is caused by the truncation of CWT due to the video duration limitation, which usually occurs on those completely played records. Besides, a Counterfactual Watch Model (CWM) is proposed, revealing that CWT equals the time users get the maximum benefit from video recommender systems. Moreover, a cost-based transform function is defined to transform the CWT into the estimation of user interest, and the model can be learned by optimizing a counterfactual likelihood function defined over observed user watch times. Extensive experiments on three real video recommendation datasets and online A/B testing demonstrated that CWM effectively enhanced video recommendation accuracy and counteracted the duration bias.

Click-Through Rate (CTR) prediction is a fundamental technique for online advertising recommendation and the complex online competitive auction process also brings many difficulties to CTR optimization. Recent studies have shown that introducing posterior auction information contributes to the performance of CTR prediction. However, existing work doesn’t fully capitalize on the benefits of auction information and overlooks the data bias brought by the auction, leading to biased and suboptimal results. To address these limitations, we propose Auction Information Enhanced Framework (AIE) for CTR prediction in online advertising, which delves into the problem of insufficient utilization of auction signals and first reveals the auction bias. Specifically, AIE introduces two pluggable modules, namely Adaptive Market-price Auxiliary Module (AM2) and Bid Calibration Module (BCM), which work collaboratively to excavate the posterior auction signals better and enhance the performance of CTR prediction. Furthermore, the two proposed modules are lightweight, model-agnostic, and friendly to inference latency. Extensive experiments are conducted on a public dataset and an industrial dataset to demonstrate the effectiveness and compatibility of AIE. Besides, a one-month online A/B test in a large-scale advertising platform shows that AIE improves the base model by 5.76% and 2.44% in terms of eCPM and CTR, respectively.

Embedding-based retrieval serves as a dominant approach to candidate item matching for industrial recommender systems. With the success of generative AI, generative retrieval has recently emerged as a new retrieval paradigm for recommendation, which casts item retrieval as a generation problem. Its model consists of two stages: semantic tokenization and autoregressive generation. The first stage involves item tokenization that constructs discrete semantic tokens to index items, while the second stage autoregressively generates semantic tokens of candidate items. Therefore, semantic tokenization serves as a crucial preliminary step for training generative recommendation models. Existing research usually employs a vector quantizier with reconstruction loss (e.g., RQ-VAE) to obtain semantic tokens of items, but this method fails to capture the essential neighborhood relationships that are vital for effective item modeling in recommender systems. In this paper, we propose a contrastive quantization-based semantic tokenization approach, named CoST, which harnesses both item relationships and semantic information to learn semantic tokens. Our experimental results highlight the significant impact of semantic tokenization on generative recommendation performance, with CoST achieving up to a 43% improvement in Recall@5 and 44% improvement in NDCG@5 on the MIND dataset over previous baselines.

Providing human-understandable explanations for the matching predictions is still challenging for current legal case matching methods. One difficulty is that legal cases are semi-structured text documents with complicated case-case and case-law article correlations. To tackle the issue, we propose a novel graph optimal transport (GOT)-based legal case matching model that is able to provide not only the matching predictions but also plausible and faithful explanations for the prediction. The model, called GEIOT-Match, first constructs a heterogeneous graph to explicitly represent the semi-structured nature of legal cases and their associations with the law articles. Therefore, matching two legal cases amounts to identifying the rationales from the paired legal case sub-graphs in the heterogeneous graph and then aligning between them. An inverse optimal transport (IOT) model on graphs is learned to extract rationales from paired legal cases. The extracted rationales and the heterogeneous graph demonstrate the key legal characteristics of legal cases, which can be further used to conduct matching and generate explanations for the matching. Experimental results showed that GEIOT-Match outperformed state-of-the-art baselines in terms of matching prediction, rationale extraction, and natural language explanation generation.

Group-fair recommendation aims at ensuring the equality of recommendation results across user groups categorized by sensitive attributes (e.g., gender, occupation, etc.). Existing group-fair recommendation models traditionally employ original user embeddings for both training and testing, primarily focusing on statistical learning while imposing group fairness constraints under the I.I.D. assumption. However, these models encounter limitations when addressing out-of-distribution (OOD) sensitive attributes. The fundamental issue of unfairness within user embeddings arises from a causal perspective, where each embedding vector comprises an exogenous component devoid of correlations with sensitive attributes and an endogenous component strongly correlated with these attributes. Overlooking the distinction between these two components during model training renders models sensitive to shifts in the distribution of sensitive attributes. This paper introduces the concept of Counterfactual Group Fairness (CGF) along with a corresponding metric to evaluate group fairness in scenarios involving OOD sensitive attributes in recommender systems. Building on this foundation, we propose a model-agnostic causal embedding learning framework named MACE. MACE effectively disentangles user embedding vectors into their exogenous and endogenous parts, thus ensuring group fairness, even in the presence of OOD sensitive attributes in embeddings. Specifically, MACE identifies the exogenous part of each user's embedding using mutual information minimization, treating it as instrumental variables. Subsequently, under the constraint of CGF, MACE reconstructs the endogenous and exogenous parts using the instrumental variable regression, combines the obtained parts into novel user embeddings using deep neural networks, and uses the combined embeddings for fair recommendation. Experimental results demonstrated that MACE can outperform the state-of-the-art baselines in terms of the metric of CGF while maintaining a comparable recommendation accuracy.

Matching two natural language sentences is a fundamental problem in both natural language processing and information retrieval. Preliminary studies have shown that the syntactic structures help improve the matching accuracy, and different syntactic structures in natural language are complementary to sentence semantic understanding. Ideally, a matching model would leverage all syntactic information. Existing models, however, are only able to combine limited (usually one) types of syntactic information due to the complex and heterogeneous nature of the syntactic information. To deal with the problem, we propose a novel matching model, which formulates sentence matching as a representation learning task on a syntactic-informed heterogeneous graph. The model, referred to as SIGN (Syntactic-Informed Graph Network), first constructs a heterogeneous matching graph based on the multiple syntactic structures of two input sentences. Then the graph attention network algorithm is applied to the matching graph to learn the high-level representations of the nodes. With the help of the graph learning framework, the multiple syntactic structures, as well as the word semantics, can be represented and interacted in the matching graph and therefore collectively enhance the matching accuracy. We conducted comprehensive experiments on three public datasets. The results demonstrate that SIGN outperforms the state of the art and also can discriminate the sentences in an interpretable way.

