Cross-domain Recommendation (CDR) aims to alleviate the data sparsity and the cold-start problems in traditional recommender systems by leveraging knowledge from an informative source domain. However, previously proposed CDR models pursue an imprudent assumption that the entire information from the source domain is equally contributed to the target domain, neglecting the evil part that is completely irrelevant to users' intrinsic interest. To address this concern, in this paper, we propose a novel knowledge enhanced cross-domain recommendation framework named CoTrans, which remolds the core procedures of CDR models with: Compression on the knowledge from the source domain and Transfer of the purity to the target domain. Specifically, following the theory of Graph Information Bottleneck, CoTrans first compresses the source behaviors with the perception of information from the target domain. Then to preserve all the important information for the CDR task, the feedback signals from both domains are utilized to promote the effectiveness of the transfer procedure. Additionally, a knowledge-enhanced encoder is employed to narrow gaps caused by the non-overlapped items across separate domains. Comprehensive experiments on three widely used cross-domain datasets demonstrate that CoTrans significantly outperforms both single-domain and state-of-the-art cross-domain recommendation approaches.

In recent years, there has been a trend in the field of recommender systems towards multi-task modeling and multi-scenario modeling. The aim is to enhance the performance of various tasks and scenarios by jointly training on multiple tasks or scenarios to learn common patterns and features. Joint modeling of tasks and scenarios has also received widespread attention recently. However, despite the rich proposals of methods for Multi-Task Learning (MTL), Multi-Scenario Learning (MSL), and Multi-Task-Multi-Scenario Learning (MTMSL) in recent years, there still lacks a comprehensive benchmark to evaluate these methods. Previous studies often employed different datasets, data processing techniques, data partitioning strategies, and hyperparameter settings, making replication of existing research and fair comparison of experimental results challenging. To address this challenge, we introduce MMLRec, the first unified comprehensive benchmark for evaluating MTL, MSL and MTMSL, featuring consistent dataset processing and identical parameter settings. This benchmark implements a range of MTL, MSL, and MTMSL algorithms, and evaluates them on multiple commonly used recommender systems datasets. Through fair comparative experiments, we find that some structurally simplistic recommendation algorithms are underestimated, as they can achieve comparable results to more complex algorithms while maintaining lower complexity. Furthermore, our experimental analysis indicates that more complex methods exhibit better robustness when there are significant differences between tasks or scenarios. By providing a unified framework (MMLRec), our goal is to promote rapid evaluation and inspire innovative research in this continuously evolving field. We hope that our open-source benchmark can facilitate swift, equitable evaluations, while also fostering further breakthrough research in the domains of MTL, MSL, and MTMSL.

With the rise of large language models (LLMs), recent works have leveraged LLMs to improve the performance of click-through rate (CTR) prediction. However, we argue that a critical obstacle remains in deploying LLMs for practical use: the efficiency of LLMs when processing long textual user behaviors. As user sequences grow longer, the current efficiency of LLMs is inadequate for training on billions of users and items. To break through the efficiency barrier of LLMs, we propose Behavior Aggregated Hierarchical Encoding (BAHE) to enhance the efficiency of LLM-based CTR modeling. Specifically, BAHE proposes a novel hierarchical architecture that decouples the encoding of user behaviors from inter-behavior interactions. Firstly, to prevent computational redundancy from repeated encoding of identical user behaviors, BAHE employs the LLM's pre-trained shallow layers to extract embeddings of the most granular, atomic user behaviors from extensive user sequences and stores them in the offline database. Subsequently, the deeper, trainable layers of the LLM facilitate intricate inter-behavior interactions, thereby generating comprehensive user embeddings. This separation allows the learning of high-level user representations to be independent of low-level behavior encoding, significantly reducing computational complexity. Finally, these refined user embeddings, in conjunction with correspondingly processed item embeddings, are incorporated into the CTR model to compute the CTR scores. Extensive experimental results show that BAHE reduces training time and memory by five times for CTR models using LLMs, especially with longer user sequences. BAHE has been deployed in a real-world system, allowing for daily updates of 50 million CTR data on 8 A100 GPUs, making LLMs practical for industrial CTR prediction.

