The blossoming of large language models (LLMs) has greatly shifted the paradigm of Sequential Recommender System (SRS). Numerous studies have attempted to integrate ID-based collaborative signals and text information for effectively capturing both ID semantics and text semantics to enhance LLM-based recommendation. However, existing fusion methods suffer from challenges like fusion noise and the semantic gap. To address these issues, we propose Hybrid Dual-Semantics Modeling for enhancing LLM-based Recommendation (HDRec), an effective hybrid fusion method based on a design of dual low-rank adaptation (LoRA). HDRec employs two LoRAs processes on a shared LLM decoder, with each process handling information from one of the two semantics. We further implement a dedicated fusion mechanism exclusively at the inference stage, allowing the robust textual representation to serve as the primary signal, which is adaptively enhanced by unique collaborative signals from ID semantics, ensuring stable and accurate final predictions. To mitigate gradient conflicts caused by the dual LoRA processes, we introduce the alternating training of dual low-rank adaptation strategy. This method effectively resolves gradient conflicts and enables successful optimization of HDRec. Extensive experiments show that HDRec outperforms existing non-LLM-based and LLM-based state-of-the-art methods. The implementation of HDRec is anonymously available at https://github.com/KDEGroup/HDRec.

Large language models (LLMs) are increasingly adopted as the backbone of recommender systems. However, user–item interactions in real-world scenarios are non-stationary, making preference drift over time inevitable. Existing model update strategies mainly rely on global fine-tuning or pointwise editing, but they face two fundamental challenges: (i) imbalanced update granularity, where global updates perturb behaviors unrelated to the target while pointwise edits fail to capture broader preference shifts; (ii) unstable incremental updates, where repeated edits interfere with prior adaptations, leading to catastrophic forgetting and inconsistent recommendations. To address these issues, we propose Region-Aware Incremental Editing (RAIE), a plug-in framework that freezes the backbone model and performs region-level updates. RAIE first constructs semantically coherent preference regions via spherical k-means in the representation space. It then assigns incoming sequences to regions via confidence-aware gating and performs three localized edit operations-Update, Expand, and Add-to dynamically revise the affected region. Each region is equipped with a dedicated Low-Rank Adaptation (LoRA) module, which is trained only on the region's updated data. During inference, RAIE routes each user sequence to its corresponding region and activates the region-specific adapter for prediction. Experiments on two benchmark datasets under a time-sliced protocol that segments data into Set-up (S), Finetune (F), and Test (T) show that RAIE significantly outperforms state-of-the-art baselines while effectively mitigating forgetting. These results demonstrate that region-aware editing offers an accurate and scalable mechanism for continual adaptation in dynamic recommendation scenarios.

Visually-aware recommender system (VARS) has become increasingly prevalent in various online services by integrating visual features of items to enhance recommendation quality. However, VARS introduces new security vulnerabilities and malicious attackers can perform visual shilling attacks to manipulate recommendation lists via uploading generated images with visually imperceptible perturbations. While prior research has explored such threats to help service providers enhance their systems, existing visual shilling attack methods still suffer from uncontrolled pixel-space perturbation, energy dispersion dilemma and semantic misalignment in reference selection. In this work, we present Semantic-Constrained Diffusion Adversarial Generation (SC-DAG) for visual shilling attacks. SC-DAG overcomes key limitations of previous methods by focusing perturbations on semantically meaningful image regions through contour-aware segmentation, guiding adversarial generation in latent space using a conditional diffusion process, and performing a hybrid reference image selection strategy that balances popularity and semantic similarity. Extensive experiments on performing visual shilling attacks against multiple VARS models show that SC-DAG achieves state-of-the-art attack performance in elevating target items' ranking, while maintaining strong perceptual indistinguishability and minimal impact on overall recommendation performance of the system. Our work offers insights into leveraging structured semantic priors for more sophisticated adversarial manipulations against VARS and also highlights the necessity for developing more robust VARS models resilient to visual shilling attacks. We provide our implementation at https://github.com/KDEGroup/SC-DAG.