The key of sequential recommendation lies in the accurate item correlation modeling. Previous models infer such information based on item co-occurrences, which may fail to capture the real causal relations, and impact the recommendation performance and explainability. In this paper, we equip sequential recommendation with a novel causal discovery module to capture causalities among user behaviors. Our general idea is firstly assuming a causal graph underlying item correlations, and then we learn the causal graph jointly with the sequential recommender model by fitting the real user behavior data. More specifically, in order to satisfy the causality requirement, the causal graph is regularized by a differentiable directed acyclic constraint. Considering that the number of items in recommender systems can be very large, we represent different items with a unified set of latent clusters, and the causal graph is defined on the cluster level, which enhances the model scalability and robustness. In addition, we provide theoretical analysis on the identifiability of the learned causal graph. To the best of our knowledge, this paper makes a first step towards combining sequential recommendation with causal discovery. For evaluating the recommendation performance, we implement our framework with different neural sequential architectures, and compare them with many state-of-the-art methods based on real-world datasets. Empirical studies manifest that our model can on average improve the performance by about 6.1% and 11.3% on F1and NDCG, respectively. To evaluate the model explainability, we build a new dataset with human labeled explanations for both quantitative and qualitative analysis.

Industrial recommender systems face the challenge of operating in non-stationary environments, where data distribution shifts arise from evolving user behaviors over time. To tackle this challenge, a common approach is to periodically re-train or incrementally update deployed deep models with newly observed data, resulting in a continual learning process. However, the conventional learning paradigm of neural networks relies on iterative gradient-based updates with a small learning rate, making it slow for large recommendation models to adapt. In this paper, we introduce ReLoop2, a self-correcting learning loop that facilitates fast model adaptation in online recommender systems through responsive error compensation. Inspired by the slow-fast complementary learning system observed in human brains, we propose an error memory module that directly stores error samples from incoming data streams. These stored samples are subsequently leveraged to compensate for model prediction errors during testing, particularly under distribution shifts. The error memory module is designed with fast access capabilities and undergoes continual refreshing with newly observed data samples during the model serving phase to support fast model adaptation. We evaluate the effectiveness of ReLoop2 on three open benchmark datasets as well as a real-world production dataset. The results demonstrate the potential of ReLoop2 in enhancing the responsiveness and adaptiveness of recommender systems operating in non-stationary environments.

The cost of hardware and energy consumption on training a click-through rate (CTR) model is highly prohibitive. A recent promising direction for reducing such costs is data distillation with gradient matching, which aims to synthesize a small distilled dataset to guide the model to a similar parameter space as those trained on real data. However, there are two main challenges to implementing such a method in the recommendation field: (1) The categorical recommended data are high dimensional and sparse one- or multi-hot data which will block the gradient flow, causing backpropagation-based data distillation invalid. (2) The data distillation process with gradient matching is computationally expensive due to the bi-level optimization. To this end, we investigate efficient data distillation tailored for recommendation data with plenty of side information where we formulate the discrete data to the dense and continuous data format. Then, we further introduce a one-step gradient matching scheme, which performs gradient matching for only a single step to overcome the inefficient training process. The overall proposed method is called Categorical data distillation with Gradient Matching (CGM), which is capable of distilling a large dataset into a small of informative synthetic data for training CTR models from scratch. Experimental results show that our proposed method not only outperforms the state-of-the-art coreset selection and data distillation methods but also has remarkable cross-architecture performance. Moreover, we explore the application of CGM on model retraining and mitigate the effect of different random seeds on the training results.

A common practice to keep the freshness of an offline Recommender System (RS) is to train models that fit the user’s most recent behaviour while directly replacing the outdated historical model. However, many feature engineering and computing resources are used to train these historical models, but they are underutilized in the downstream RS model training. In this paper, to turn these historical models into treasures, we introduce a model inversed data synthesis framework, which can recover training data information from the historical model and use it for knowledge transfer. This framework synthesizes a new form of data from the historical model. Specifically, we ’invert’ an off-the-shield pretrained model to synthesize binary class user-item pairs beginning from random noise without requiring any additional information from the training dataset. To synthesize informative data from a pretrained model, we propose a new continuous data type rather than the original one- or multi-hot vectors. An additional statistical regularization is added to further improve the quality of the synthetic data inverted from the deep model with batch normalization. The experimental results show that our framework can generalize across different types of models. We can efficiently train different types of classical Click-Through-Rate (CTR) prediction models from scratch with significantly few inversed synthetic data (2 orders of magnitude). Moreover, our framework can also work well in the knowledge transfer scenarios such as model retraining and data-free knowledge distillation.

The ultimate goal of recommender systems is satisfying users’ information needs in the long term. Despite the success of current recommendation techniques in targeting user interest, optimizing long-term user engagement and platform revenue is still challenging due to the restriction of optimization objectives such as clicks, ratings, and dwell time. Customer lifetime value (LTV) reflects the total monetary value of a customer to a business over the course of their relationship. Accurate LTV prediction can guide personalized service providers to optimize their marketing, sales, and service strategies to maximize customer retention, satisfaction, and profitability. However, the extreme sparsity, volatility, and randomness of consumption behaviors make LTV prediction rather intricate and challenging. In this tutorial, we give a detailed introduction to the key technologies and problems in LTV prediction. We present a systematic technique chronicle of LTV prediction over decades, including probabilistic models, traditional machine learning methods, and deep learning techniques. Based on this overview, we introduce several critical challenges in algorithm design, performance evaluation and system deployment from an industrial perspective, from which we derive potential directions for future exploration. From this tutorial, the RecSys community can gain a better understanding of the unique characteristics and challenges of LTV prediction, and it may serve as a catalyst to shift the focus of recommender systems from short-term targets to long-term ones.