Click-through rate (CTR) prediction plays a crucial role in recommendation systems, with significant impact on user experience and platform revenue generation. Despite the various public CTR datasets available due to increasing interest from both academia and industry, these datasets have limitations. They cover a limited range of scenarios and predominantly focus on ID-based features, neglecting the vital role of multi-modal features for effective multi-scenario CTR prediction. Moreover, their scale is modest compared to real-world industrial datasets, hindering robust and comprehensive evaluation of complex models. To address these challenges, we introduce a large-scale Multi-Scenario Multi-Modal CTR dataset named AntM2 C, built from real industrial data from Alipay. This dataset offers an impressive breadth and depth of information, covering CTR data from four diverse business scenarios, including advertisements, consumer coupons, mini-programs, and videos. Unlike existing datasets, AntM2 C provides not only ID-based features but also five textual features and one image feature for both users and items, supporting more delicate multi-modal CTR prediction. AntM2 C is also substantially larger than existing datasets, comprising 100 million CTR data. This scale allows for robust and comprehensive evaluation and comparison of CTR prediction models. We employ AntM2 C to construct several typical CTR tasks, including multi-scenario modeling, item and user cold-start modeling, and multi-modal modeling. Initial experiments and comparisons with baseline methods have shown that AntM2 C presents both new challenges and opportunities for CTR models, with the potential to significantly advance CTR research. The AntM2 C dataset is available at https://www.atecup.cn/OfficalDataSet.

Conversion rate (CVR) prediction models play a vital role in recommendation systems. Recent research shows that learning a unified model to serve multiple scenarios is effective for improving overall performance. However, it remains challenging to improve model prediction performance across scenarios at low model parameter cost, and current solutions are hard to robustly model multi-scenario diversity. In this paper, we propose MI-DPG for the multi-scenario CVR prediction, which learns scenario-conditioned dynamic model parameters for each scenario in a more efficient and effective manner. Specifically, we introduce an auxiliary network to generate scenario-conditioned dynamic weighting matrices, which are obtained by combining decomposed scenario-specific and scenario-shared low-rank matrices with parameter efficiency. For each scenario, weighting the backbone model parameters by the weighting matrix helps to specialize the model parameters for different scenarios. It can not only modulate the complete parameter space of the backbone model but also improve the model effectiveness. Furthermore, we design a mutual information regularization to enhance the diversity of model parameters across scenarios by maximizing the mutual information between the scenario-aware input and the scenario-conditioned dynamic weighting matrix. Experiments from three real-world datasets show that MI-DPG outperforms previous multi-scenario recommendation models.

In real-world industrial advertising systems, user behavior sparsity is a key issue that affects online recommendation performance. We observe that users with rich behaviors can obtain better recommendation results than those with sparse behaviors in a conversion-rate (CVR) prediction model. Inspired by this phenomenon, we propose a new method SeqGen, in an effort to exploit user side information to bridge the gap between rich and sparse behaviors. SeqGen is a learnable and pluggable module, which can be easily integrated into any CVR model and no longer requires two-stage training as in previous works. In particular, SeqGen learns a mapping relationship between the user side information and behavior sequences, only on the basis of the users with long behavior sequences. After that, SeqGen can generate rich sequence features for users with sparse behaviors based on their side information, so as to alleviate the issue of user behavior sparsity. The generated sequence features will then be fed into the classifier tower of an arbitrary CVR model together with the original sequence features. To the best of our knowledge, our approach constitutes the first attempt to exploit user side information for addressing the user behavior sparsity issue. We validate the effectiveness of SeqGen on the publicly available dataset MovieLens-1M, and our method receives an improvement of up to 0.5% in terms of the AUC score. More importantly, we successfully deploy SeqGen in the commercial advertising system Xlight of Alipay, which improves the grouped AUC of the CVR model by 0.6% and brings a boost of 0.49% in terms of the conversion rate on A/B testing.