Software development is a repetitive task, as developers usually reuse or get inspiration from existing implementations. Code search, which refers to the retrieval of relevant code snippets from a codebase according to the developer’s intent that has been expressed as a query, has become increasingly important in the software development process. Due to the success of deep learning in various applications, a great number of deep learning-based code search approaches have sprung up and achieved promising results. However, developers may not follow the same naming conventions and the same variable may have different variable names in different implementations, bringing a challenge to deep learning-based code search methods that rely on explicit variable correspondences to understand source code. To overcome this challenge, we propose a Naming-Agnostic Code Search (NACS) method based on contrastive multi-view code representation learning. NACS strips information bound to variable names from Abstract Syntax Tree (AST), the representation of the abstract syntactic structure of source code, and focuses on capturing intrinsic properties solely from AST structures. We use semantic-level and syntax-level augmentation techniques to prepare realistically rational data and adopt contrastive learning to design a graph-view modeling component in NACS to enhance the understanding of code snippets. We further model ASTs in a path view to strengthen the graph-view modeling component through multi-view learning. Extensive experiments show that NACS provides superior code search performance compared to baselines and NACS can be adapted to help existing code search methods overcome the impact of different naming conventions. Our implementation is available at https://github.com/KDEGroup/NACS .

Recommendation systems (RS) are crucial for alleviating the information overload problem. Due to its pivotal role in guiding users to make decisions, unscrupulous parties are lured to launch attacks against RS to affect the decisions of normal users and gain illegal profits. Among various types of attacks, shilling attack is one of the most subsistent and profitable attacks. In shilling attack, an adversarial party injects a number of well-designed fake user profiles into the system to mislead RS so that the attack goal can be achieved. Although existing shilling attack methods have achieved promising results, they all adopt the attack paradigm of multi-user injection, where some fake user profiles are required. This paper provides the first study of shilling attack in an extremely limited scenario: only one fake user profile is injected into the victim RS to launch shilling attacks (i.e., single-user injection). We propose a novel single-user injection method SUI-Attack for invisible shilling attack. SUI-Attack is a graph based attack method that models shilling attack as a node generation task over the user-item bipartite graph of the victim RS, and it constructs the fake user profile by generating user features and edges that link the fake user to items. Extensive experiments demonstrate that SUI-Attack can achieve promising attack results in single-user injection. In addition to its attack power, SUI-Attack increases the stealthiness of shilling attack and reduces the risk of being detected. We provide our implementation at: https://github.com/KDEGroup/SUI-Attack.

Heterogeneous Information Network based Recommender Systems (HIN-based RS) can model the complex interactions between different objects in RS. However, existing models assume HIN is invariable and merely use HIN as a data source for assisting recommendation. In this paper, we summarize our multi-task learning framework MTRec for recommendation over HIN. MTRec relies on the self-attention mechanism to learn the semantics of meta-paths in HIN and jointly optimizes the tasks of both recommendation and link prediction. Using a Bayesian task weight learner, MTRec is able to achieve the balance of two tasks during optimization automatically. Moreover, MTRec provides good interpretability of recommendation through a “translation” mechanism which is used to model the three-way interactions among users, items and the meta-paths connecting them. Experimental results demonstrate the effectiveness and the robustness of MTRec over state-of-the-art models.

Nowadays, Multi-purpose Messaging Mobile App (MMMA) has become increasingly prevalent. MMMAs attract fraudsters and some cybercriminals provide support for frauds via black market accounts (BMAs). Compared to fraudsters, BMAs are not directly involved in frauds and are more difficult to detect. This paper illustrates our BMA detection system SGRL (Self-supervised Graph Representation Learning) used in WeChat, a representative MMMA with over a billion users. We tailor Graph Neural Network and Graph Self-supervised Learning in SGRL for BMA detection. The workflow of SGRL contains a pretraining phase that utilizes structural information, node attribute information and available human knowledge, and a lightweight detection phase. In offline experiments, SGRL outperforms state-of-the-art methods by 16.06%-58.17% on offline evaluation measures. We deploy SGRL in the online environment to detect BMAs on the billion-scale WeChat graph, and it exceeds the alternative by 7.27% on the online evaluation measure. In conclusion, SGRL can alleviate label reliance, generalize well to unseen data, and effectively detect BMAs in WeChat.