In the video recommendation, watch time is commonly adopted as an indicator of user interest. However, watch time is not only influenced by the matching of users’ interests but also by other factors, such as duration bias and noisy watching. Duration bias refers to the tendency for users to spend more time on videos with longer durations, regardless of their actual interest level. Noisy watching, on the other hand, describes users taking time to determine whether they like a video or not, which can result in users spending time watching videos they do not like. Consequently, the existence of duration bias and noisy watching make watch time an inadequate label for indicating user interest. Furthermore, current methods primarily address duration bias and ignore the impact of noisy watching, which may limit their effectiveness in uncovering user interest from watch time. In this study, we first analyze the generation mechanism of users’ watch time from a unified causal viewpoint. Specifically, we considered the watch time as a mixture of the user’s actual interest level, the duration-biased watch time, and the noisy watch time. To mitigate both the duration bias and noisy watching, we propose Debiased and Denoised watch time Correction (D2Co), which can be divided into two steps: First, we employ a duration-wise Gaussian Mixture Model plus frequency-weighted moving average for estimating the bias and noise terms; then we utilize a sensitivity-controlled correction function to separate the user interest from the watch time, which is robust to the estimation error of bias and noise terms. The experiments on two public video recommendation datasets and online A/B testing indicate the effectiveness of the proposed method.

Two-tower models are a prevalent matching framework for recommendation, which have been widely deployed in industrial applications. The success of two-tower matching attributes to its efficiency in retrieval among a large number of items, since the item tower can be precomputed and used for fast Approximate Nearest Neighbor (ANN) search. However, it suffers two main challenges, including limited feature interaction capability and reduced accuracy in online serving. Existing approaches attempt to design novel late interactions instead of dot products, but they still fail to support complex feature interactions or lose retrieval efficiency. To address these challenges, we propose a new matching paradigm named SparCode, which supports not only sophisticated feature interactions but also efficient retrieval. Specifically, SparCode introduces an all-to-all interaction module to model fine-grained query-item interactions. Besides, we design a discrete code-based sparse inverted index jointly trained with the model to achieve effective and efficient model inference. Extensive experiments have been conducted on open benchmark datasets to demonstrate the superiority of our framework. The results show that SparCode significantly improves the accuracy of candidate item matching while retaining the same level of retrieval efficiency with two-tower models.

Legal case matching, which automatically constructs a model to estimate the similarities between the source and target cases, has played an essential role in intelligent legal systems. Semantic text matching models have been applied to the task where the source and target legal cases are considered as long-form text documents. These general-purpose matching models make the predictions solely based on the texts in the legal cases, overlooking the essential role of the law articles in legal case matching. In the real world, the matching results (e.g., relevance labels) are dramatically affected by the law articles because the contents and the judgments of a legal case are radically formed on the basis of law. From the causal sense, a matching decision is affected by the mediation effect from the cited law articles by the legal cases, and the direct effect of the key circumstances (e.g., detailed fact descriptions) in the legal cases. In light of the observation, this paper proposes a model-agnostic causal learning framework called Law-Match, under which the legal case matching models are learned by respecting the corresponding law articles. Given a pair of legal cases and the related law articles, Law-Match considers the embeddings of the law articles as instrumental variables(IVs), and the embeddings of legal cases as treatments. Using IV regression, the treatments can be decomposed into law-related and law-unrelated parts, respectively reflecting the mediation and direct effects. These two parts are then combined with different weights to collectively support the final matching prediction. We show that the framework is model-agnostic, and a number of legal case matching models can be applied as the underlying models. Comprehensive experiments show that Law-Match can outperform state-of-the-art baselines on three public datasets.

Multi-task Recommender Systems (MTRSs) has become increasingly prevalent in a variety of real-world applications due to their exceptional training efficiency and recommendation quality. However, conventional MTRSs often input all relevant feature fields without distinguishing their contributions to different tasks, which can lead to confusion and a decline in performance. Existing feature selection methods may neglect task relations or require significant computation during model training in multi-task setting. To this end, this paper proposes a novel Single-shot Feature Selection framework for MTRSs, referred to as MultiSFS, which is capable of selecting feature fields for each task while considering task relations in a single-shot manner. Specifically, MultiSFS first efficiently obtains task-specific feature importance through a single forward-backward pass. Then, a data-task bipartite graph is constructed to learn field-level task relations. Subsequently, MultiSFS merges the feature importance according to task relations and selects feature fields for different tasks. To demonstrate the effectiveness and properties of MultiSFS, we integrate it with representative MTRS models and evaluate on three real-world datasets. The implementation code is available online to ease reproducibility.

In the field of music information retrieval (MIR), cover song identification (CSI) is a challenging task that aims to identify cover versions of a query song from a massive collection. Existing works still suffer from high intra-song variances and inter-song correlations, due to the entangled nature of version-specific and version-invariant factors in their modeling. In this work, we set the goal of disentangling version-specific and version-invariant factors, which could make it easier for the model to learn invariant music representations for unseen query songs. We analyze the CSI task in a disentanglement view with the causal graph technique, and identify the intra-version and inter-version effects biasing the invariant learning. To block these effects, we propose the disentangled music representation learning framework (DisCover) for CSI. DisCover consists of two critical components: (1) Knowledge-guided Disentanglement Module (KDM) and (2) Gradient-based Adversarial Disentanglement Module (GADM), which block intra-version and inter-version biased effects, respectively. KDM minimizes the mutual information between the learned representations and version-variant factors that are identified with prior domain knowledge. GADM identifies version-variant factors by simulating the representation transitions between intra-song versions, and exploits adversarial distillation for effect blocking. Extensive comparisons with best-performing methods and in-depth analysis demonstrate the effectiveness of DisCover and the and necessity of disentanglement for CSI.

Recently, there has been growing interest in integrating causal inference into recommender systems to answer the hypothetical question: "what would be the potential feedback when a user is recommended a product?" Various unbiased estimators, including Inverse Propensity Score (IPS) and Doubly Robust (DR), have been proposed to address this question. However, these estimators often assume that confounders are precisely observable, which is not always the case in real-world scenarios. To address this challenge, we propose a novel method called Adversarial Training-based IPS (AT-IPS), which uses adversarial training to handle noisy confounders. The proposed method defines a feasible region for the confounders, obtains the worst-case noise (adversarial noise) within the region, and jointly trains the propensity model and the prediction model against such noise to improve their robustness. We provide a theoretical analysis of the accuracy-robustness tradeoff of AT-IPS and demonstrate its superior performance compared to other popular estimators on both real-world and semi-synthetic datasets.