Numerical features are an important type of input for CTR prediction models. Recently, several discretization and numerical transformation methods have been proposed to deal with numerical features. However, existing approaches do not fully consider compatibility with different distributions. Here, we propose a novel numerical feature embedding framework, called Distribution-Aware Embedding (DAE), which is applicable to various numerical feature distributions. First, DAE efficiently approximates the cumulative distribution function by estimating the expectation of the order statistics. Then, the distribution information is applied to the embedding layer by nonlinear interpolation. Finally, to capture both local and global information, we aggregate the embeddings at multiple scales to obtain the final representation. Empirical results validate the effectiveness of DAE compared to the baselines, while demonstrating the adaptability to different CTR models and distributions.

Industrial recommender systems usually need to serve multiple scenarios at the same time. In practice, there are various heterogeneous scenarios, since users frequently engage in scenarios with varying intentions and the items within each scenario typically belong to diverse categories. Existing works of multi-scenario recommendation mainly focus on modeling homogeneous scenarios which have similar data distributions. They equally transfer knowledge to each scenario without considering the diversity of heterogeneous scenarios. In this paper, we argue that the heterogeneity in multi-scenario recommendations is a key problem that needs to be solved. To this end, we propose an industrial framework named Scenario-Aware Model-Agnostic Meta Distillation (SAMD) for the multi-scenario recommendation. SAMD aims to provide scenario-aware and model-agnostic knowledge sharing across heterogeneous scenarios by modeling scenarios' relationship and conducting heterogeneous knowledge distillation. Specifically, SAMD first measures the comprehensive representation of each scenario and then proposes a novel meta distillation paradigm to conduct scenario-aware knowledge sharing. The meta network first establishes the potential scenarios' relationships and generates the strategies of knowledge sharing for each scenario. Then the heterogeneous knowledge distillation utilizes scenario-aware strategies to share knowledge across heterogeneous scenarios through intermediate features distillation without the restriction of the model architecture. In this way, SAMD shares knowledge across heterogeneous scenarios in a scenario-aware and model-agnostic manner, which addresses the problem of heterogeneity. Compared with other state-of-the-art methods, extensive offline experiments, and online A/B testing demonstrate the superior performance of the proposed SAMD framework, especially in heterogeneous scenarios.

In industrial recommendation systems, both data sizes and computational resources vary across different scenarios. For scenarios with limited data, data sparsity can lead to a decrease in model performance. Heterogeneous knowledge distillation-based transfer learning can be used to transfer knowledge from models in data-rich domains. However, in recommendation systems, the target domain possesses specific privileged features that significantly contribute to the model. While existing knowledge distillation methods have not taken these features into consideration, leading to suboptimal transfer weights. To overcome this limitation, we propose a novel algorithm called Uncertainty-based Heterogeneous Privileged Knowledge Distillation (UHPKD). Our method aims to quantify the knowledge of both the source and target domains, which represents the uncertainty of the models. This approach allows us to derive transfer weights based on the knowledge gain, which captures the difference in knowledge between the source and target domains. Experiments conducted on both public and industrial datasets demonstrate the superiority of our UHPKD algorithm compared to other state-of-the-art methods.

The cold-start problem of conversion rate prediction is a common challenge in online advertising systems. To alleviate this problem, a large number of methods either use content information or uncertainty methods, or use meta-learning based methods to improve the ranking performance of cold-start items. However, they can work for cold-start scenarios but fail to adaptively unify warm and cold recommendations into one model, requiring additional human efforts or knowledge to adapt to different scenarios. Meanwhile, none of them pay attention to the discrepancy between model predictions and true likelihoods of cold items, while over- or under-estimation is harmful to the ROI (Return on Investment) of advertising placements. In this paper, in order to address the above issues, we propose a framework called Distribution-Constrained Batch Transformer (DCBT). Specifically, the framework introduces a Transformer module into the batch dimension to automatically choose proper information from warm samples to enhance the representation of cold samples and preserve the property of warm samples. In addition, to avoid the distribution of cold samples being affected by the warm samples, the framework adds MMD loss to constrain the sample distribution before and after feeding into the Transformer module. Extensive offline experiments on two real-world datasets show that our proposed method attains state-of-the-art performance in AUC and PCOC (Predicted CVR over CVR) for cold items and warm items. An online A/B test demonstrates that the DCBT model obtained a 20.08% improvement in CVR and a 13.21% increase in GMV (Gross Merchandise Volume).