We study route planning that utilizes historical trajectories to predict a realistic route from a source to a destination on a road network at given departure time. Route planning is a fundamental task in many location-based services. It is challenging to capture latent patterns implied by complex trajectory data for accurate route planning. Recent studies mainly resort to deep learning techniques that incur immense computational costs, especially on massive data, while their effectiveness are complicated to interpret. This paper proposes DRPK, an effective and efficient route planning method that achieves state-of-the-art performance via a series of novel algorithmic designs. In brief, observing that a route planning query (RPQ) with closer source and destination is easier to be accurately predicted, we fulfill a promising idea in DRPK to first detect the key segment of an RPQ by a classification model KSD, in order to split the RPQ into shorter RPQs, and then handle the shorter RPQs by a destination-driven route planning procedure DRP. Both KSD and DRP modules rely on a directed association (DA) indicator, which captures the dependencies between road segments from historical trajectories in a surprisingly intuitive but effective way. Leveraging the DA indicator, we develop a set of well-thought-out key segment concepts that holistically consider historical trajectories and RPQs. KSD is powered by effective encoders to detect high-quality key segments, without inspecting all segments in a road network for efficiency. We conduct extensive experiments on 5 large-scale datasets. DRPK consistently achieves the highest effectiveness, often with a significant margin over existing methods, while being much faster to train. Moreover, DRPK is efficient to handle thousands of online RPQs in a second,e.g., 2768 RPQs per second on a PT dataset,i.e., 0.36 milliseconds per RPQ.

Review-based recommender systems (RRS) have received an increasing interest since reviews greatly enhance recommendation quality and interpretability. However, existing RRS suffer from high computational complexity, biased recommendation and poor generalization. The three problems make them inadequate to handle real recommendation scenarios. Previous studies address each issue separately, while none of them consider solving three problems together under a unified framework. This paper presents LUME (a Lightweight Unbiased Multi-teacher Ensemble) for RRS. LUME is a novel framework that addresses the three problems simultaneously. LUME uses multi-teacher ensemble and debiased knowledge distillation to aggregate knowledge from multiple pretrained RRS, and generates a small, unbiased student recommender which generalizes better. Extensive experiments on various real-world benchmarks demonstrate that LUME successfully tackles the three problems and has superior performance than state-of-the-art RRS and knowledge distillation based RS.

Configuration tuning is vital to optimize the performance of big data analysis platforms like Spark. Existing methods (e.g. auto-tuning relational databases) are not effective for tuning Spark, because the unique characteristics of Spark pose new challenges to configuration tuning. (C1) The Spark applications own various code structures and semantics, and the code features significantly affect Spark performance and configuration selection; (C2) Spark applications are extremely time-consuming on big data. It is infeasible for approaches such as Bayesian Optimization and Reinforcement Learning to collect sufficient training instances or repeatedly execute the applications; (C3) Spark supports various analytical applications and the tuning system needs to adapt to different applications. To address these challenges, we propose a LIghtweighT knob rEcommender system (LITE) for auto-tuning Spark configurations on various analytical applications and large-scale datasets. We first propose a code learning framework that can utilize code features to learn complex correlations between application performance and knob values (addressing C1). We then propose a lightweight auto-tuning method that migrates the knowledge learned from small-scale datasets to large-scale datasets (addressing C2). Next, to generalize to different Spark applications, we propose an adaptive model update approach to fine-tune the model via adversarial learning with newly collected feedback (addressing C3). Extensive experiments showed that LITE achieves much better performance compared with state-of-the-art auto-tuning methods.