Multi-layer perceptron (MLP) serves as a core component in many deep models for click-through rate (CTR) prediction. However, vanilla MLP networks are inefficient in learning multiplicative feature interactions, making feature interaction learning an essential topic for CTR prediction. Existing feature interaction networks are effective in complementing the learning of MLPs, but they often fall short of the performance of MLPs when applied alone. Thus, their integration with MLP networks is necessary to achieve improved performance. This situation motivates us to explore a better alternative to the MLP backbone that could potentially replace MLPs. Inspired by factorization machines, in this paper, we propose FINAL, a factorized interaction layer that extends the widely-used linear layer and is capable of learning 2nd-order feature interactions. Similar to MLPs, multiple FINAL layers can be stacked into a FINAL block, yielding feature interactions with an exponential degree growth. We unify feature interactions and MLPs into a single FINAL block and empirically show its effectiveness as a replacement for the MLP block. Furthermore, we explore the ensemble of two FINAL blocks as an enhanced two-stream CTR model, setting a new state-of-the-art on open benchmark datasets. FINAL can be easily adopted as a building block and has achieved business metric gains in multiple applications at Huawei. Our source code will be made available at MindSpore/models and FuxiCTR/model_zoo.

Alleviating the examination and trust bias in ranking systems is an important research line in unbiased learning-to-rank (ULTR). Current methods typically use the propensity to correct the biased user clicks and then learn ranking models based on the corrected clicks. Though successes have been achieved, directly modifying the clicks suffers from the inherent high variance because the propensities are usually involved in the denominators of corrected clicks. The problem gets even worse in the situation of mixed examination and trust bias. To address the issue, this paper proposes a novel ULTR method called Decomposed Ranking Debiasing (DRD). DRD is tailored for learning unbiased relevance models with low variance in the existence of examination and trust bias. Unlike existing methods that directly modify the original user clicks, DRD proposes to decompose each click prediction as the combination of a relevance term outputted by the ranking model and other bias terms. The unbiased relevance model, therefore, can be learned by fitting the overall click predictions to the biased user clicks. A joint learning algorithm is developed to learn the relevance and bias models' parameters alternatively. Theoretical analysis showed that, compared with existing methods, DRD has lower variance while retains unbiasedness. Empirical studies indicated that DRD can effectively reduce the variance and outperform the state-of-the-art ULTR baselines.

User-curated item lists, such as video-based playlists on Youtube and book-based lists on Goodreads, have become prevalent for content sharing on online platforms. Item list continuation is proposed to model the overall trend of a list and predict subsequent items. Recently, Transformer-based models have shown promise in comprehending contextual information and capturing item relationships in a list. However, deploying them in real-time industrial applications is challenging, mainly because the autoregressive generation mechanism used in them is time-consuming. In this paper, we propose a novel fast non-autoregressive sequence generation model, namely FANS, to enhance inference efficiency and quality for item list continuation. First, we use a non-autoregressive generation mechanism to decode next K items simultaneously instead of one by one in existing models. Then, we design a two-stage classifier to replace the vanilla classifier used in current transformer-based models to further reduce the decoding time. Moreover, to improve the quality of non-autoregressive generation, we employ a curriculum learning strategy to optimize training. Experimental results on four real-world item list continuation datasets including Zhihu, Spotify, AotM, and Goodreads show that our FANS model can significantly improve inference efficiency (up to 8.7x) while achieving competitive or better generation quality for item list continuation compared with the state-of-the-art autoregressive models. We also validate the efficiency of FANS in an industrial setting. Our source code and data will be available at MindSpore/models1 and Github2.

In this paper, we address the issue of recommending fairly from the aspect of providers, which has become increasingly essential in multistakeholder recommender systems. Existing studies on provider fairness usually focused on designing proportion fairness (PF) metrics that first consider systematic fairness. However, sociological researches show that to make the market more stable, max-min fairness (MMF) is a better metric. The main reason is that MMF aims to improve the utility of the worst ones preferentially, guiding the system to support the providers in weak market positions. When applying MMF to recommender systems, how to balance user preferences and provider fairness in an online recommendation scenario is still a challenging problem. In this paper, we proposed an online re-ranking model named Provider Max-min Fairness Re-ranking (P-MMF) to tackle the problem. Specifically, P-MMF formulates provider fair recommendation as a resource allocation problem, where the exposure slots are considered the resources to be allocated to providers and the max-min fairness is used as the regularizer during the process. We show that the problem can be further represented as a regularized online optimizing problem and solved efficiently in its dual space. During the online re-ranking phase, a momentum gradient descent method is designed to conduct the dynamic re-ranking. Theoretical analysis showed that the regret of P-MMF can be bounded. Experimental results on four public recommender datasets demonstrated that P-MMF can outperformed the state-of-the-art baselines. Experimental results also show that P-MMF can retain small computationally costs on a corpus with the large number of items.

Explainable recommendation has recently attracted increasing attention from both academic and industry communities. Among different explainable strategies, generating natural language explanations is an important method, which can deliver more informative, flexible and readable explanations to facilitate better user decisions. Despite the effectiveness, existing models are mostly optimized based on the observed datasets, which can be skewed due to the selection or exposure bias. To alleviate this problem, in this paper, we formulate the task of explainable recommendation with a causal graph, and design a causality enhanced framework to generate unbiased explanations. More specifically, we firstly define an ideal unbiased learning objective, and then derive a tractable loss for the observational data based on the inverse propensity score (IPS), where the key is a sample re-weighting strategy for equalizing the loss and ideal objective in expectation. Considering that the IPS estimated from the sparse and noisy recommendation datasets can be inaccurate, we introduce a fault tolerant mechanism by minimizing the maximum loss induced by the sample weights near the IPS. For more comprehensive modeling, we further analyze and infer the potential latent confounders induced by the complex and diverse user personalities. We conduct extensive experiments by comparing with the state-of-the-art methods based on three real-world datasets to demonstrate the effectiveness of our method.