Multi-task learning (MTL) has been successfully applied to a wide range of real-world applications. However, MTL models often suffer from performance degradation with negative transfer due to sharing all features without distinguishing their helpfulness for all tasks. To this end, many works on feature selection for multi-task learning (FS-MTL) have been proposed to alleviate negative transfer between tasks by learning features selectively for each specific task. However, due to latent confounders between features and task targets, the correlations captured by the feature selection modules proposed in these works may fail to reflect the actual effect of the features on the targets. This paper explains negative transfer in FS-MTL from a causal perspective and presents a novel architecture called Causal Feature Selection for Multi-task Learning(CFS-MTL). This method incorporates the idea of causal inference into feature selection for multi-task learning via pseudo-intervention. It aims to select features with more stable causal effects rather than spurious correlations for each task by regularizing the distance between feature ITEs and feature importance. We conduct extensive experiments based on three real-world datasets to demonstrate that our proposed CFS-MTL outperforms state-of-the-art MTL models significantly in the AUC metric.

Conversion Rate (CVR) prediction is a crucial task in online advertising systems. Existing single-domain CVR prediction models suffer from the data sparsity problem since few users purchase items after clicking. In recent years, a robust and effective technique called fine-tuning can transfer knowledge from a data-rich source domain to enhance the CVR prediction performance in a data-sparse target domain. However, since most CVR prediction models have a large number of parameters, fine-tuning all the parameters on a data-sparse domain may lead to over-fitting. In this paper, we propose a general and efficient transfer learning method called Gradient-Flow based Fine-Tuning (GFlow-FT), which only needs to update a subset of parameters (called child network) via pruning the gradients to restrain gradient norm against over-fitting. In addition, our method employs the gradient-flow based measure via calculating the Hessian-gradient product as the criteria for picking the child network, which is superior to the magnitude-based and loss-based measure from empirical results. Extensive experimental results on three real-world datasets from recommendation systems show that GFlow-FT can significantly improve the performance of CVR prediction compared with state-of-the-art fine-tuning approaches.

Multi-task learning (MTL) aims at solving multiple related tasks simultaneously and has experienced rapid growth in recent years. However, MTL models often suffer from performance degeneration with negative transfer due to learning several tasks simultaneously. Some related work attributed the source of the problem is the conflicting gradients. In this case, it is needed to select useful gradient updates for all tasks carefully. To this end, we propose a novel optimization approach for MTL, named GDOD, which manipulates gradients of each task using an orthogonal basis decomposed from the span of all task gradients. GDOD decomposes gradients into task-shared and task-conflict components explicitly and adopts a general update rule for avoiding interference across all task gradients. This allows guiding the update directions depending on the task-shared components. Moreover, we prove the convergence of GDOD theoretically under both convex and non-convex assumptions. Experiment results on several multi-task datasets not only demonstrate the significant improvement of GDOD performed to existing MTL models but also prove that our algorithm outperforms state-of-the-art optimization methods in terms of AUC and Logloss metrics.

Multi-task learning (MTL) has experienced rapid growth in recent years. A typical way of conducting MTL with deep neural networks (DNNs) is either establishing a sort of global feature sharing mechanism across all tasks or assigning each task an individual set of parameters with cross-connections. However, these existing approaches leverage DNNs only to share features of a certain order. Several modelsdemonstrated that explicitly modeling feature sharing with both low-order and high-order features can boost performance. To this end, we propose a model-agnostic sparse routing architecture called MASR, which emphasizes arbitrary order feature sharing for multi-task learning. It is able to choose specific orders of features to route for a given task through learnable latent variables. Moreover, MASR is model-agnostic and can be combined with existing MTL models to share features of both low-order and high-order. Extensive experimental results on several real-world datasets not only confirm the significant improvement of MASR performed to existing MTL models but also outperform existing hybrid architectures in terms of AUC metric.