The importance of modeling contextual information within a search session has been widely acknowledged. However, learning representations of multi-query multi-modal (MM) search, in which Mobile Taobao users repeatedly submit textual and visual queries, remains unexplored in literature. Previous work which learns task-specific representations of textual query sessions fails to capture diverse query types and correlations in MM search sessions. This paper presents to represent MM search sessions by heterogeneous graph neural network (HGN). A multi-view contrastive learning framework is proposed to pretrain the HGN, with two views to model different intra-query, inter-query, and inter-modality information diffusion in MM search. Extensive experiments demonstrate that, the pretrained session representation can benefit state-of-the-art baselines on various downstream tasks, such as personalized click prediction, query suggestion, and intent classification.

Instant messaging client (IMC) is now an essential tool for mobile users. In the representative IMC We Chat, cybercriminals deceive frauds, causing financial loss to normal users. Through statistical analysis, we find that certain fraud interactions commonly occur among WeChat users who are not k-hop neighbors. Therefore, efficiently answering whether the distance between two vertices is not longer than k at a certain time point (i.e., k-hop reachability queries) over the dynamic social graph of WeChat becomes a crucial task for fraud feature extraction in the detection system: it can help human experts quickly identify suspicious user interactions and the query results can be further used as the input feature to the downstream machine learning based detection methods. In this paper, we illustrate Bidirectional k-hop Reachability Query Processing over a Dynamic Graph (BREAD) that is used in WeChat for extracting the k-hop reachability feature for fraud detection. BREAD adopts the idea of estimating Personalized PageRank value. It first conducts the backward search from the destination vertex to construct an intermediate vertex set. Then, it performs a certain amount of random walks from the start vertex to see whether they can hit the intermediate vertex set, and the results are returned to answer k-hop reachability queries. We further propose$\text{BREAD}++$that leverages the massive parallel processing power of GPU to achieve a considerable performance gain. Experiments on several large-scale dynamic graph benchmarks and the social graph of WeChat have demonstrated that$\text{BREAD}/\text{BREAD}++$is superior than existing index-free competitors: our methods provide not only fast but also accurate responses and they are of practical value to k-hop reachability feature extraction in the fraud detection system of WeChat. Our implementation is available at https://github.com/XMUDM/BREAD.

Most existing recommendation models learn vectorized representations for items, i.e., item embeddings to make predictions. Item embeddings inherit popularity bias from the data, which leads to biased recommendations. We use this observation to design two simple and effective strategies, which can be flexibly plugged into different backbone recommendation models, to learn popularity neutral item representations. One strategy isolates popularity bias in one embedding direction and neutralizes the popularity direction post-training. The other strategy encourages all embedding directions to be disentangled and popularity neutral. We demonstrate that the proposed strategies outperform state-of-the-art debiasing methods on various real-world datasets, and improve recommendation quality of shallow and deep backbone models.

Traditional recommender systems (RS) only consider homogeneous data and cannot fully model heterogeneous information of complex objects and relations. Recent advances in the study of Heterogeneous Information Network (HIN) have shed some light on how to leverage heterogeneous information in RS. However, existing HIN-based recommendation models assume HIN is invariable and merely use HIN as a data source for assisting recommendation, which limits their performance. In this paper, we propose a multi-task learning framework, called MTRec, for recommendation over HIN. MTRec relies on self-attention mechanism to learn the semantics of meta-paths in HIN and jointly optimizes the tasks of both recommendation and link prediction. Using a Bayesian task weight learner, MTRec is able to achieve the balance of two tasks during optimization automatically. Moreover, MTRec provides good interpretabilities of recommendation through a “translation” mechanism which is used to model the three-way interactions among users, items and the meta-paths connecting them. Experimental results demonstrate the superiority of MTRec over state-of-the-art HIN-based recommendation models, and the case studies we provide illustrate that MTRec enhances the explainability of RS.