The bias problems in recommender systems are an important challenge. In this paper, we focus on solving the bias problems via uniform data. Previous works have shown that simple modeling with a uniform data can alleviate the bias problems and improve the performance. However, the uniform data is usually few and expensive to collect in a real product. In order to use the valuable uniform data more effectively, we propose a novel and general knowledge distillation framework for counterfactual recommendation with four specific methods, including label-based distillation, feature-based distillation, sample-based distillation and model structure-based distillation. Moreover, we discuss the relation between the proposed framework and the previous works. We then conduct extensive experiments on both public and product datasets to verify the effectiveness of the proposed four methods. In addition, we explore and analyze the performance trends of the proposed methods on some key factors, and the changes in the distribution of the recommendation lists. Finally, we emphasize that counterfactual modeling with uniform data is a rich research area, and list some interesting and promising research topics worthy of further exploration. Note that the source codes are available athttps://github.com/dgliu/TKDE_KDCRec.

Debiased recommendation with a randomized dataset has shown very promising results in mitigating system-induced biases. However, it still lacks more theoretical insights or an ideal optimization objective function compared with the other more well-studied routes without a randomized dataset. To bridge this gap, we study the debiasing problem from a new perspective and propose to directly minimize the upper bound of an ideal objective function, which facilitates a better potential solution to system-induced biases. First, we formulate a new ideal optimization objective function with a randomized dataset. Second, according to the prior constraints that an adopted loss function may satisfy, we derive two different upper bounds of the objective function: a generalization error bound with triangle inequality and a generalization error bound with separability. Third, we show that most existing related methods can be regarded as the insufficient optimization of these two upper bounds. Fourth, we propose a novel method called debiasing approximate upper bound ( DUB ) with a randomized dataset, which achieves a more sufficient optimization of these upper bounds. Finally, we conduct extensive experiments on a public dataset and a real product dataset to verify the effectiveness of our DUB.

Debiased recommendation has recently attracted increasing attention from both industry and academic communities. Traditional models mostly rely on the inverse propensity score (IPS), which can be hard to estimate and may suffer from the high variance issue. To alleviate these problems, in this article, we propose a novel debiased recommendation framework based on user feature balancing. The general idea is to introduce a projection function to adjust user feature distributions, such that the ideal unbiased learning objective can be upper bounded by a solvable objective purely based on the offline dataset. In the upper bound, the projected user distributions are expected to be equal given different items. From the causal inference perspective, this requirement aims to remove the causal relation from the user to the item, which enables us to achieve unbiased recommendation, bypassing the computation of IPS. To efficiently balance the user distributions upon each item pair, we propose three strategies, including clipping, sampling, and adversarial learning to improve the training process. For more robust optimization, we deploy an explicit model to capture the potential latent confounders in recommendation systems. To the best of our knowledge, this article is the first work on debiased recommendation based on confounder balancing. In the experiments, we compare our framework with many state-of-the-art methods based on synthetic, semi-synthetic, and real-world datasets. Extensive experiments demonstrate that our model is effective in promoting the recommendation performance.

How to effectively mitigate the bias of feedback in recommender systems is an important research topic. In this article, we first describe the generation process of the biased and unbiased feedback in recommender systems via two respective causal diagrams, where the difference between them can be regarded as the source of system-induced biases. We then define this difference as a confounding bias and propose a new perspective on debiased representation learning to alleviate it. Specifically, for the case with biased feedback alone, we derive the conditions that need to be satisfied to obtain a debiased representation from the causal diagrams. Then, we propose a novel framework called debiased information bottleneck (DIB) to optimize these conditions and then find a tractable solution for it. The proposed framework constrains the model to learn a biased embedding vector with independent biased and unbiased components in the training phase, and uses only the unbiased component in the test phase to deliver more accurate recommendations. We further propose a variant of DIB by relaxing the independence between the biased and unbiased components. Finally, we conduct extensive experiments on a public dataset and a real product dataset to verify the effectiveness of the proposed framework.

Music recommendation is usually modeled as a Click-Through Rate (CTR) prediction problem, which estimates the probability of a user listening a recommended song. CTR prediction can be formulated as a binary classification problem where the played songs are labeled as positive samples and the skipped songs are labeled as negative samples. However, such naively defined labels are noisy and biased in practice, causing inaccurate model predictions. In this work, we first identify serious label noise issues in an industrial music App, and then propose an adaptive Label Correction method for Denoising (LCD) music recommendation by ensembling the noisy labels and the model outputs to encourage a consensus prediction. Extensive offline experiments are conducted to evaluate the effectiveness of LCD on both industrial and public datasets. Furthermore, in a one-week online AB test, LCD also significantly increases both the music play count and time per user by 1% to 5%.

Scoring a large number of candidates precisely in several milliseconds is vital for industrial pre-ranking systems. Existing pre-ranking systems primarily adopt the two-tower model since the "user-item decoupling architecture" paradigm is able to balance the efficiency and effectiveness. However, the cost of high efficiency is the neglect of the potential information interaction between user and item towers, hindering the prediction accuracy critically. In this paper, we show it is possible to design a two-tower model that emphasizes both information interactions and inference efficiency. The proposed model, IntTower (short for Interaction enhanced Two-Tower), consists of Light-SE, FE-Block and CIR modules. Specifically, lightweight Light-SE module is used to identify the importance of different features and obtain refined feature representations in each tower. FE-Block module performs fine-grained and early feature interactions to capture the interactive signals between user and item towers explicitly and CIR module leverages a contrastive interaction regularization to further enhance the interactions implicitly. Experimental results on three public datasets show that IntTower outperforms the SOTA pre-ranking models significantly and even achieves comparable performance in comparison with the ranking models. Moreover, we further verify the effectiveness of IntTower on a large-scale advertisement pre-ranking system. The code of IntTower is publicly available https://gitee.com/mindspore/models/tree/master/research/recommend/IntTower.