In recommender systems, content-based methods and meta-learning involved methods usually have been adopted to alleviate the item cold-start problem. The former consider utilizing item attributes at the feature level and the latter aim at learning a globally shared initialization for all tasks to achieve fast adaptation with limited data at the task level. However, content-based methods only focus on the similarity of item attributes, ignoring the relationships established by user interactions. And for tasks with different distributions, most meta-learning-based methods are difficult to achieve better performance under a single initialization. To address the limitations mentioned above and combine the strengths of both methods, we propose a Task Similarity Aware Meta-Learning (TSAML) framework from two aspects. Specifically, at the feature level, we simultaneously introduce content information and user-item relationships to exploit task similarity. At the task level, we design an automatic soft clustering module to cluster similar tasks and generate the same initialization for similar tasks. Extensive offline experiments demonstrate that the TSAML framework has superior performance and recommends cold items to preferred users more effectively than other state-of-the-art methods.

There exists the cold-start problem in the recommendation systems when observed user-item interactions are insufficient. To alleviate this problem, most existing works aim to learn globally shared prior knowledge across all items and be fast adapted to a new item with few interactions. However, such learning techniques are data demanding and work poorly on new items with no interactions. In this applied paper, we present an industrial framework recently deployed on Alipay to address the item cold-start problem in zero-shot scenarios. The proposed framework provides both efficient and high-quality recommendations for cold items with no log data. Specifically, we formulate the cold-start problem as a zero-shot learning problem and build a highly efficient infrastructure to accomplish online zero-shot recommendations used on large-scale platforms. Extensive offline experiments and online A/B testing demonstrate that the proposed framework has superior performance and recommends cold items to preferred users more effectively than other state-of-the-art methods.

Multi-task learning(MTL) is an open and challenging problem in various real-world applications. The typical way of conducting multi-task learning is establishing some global parameter sharing mechanism across all tasks or assigning each task an individual set of parameters with cross-connections between tasks. However, for most existing approaches, all tasks just thoroughly or proportionally share all the features without distinguishing the helpfulness of them. By that, some tasks would be intervened by the unhelpful features that are useful for other tasks, leading to undesired negative transfer between tasks. In this paper, we design a novel architecture named the Multiple-level Sparse Sharing Model (MSSM), which can learn features selectively and share knowledge across all tasks efficiently. MSSM first employs a field-level sparse connection module (FSCM) to enable much more expressive combinations of feature fields to be learned for generalization across tasks while still allowing for task-specific features to be customized for each task. Furthermore, a cell-level sparse sharing module (CSSM) can recognize the sharing pattern through a set of coding variables that selectively choose which cells to route for a given task. Extensive experimental results on several real-world datasets show that MSSM outperforms SOTA models significantly in terms of AUC and LogLoss metrics.

Transfer learning leverages knowledge from a source domain with rich data to a target domain with sparse data. However, the difference between the source and target data distribution weakens the transferability. To bridge this gap, we focus on selecting source instances that are closely related to and have the same distribution as the target domain. In this paper, we propose a novel Adaptive Clustering Transfer Learning (ACTL) method to improve transferability. Specifically, we simultaneously train the instance selector and the transfer learning model. The selector adaptively conducts clustering on the training data and learns the weights for source instances. The weight will activate or inhibit the contribution of the corresponding source instance during transfer learning. Meanwhile, the transfer learning model guides the selector to learn the weight appropriately according to the objective function. To evaluate the effectiveness of our method, we conduct experiments on two different tasks including recommender system and text matching. Experimental results show that our method consistently outperforms competing methods and the selected source instances share a similar data distribution with the target domain.