Nonnegative matrix factorization (NMF) has been successfully applied in several data mining tasks. Recently, there is an increasing interest in the acceleration of NMF, due to its high cost on large matrices. On the other hand, the privacy issue of NMF over federated data is worthy of attention, since NMF is prevalently applied in image and text analysis which may involve leveraging privacy data (e.g, medical image and record) across several parties (e.g., hospitals). In this paper, we study theaccelerationandsecurityproblems of distributed NMF. First, we propose adistributed sketched alternating nonnegative least squares(DSANLS) framework for NMF, which utilizes a matrix sketching technique to reduce the size of nonnegative least squares subproblems with a convergence guarantee. For the second problem, we show that DSANLS with modification can be adapted to the security setting, but only forone or limited iterations. Consequently, we propose four efficient distributed NMF methods in both synchronous and asynchronous settings with a security guarantee. We conduct extensive experiments on several real datasets to show the superiority of our proposed methods. The implementation of our methods is available athttps://github.com/qianyuqiu79/DSANLS.

Social Recommender Systems (SRS) have attracted considerable attention since its accompanying service, social networks, helps increase user satisfaction and provides auxiliary information to improve recommendations. However, most existing SRS focus on social influence and ignore another essential social phenomenon, i.e., social homophily. Social homophily, which is the premise of social influence, indicates that people tend to build social relations with similar people and form influence propagation paths. In this article, we propose a generic framework Social PathExplorer (SPEX) to enhance neural SRS. SPEX treats the neural recommendation model as a black box and improves the quality of recommendations by modeling the social recommendation task, the formation of social homophily, and their mutual effect in the manner of multi-task learning. We design a Graph Neural Network based component for influence propagation path prediction to help SPEX capture the rich information conveyed by the formation of social homophily. We further propose an uncertainty based task balancing method to set appropriate task weights for the recommendation task and the path prediction task during the joint optimization. Extensive experiments have validated that SPEX can be easily plugged into various state-of-the-art neural recommendation models and help improve their performance. The source code of our work is available at: https://github.com/XMUDM/SPEX.

Biases and de-biasing in recommender systems (RS) have become a research hotspot recently. This paper reveals an unexplored type of bias, i.e., sentiment bias. Through an empirical study, we find that many RS models provide more accurate recommendations on user/item groups having more positive feedback (i.e., positive users/items) than on user/item groups having more negative feedback (i.e., negative users/items). We show that sentiment bias is different from existing biases such as popularity bias: positive users/items do not have more user feedback (i.e., either more ratings or longer reviews). The existence of sentiment bias leads to low-quality recommendations to critical users and unfair recommendations for niche items. We discuss the factors that cause sentiment bias. Then, to fix the sources of sentiment bias, we propose a general de-biasing framework with three strategies manifesting in different regularizers that can be easily plugged into RS models without changing model architectures. Experiments on various RS models and benchmark datasets have verified the effectiveness of our de-biasing framework. To our best knowledge, sentiment bias and its de-biasing have not been studied before. We hope that this work can help strengthen the study of biases and de-biasing in RS.

Online gaming is growing faster than ever before, with increasing challenges of providing better user experience. Recommender systems (RS) for online games face unique challenges since they must fulfill players’ distinct desires, at different user levels, based on their action sequences of various action types. Although many sequential RS already exist, they are mainly single-sequence, single-task, and single-user-level. In this paper, we introduce a new sequential recommendation model for multiple sequences, multiple tasks, and multiple user levels (abbreviated as M3Rec) in Tencent Games platform, which can fully utilize complex data in online games. We leverage Graph Neural Network and multi-task learning to design M3Rec in order to model the complex information in the heterogeneous sequential recommendation scenario of Tencent Games. We verify the effectiveness of M3Rec on three online games of Tencent Games platform, in both offline and online evaluations. The results show that M3Rec successfully addresses the challenges of recommendation in online games, and it generates superior recommendations compared with state-of-the-art sequential recommendation approaches.