Sequential recommender systems train their models based on a large amount of implicit user feedback data and may be subject to biases when users are systematically under/over-exposed to certain items. Unbiased learning based on inverse propensity scores (IPS), which estimate the probability of observing a user-item pair given the historical information, has been proposed to address the issue. In these methods, propensity score estimation is usually limited to the view of item, that is, treating the feedback data as sequences of items that interacted with the users. However, the feedback data can also be treated from the view of user, as the sequences of users that interact with the items. Moreover, the two views can jointly enhance the propensity score estimation. Inspired by the observation, we propose to estimate the propensity scores from the views of user and item, called Dually Enhanced Propensity Score Estimation (DEPS). Specifically, given a target user-item pair and the corresponding item and user interaction sequences, DEPS first constructs a time-aware causal graph to represent the user-item observational probability. According to the graph, two complementary propensity scores are estimated from the views of item and user, respectively, based on the same set of user feedback data. Finally, two transformers are designed to make use of the two propensity scores and make the final preference prediction. Theoretical analysis showed the unbiasedness and variance of DEPS. Experimental results on three publicly available benchmarks and a proprietary industrial dataset demonstrated that DEPS can significantly outperform the state-of-the-art baselines.

Building appropriate scenarios to meet the personalized demands of different user groups is a common practice. Despite various scenario brings personalized service, it also leads to challenges for the recommendation on multiple scenarios, especially the scenarios with limited traffic. To give desirable recommendation service for all scenarios and reduce the cost of resource consumption, how to leverage the information from multiple scenarios to construct a unified model becomes critical. Unfortunately, the performance of existing multi-scenario recommendation approaches is poor since they introduce unnecessary information from other scenarios to target scenario. In this paper, we show it is possible to selectively utilize the information from different scenarios to construct the scenario-aware estimators in a unified model. Specifically, we first do analysis on multi-scenario modeling with causal graph from the perspective of users and modeling processes, and then propose the Causal Inspired Intervention (CausalInt) framework for multi-scenario recommendation. CausalInt consists of three modules: (1) Invariant Representation Modeling module to squeeze out the scenario-aware information through disentangled representation learning and obtain a scenario-invariant representation; (2) Negative Effects Mitigating module to resolve conflicts between different scenarios and conflicts between scenario-specific and scenario-invariant representations via gradient based orthogonal regularization and model-agnostic meta learning, respectively; (3) Inter-Scenario Transferring module designs a novel TransNet to simulate a counterfactual intervention and effectively fuse the information from other scenarios. Offline experiments over two real-world dataset and online A/B test are conducted to demonstrate the superiority of CausalInt.

Post-click conversion rate (CVR) prediction is an essential task for discovering user interests and increasing platform revenues in a range of industrial applications. One of the most challenging problems of this task is the existence of severe selection bias caused by the inherent self-selection behavior of users and the item selection process of systems. Currently, doubly robust (DR) learning approaches achieve the state-of-the-art performance for debiasing CVR prediction. However, in this paper, by theoretically analyzing the bias, variance and generalization bounds of DR methods, we find that existing DR approaches may have poor generalization caused by inaccurate estimation of propensity scores and imputation errors, which often occur in practice. Motivated by such analysis, we propose a generalized learning framework that not only unifies existing DR methods, but also provides a valuable opportunity to develop a series of new debiasing techniques to accommodate different application scenarios. Based on the framework, we propose two new DR methods, namely DR-BIAS and DR-MSE. DR-BIAS directly controls the bias of DR loss, while DR-MSE balances the bias and variance flexibly, which achieves better generalization performance. In addition, we propose a novel tri-level joint learning optimization method for DR-MSE in CVR prediction, and an efficient training algorithm correspondingly. We conduct extensive experiments on both real-world and semi-synthetic datasets, which validate the effectiveness of our proposed methods.

In streaming media applications, like music Apps, songs are recommended in a continuous way in users' daily life. The recommended songs are played automatically although users may not pay any attention to them, posing a challenge of user attention bias in training recommendation models, i.e., the training instances contain a large number of false-positive labels (users' feedback). Existing approaches either directly use the auto-feedbacks or heuristically delete the potential false-positive labels. Both of the approaches lead to biased results because the false-positive labels cause the shift of training data distribution, hurting the accuracy of the recommendation models. In this paper, we propose a learning-based counterfactual approach to adjusting the user auto-feedbacks and learning the recommendation models using Neural Dueling Bandit algorithm, called NDB. Specifically, NDB maintains two neural networks: a user attention network for computing the importance weights that are used for modifying the original rewards, and another random network trained with dueling bandit for conducting online recommendations based on the modified rewards. Theoretical analysis showed that the modified rewards are statistically unbiased, and the learned bandit policy enjoys a sub-linear regret bound. Experimental results demonstrated that NDB can significantly outperform the state-of-the-art baselines.

Deep learning-based recommendation has become a widely adopted technique in various online applications. Typically, a deployed model undergoes frequent re-training to capture users' dynamic behaviors from newly collected interaction logs. However, the current model training process only acquires users' feedbacks as labels, but fails to take into account the errors made in previous recommendations. Inspired by the intuition that humans usually reflect and learn from mistakes, in this paper, we attempt to build a self-correction continual learning loop (dubbed ReLoop) for recommender systems. In particular, a new customized loss is employed to encourage every new model version to reduce prediction errors over the previous model version during training. Our ReLoop learning framework enables a continual self-correction process in the long run and thus is expected to obtain better performance over existing training strategies. Both offline experiments and an online A/B test have been conducted to validate the effectiveness of ReLoop.

As an essential operation of legal retrieval, legal case matching plays a central role in intelligent legal systems. This task has a high demand on the explainability of matching results because of its critical impacts on downstream applications --- the matched legal cases may provide supportive evidence for the judgments of target cases and thus influence the fairness and justice of legal decisions. Focusing on this challenging task, we propose a novel and explainable method, namely IOT-Match, with the help of computational optimal transport, which formulates the legal case matching problem as an inverse optimal transport (IOT) problem. Different from most existing methods, which merely focus on the sentence-level semantic similarity between legal cases, our IOT-Match learns to extract rationales from paired legal cases based on both semantics and legal characteristics of their sentences. The extracted rationales are further applied to generate faithful explanations and conduct matching. Moreover, the proposed IOT-Match is robust to the alignment label insufficiency issue commonly in practical legal case matching tasks, which is suitable for both supervised and semi-supervised learning paradigms. To demonstrate the superiority of our IOT-Match method and construct a benchmark of explainable legal case matching task, we not only extend the well-known Challenge of AI in Law (CAIL) dataset but also build a new Explainable Legal cAse Matching (ELAM) dataset, which contains lots of legal cases with detailed and explainable annotations. Experiments on these two datasets show that our IOT-Match outperforms state-of-the-art methods consistently on matching prediction, rationale extraction, and explanation generation.