Online text streams such as Twitter are the major information source for users when they are looking for ongoing events. Realtime event summarization aims to generate and update coherent and concise summaries to describe the state of a given event. Due to the enormous volume of continuously coming texts, realtime event summarization has become the de facto tool to facilitate information acquisition. However, there exists a challenging yet unexplored issue in current text summarization techniques: how to preserve the integrity, i.e., the accuracy and consistency of summaries during the update process. The issue is critical since online text stream is dynamic and conflicting information could spread during the event period. For example, conflicting numbers of death and injuries might be reported after an earthquake. Such misleading information should not appear in the earthquake summary at any timestamp. In this article, we present a novel realtime event summarization framework called IAEA (i.e., Integrity-Aware Extractive-Abstractive realtime event summarization). Our key idea is to integrate an inconsistency detection module into a unified extractive–abstractive framework. In each update, important new tweets are first extracted in an extractive module, and the extraction is refined by explicitly detecting inconsistency between new tweets and previous summaries. The extractive module is able to capture the sentence-level attention which is later used by an abstractive module to obtain the word-level attention. Finally, the word-level attention is leveraged to rephrase words. We conduct comprehensive experiments on real-world datasets. To reduce efforts required for building sufficient training data, we also provide automatic labeling steps of which the effectiveness has been empirically verified. Through experiments, we demonstrate that IAEA can generate better summaries with consistent information than state-of-the-art approaches.

Recommendation Systems (RS) have become an essential part of many online services. Due to its pivotal role in guiding customers towards purchasing, there is a natural motivation for unscrupulous parties to spoof RS for profits. In this paper, we study the shilling attack: a subsistent and profitable attack where an adversarial party injects a number of user profiles to promote or demote a target item. Conventional shilling attack models are based on simple heuristics that can be easily detected, or directly adopt adversarial attack methods without a special design for RS. Moreover, the study on the attack impact on deep learning based RS is missing in the literature, making the effects of shilling attack against real RS doubtful. We present a novel Augmented Shilling Attack framework (AUSH) and implement it with the idea of Generative Adversarial Network. AUSH is capable of tailoring attacks against RS according to budget and complex attack goals, such as targeting a specific user group. We experimentally show that the attack impact of AUSH is noticeable on a wide range of RS including both classic and modern deep learning based RS, while it is virtually undetectable by the state-of-the-art attack detection model.

Sequential recommendation is one of the main tasks in recommender systems, where the next action (e.g., purchase, visit, and click) of the user is predicted based on his/her past sequence of actions. Translating Embeddings is a knowledge graph completion approach which was recently adapted to a translation-based sequential recommendation (TransRec) method. We observe a flaw of TransRec when handling complex translations, which hinders it from generating accurate suggestions. In view of this, we propose a translation-based recommender for complex users (CTransRec), which utilizes category-specific projection and temporal dynamic relaxation. Using our proposed Margin-based Pairwise Bayesian Personalized Ranking and Time-Aware Negative Sampling, CTransRec outperforms state-of-the-art methods for sequential recommendation on extremely sparse data. The superiority of CTransRec, which is confirmed by our extensive experiments on both public data and real data obtained from the industry, comes from not only the additional information used in training but also the fact that CTransRec makes good use of this additional information to model the complex translations.

Modeling sentiment evolution for social incidents in Microblogs is of vital importance for both enterprises and government officials. Existing works on sentiment tracking are not satisfying, due to the lack of entity-level sentiment extraction and accurate sentiment shift detection. Identifying entity-level sentiment is challenging as Microbloggers often use multiple opinion expressions in a sentence which targets different entities. Moreover, the evolution of the background sentiment, which is essential to shift detection, is ignored in the previous study. To address these issues, we leverage the proximity information to obtain more precise entity-level sentiment extraction. Based on it, we propose to simultaneously model the evolution of background opinion and the sentiment shift using a state space model on the time series of sentiment polarities. Experiments on real data sets demonstrate that our proposed approaches outperform state-of-the-art methods on the task of modeling sentiment evolution for social incidents.