Collaborative filtering (CF) is a widely studied research topic in recommender systems. The learning of a CF model generally depends on three major components, namely interaction encoder, loss function, and negative sampling. While many existing studies focus on the design of more powerful interaction encoders, the impacts of loss functions and negative sampling ratios have not yet been well explored. In this work, we show that the choice of loss function as well as negative sampling ratio is equivalently important. More specifically, we propose the cosine contrastive loss (CCL) and further incorporate it to a simple unified CF model, dubbed SimpleX. Extensive experiments have been conducted on 10 benchmark datasets and compared with 28 existing CF models in total. Surprisingly, the results show that, under our CCL loss and a large negative sampling ratio, SimpleX can surpass most sophisticated state-of-the-art models by a large margin (e.g., max 48.5% improvement in [email protected] over LightGCN). We believe that SimpleX could not only serve as a simple strong baseline to foster future research on CF, but also shed light on the potential research direction towards improving loss function and negative sampling.

Top-N recommendation, which aims to learn user ranking-based preference, has long been a fundamental problem in a wide range of applications. Traditional models usually motivate themselves by designing complex or tailored architectures based on different assumptions. However, the training data of recommender system can be extremely sparse and imbalanced, which poses great challenges for boosting the recommendation performance. To alleviate this problem, in this paper, we propose to reformulate the recommendation task within the causal inference framework, which enables us to counterfactually simulate user ranking-based preferences to handle the data scarce problem. The core of our model lies in the counterfactual question: "what would be the user's decision if the recommended items had been different?''. To answer this question, we firstly formulate the recommendation process with a series of structural equation models (SEMs), whose parameters are optimized based on the observed data. Then, we actively indicate many recommendation lists (called intervention in the causal inference terminology) which are not recorded in the dataset, and simulate user feedback according to the learned SEMs for generating new training samples. Instead of randomly intervening on the recommendation list, we design a learning-based method to discover more informative training samples. Considering that the learned SEMs can be not perfect, we, at last, theoretically analyze the relation between the number of generated samples and the model prediction error, based on which a heuristic method is designed to control the negative effect brought by the prediction error. Extensive experiments are conducted based on both synthetic and real-world datasets to demonstrate the effectiveness of our framework.

Interactive recommendation has drawn widespread attention from both academia and industry due to its effectiveness in real-world mobile applications. Instead of receiving message passively, customers can exploit further with less effort through generated queries. Usually, such systems mainly contain two main components: query generation and item recommendation. In this paper, we propose a novel framework that models both queries and items in shared latent embedding space via a dual sequence transformer structure, which captures customer's potential interest from the prospect of reconciling the historical queries and corresponding customers interactions. We propose a click-through-rate model to generate query candidates, and a session search model for further more precise information. Comprehensive offline and online experiments are conducted, and the results demonstrate that our proposed dual-sequence-transformer based model can better utilize interaction and improve the accuracy of recommendations.

How to effectively mitigate the bias of feedback in recommender systems is an important research topic. In this paper, we first describe the generation process of the biased and unbiased feedback in recommender systems via two respective causal diagrams, where the difference between them can be regarded as the source of bias. We then define this difference as a confounding bias, which can be regarded as a collection of some specific biases that have previously been studied. For the case with biased feedback alone, we derive the conditions that need to be satisfied to obtain a debiased representation from the causal diagrams. Based on information theory, we propose a novel method called debiased information bottleneck (DIB) to optimize these conditions and then find a tractable solution for it. In particular, the proposed method constrains the model to learn a biased embedding vector with independent biased and unbiased components in the training phase, and uses only the unbiased component in the test phase to deliver more accurate recommendations. Finally, we conduct extensive experiments on a public dataset and a real product dataset to verify the effectiveness of the proposed method and discuss its properties.

Click-through rate (CTR) prediction is an important topic in mobile recommendation systems and computational advertising. As previous research indicates, a key point to maximize CTR is feature conjunction for making the training data more informative. Despite great progress, existing methods still fail to choose suitable settings of feature conjunction for the given data. In particular, a linear model on the pair-wise feature conjunction may overfit the training set if the data set is highly sparse. For such data, a model based on low-rank latent matrices are shown to be more appropriate. Unfortunately, practitioners now face difficulties to decide when to use which. In this paper, we propose an adaptive framework to address the feature conjunction problem. Our proposed framework adaptively chooses effective models to do feature conjunction according to data properties. We offer a case for building feature conjunction based on feature-pair frequency. Efficient training, as well as parameter selection, are thoroughly investigated. We conduct comprehensive online and offline experiments to demonstrate the effectiveness of the adaptive model over existing models for CTR prediction.

Most commercial industrial recommender systems have built their closed feedback loops. Though it is helpful in item recommendation and model training, the closed feedback loop may lead to the so-called bias problems, including the position bias, selection bias and popularity bias. The recommendation models trained with biased may hurt the user experiences by recommending homogenous items. How to control the biases in the closed feedback loop has become one of major challenges in modern recommender systems. This talk discusses the counterfactual learning technologies for tackling the bias problem in recommendation.

Click-through rate (CTR) prediction is a core problem of building advertising systems. In many real-world applications, because an ad placed in various positions has different click probabilities, the position information should be considered in both training and prediction. For such position-aware systems, existing approaches learn CTR models from clicks/not-clicks on historically displayed events by leveraging the position information in different ways. In this work, we explain that these approaches may give a heavily biased model. We first point out that in position-aware systems, two different types of selection biases coexist in displayed events. Secondly, we explain that some approaches attempting to eliminate the position effect from clicks/not-clicks may possess an additional bias. Finally, to obtain an unbiased CTR model for position-aware systems, we propose a novel counterfactual learning framework. Experiments confirm both our analysis on selection biases and the effectiveness of our proposed counterfactual learning framework.