We present Mmkg, a collection of three knowledge graphs that contain both numerical features and (links to) images for all entities as well as entity alignments between pairs of KGs. Therefore, multi-relational link prediction and entity matching communities can benefit from this resource. We believe this data set has the potential to facilitate the development of novel multi-modal learning approaches for knowledge graphs. We validate the utility of Mmkg in the $$\mathtt {sameAs}$$ link prediction task with an extensive set of experiments. These experiments show that the task at hand benefits from learning of multiple feature types.

Fault-tolerant group recommendation systems based on subspace clustering successfully alleviate high-dimensionality and sparsity problems. However, the cost of recommendation grows exponentially with the size of dataset. To address this issue, we model the fault-tolerant subspace clustering problem as a search problem on graphs and present an algorithm, GraphRec, based on the concept of α-ß-core. Moreover, we propose two variants of our approach that use indexes to improve query latency. Our experiments on different datasets demonstrate that our methods are extremely fast compared to the state-of-the-art.

Location-based social networks such as Foursquare and Plancast have gained increasing popularity. On those sites, users can organize and participate in group activities; hence, recommending venues to a group is of practical importance. In this paper, we study the problem of recommending venues to groups of users and propose a Hierarchical Bayesian Model (HBGG) for this purpose. First, a generative group geographical topic model (GG) which exploits group membership, group mobility regions and group preferences is proposed. And we integrate social structure into one-class collaborative filtering as social-based collaborative filtering (SOCF) to leverage social wisdom. Through the shared latent group features, HBGG connects the group geographical model with SOCF framework for group recommendation. Experimental results on two real datasets show that our methods outperforms the state-of-the-art group recommenders, especially on cold-start user groups.

Recommender systems have many successful applications in e-commerce and social media, including Amazon, Netflix, and Yelp. Matrix Factorization (MF) is one of the most popular recommendation approaches; the original user-product rating matrix R with millions of rows and columns is decomposed into a user matrix Q and an item matrix P, such that the product QT P approximates R. Each column q (p) of Q (P) holds the latent factors of the corresponding user (item), and qT p is a prediction of the rating to item p by user q. Recommender systems based on MF suggest to a user in q the items with the top-k scores in qT P. For this problem, we propose a Fast and EXact Inner PROduct retrieval (FEXIPRO) framework, based on sequential scan, which includes three elements. First, FEXIPRO applies an SVD transformation to P, after which the first several dimensions capture a large percentage of the inner products. This enables us to prune item vectors by only computing their partial inner products with q. Second, we construct an integer approximation version of P, which can be used to compute fast upper bounds for the inner products that can prune item vectors. Finally, we apply a lossless transformation to P, such that the resulting matrix has only positive values, allowing for the inner products to be monotonically increasing with dimensionality. Experiments on real data demonstrate that our framework outperforms alternative approaches typically by an order of magnitude.

Recommender systems have become de facto tools for suggesting items that are of potential interest to users. Predicting a user's rating on an item is the fundamental recommendation task. Traditional methods that generate predictions by analyzing the user-item rating matrix perform poorly when the matrix is sparse. Recent approaches use data from social networks to improve accuracy. However, most of the social-network based recommender systems only consider direct friendships and they are less effective when the targeted user has few social connections. In this paper, we propose two alternative models that incorporate the overlapping community regularization into the matrix factorization framework. Our empirical study on four real datasets shows that our approaches outperform the state-of-the-art algorithms in both traditional and social-network based recommender systems regarding both cold-start users and normal users.

With the rapid expansion of online social networks, social network-based recommendation has become a meaningful and effective way of suggesting new items or activities to users. In this paper, we propose two methods to improve the performance of the state-of-art social network-based recommender system (SNRS), which is based on a probabilistic model. Our first method classifies the correlations between pairs of users' ratings. The other is making the system robust to sparse data, i.e., few immediate friends having few common ratings with the target user. Our experimental study demonstrates that our techniques significantly improve the accuracy of SNRS.