Recommender systems are feedback loop systems, which often face bias problems such as popularity bias, previous model bias and position bias. In this paper, we focus on solving the bias problems in a recommender system via a uniform data. Through empirical studies in online and offline settings, we observe that simple modeling with a uniform data can alleviate the bias problems and improve the performance. However, the uniform data is always few and expensive to collect in a real product. In order to use the valuable uniform data more effectively, we propose a general knowledge distillation framework for counterfactual recommendation that enables uniform data modeling through four approaches: (1) label-based distillation focuses on using the imputed labels as a carrier to provide useful de-biasing guidance; (2) feature-based distillation aims to filter out the representative causal and stable features; (3) sample-based distillation considers mutual learning and alignment of the information of the uniform and non-uniform data; and (4) model structure-based distillation constrains the training of the models from the perspective of embedded representation. We conduct extensive experiments on both public and product datasets, demonstrating that the proposed four methods achieve better performance over the baseline models in terms of AUC and NLL. Moreover, we discuss the relation between the proposed methods and the previous works. We emphasize that counterfactual modeling with uniform data is a rich research area, and list some interesting and promising research topics worthy of further exploration. Note that the source codes are available at \urlhttps://github.com/dgliu/SIGIR20_KDCRec.

Recommender system is one of the most successful machine learning technologies for commerce. However, it can reinforce the closed feedback loop problem, where the recommender system generates items to users, then the further recommendation model is trained with the data that users' feedback to the items. Such self-reinforcing pattern can cause data bias problems. There are several debiasing methods, inverse-propensity-scoring (IPS) is a practical one for industry product. Since it is relatively easy to reweight training samples, and ameliorate the distribution shift problem. However,because of deterministic policy problem and confoundings in real-world data, it is hard to predict propensity score accurately. Inspired by the sample reweight work for robust deep learning, we propose a novel influence function based method for recommendation modeling, and analyze how the influence function corrects the bias. In the experiments, our proposed method achieves better performance against the state-of-the-art approaches.

Recommender systems often face heterogeneous datasets containing highly personalized historical data of users, where no single model could give the best recommendation for every user. We observe this ubiquitous phenomenon on both public and private datasets and address the model selection problem in pursuit of optimizing the quality of recommendation for each user. We propose a meta-learning framework to facilitate user-level adaptive model selection in recommender systems. In this framework, a collection of recommenders is trained with data from all users, on top of which a model selector is trained via meta-learning to select the best single model for each user with the user-specific historical data. We conduct extensive experiments on two public datasets and a real-world production dataset, demonstrating that our proposed framework achieves improvements over single model baselines and sample-level model selector in terms of AUC and LogLoss. In particular, the improvements may lead to huge profit gain when deployed in online recommender systems.

Download fraud is a prevalent threat in mobile App markets, where fraudsters manipulate the number of downloads of Apps via various cheating approaches. Purchased fake downloads can mislead recommendation and search algorithms and further lead to bad user experience in App markets. In this paper, we investigate download fraud problem based on a company's App Market, which is one of the most popular Android App markets. We release a honeypot App on the App Market and purchase fake downloads from fraudster agents to track fraud activities in the wild. Based on our interaction with the fraudsters, we categorize download fraud activities into three types according to their intentions: boosting front end downloads, optimizing App search ranking, and enhancing user acquisition&retention rate. For the download fraud aimed at optimizing App search ranking, we select, evaluate, and validate several features in identifying fake downloads based on billions of download data. To get a comprehensive understanding of download fraud, we further gather stances of App marketers, fraudster agencies, and market operators on download fraud. The followed analysis and suggestions shed light on the ways to mitigate download fraud in App markets and other social platforms. To the best of our knowledge, this is the first work that investigates the download fraud problem in mobile App markets.

Click-through rate (CTR) prediction is the core problem of building advertising systems. Most existing state-of-the-art approaches model CTR prediction as binary classification problems, where displayed events with and without click feedbacks are respectively considered as positive and negative instances for training and offline validation. However, due to the selection mechanism applied in most advertising systems, a selection bias exists between distributions of displayed and non-displayed events. Conventional CTR models ignoring the bias may have inaccurate predictions and cause a loss of the revenue. To alleviate the bias, we need to conduct counterfactual learning by considering not only displayed events but also non-displayed events. In this paper, through a review of existing approaches of counterfactual learning, we point out some difficulties for applying these approaches for CTR prediction in a real-world advertising system. To overcome these difficulties, we propose a novel framework for counterfactual CTR prediction. In experiments, we compare our proposed framework against state-of-the-art conventional CTR models and existing counterfactual learning approaches. Experimental results show significant improvements.

The location selection (LS) problem aims to mine the optimal location to place a new facility from a set of candidates such that the benefit or influence on a given set of objects is maximized. State-of-the-art LS techniques assume each object is static and can only be influenced by a single facility. However, in reality, objects (e.g., people, vehicles) are mobile and are influenced by multiple facilities. Consequently, classical LS solutions fail to select locations accurately. In this work, we introduce a generalized LS problem called PRIME-LS which takes mobility and probability factors into consideration to address the aforementioned limitations. To solve the problem, we propose an algorithm called PINOCCHIO, which leverages two pruning rules based on a novel distance measure, and further extend it by incorporating two optimization strategies. Experimental study over two real-world datasets demonstrates superiority of our framework in comparison to state-of-the-art LS techniques.

The location selection (ls) problem, which aims to mine the optimal location from a set of candidates to place a new facility such that a score (i.e., benefit or influence on some given objects) can be maximized, has drawn significant research attention in recent years. State-of-the-art ls techniques assume each object is static and can only be influenced by a single facility. However, in reality, objects (e.g., people, vehicles) are mobile and are influenced by multiple facilities, which prevents classical ls solutions from selecting accurate results. In this paper, we introduce a generalizedls problem called Prime-ls which takes mobility and probability factors into consideration to address the aforementioned limitations. Specifically, given a set of candidate locations, Prime-ls aims to mine the optimal location which can influence the most number of moving objects. Also, to address the problem we propose an efficient algorithm called Pinocchio that leverages two pruning rules based on a novel distance measure. These rules enable us to prune many inferior candidate locations prior to influence computation, paving the way to efficient and accurate solution. Furthermore, we extend Pinocchio (Pinocchio-vo) by incorporating two optimization strategies during candidate validation phase, which further reduce unnecessary computations. Experimental study over two real-world datasets demonstrates superiority of our framework in comparison to state-of-the-art ls techniques.