OpenRaiser/Intern-Atlas huggingface dataset:
Dual channel hypergraph collaborative filtering
Authors: Shuyi Ji, Yifan Feng 0001, Rongrong Ji, Xibin Zhao, Wanwan Tang, Yue Gao 0002
Venue: Conference KDD 2020
Year: 2020

Collaborative filtering (CF) is one of the most popular and important recommendation methodologies in the heart of numerous recommender systems today. Although widely adopted, existing CF-based methods, ranging from matrix factorization to the emerging graph-based methods, suffer inferior performance especially when the data for training are very limited. In this paper, we first pinpoint the root causes of such deficiency and observe two main disadvantages that stem from the inherent designs of existing CF-based methods, i.e., 1) inflexible modeling of users and items and 2) insufficient modeling of high-order correlations among the subjects. Under such circumstances, we propose a dual channel hypergraph collaborative filtering (DHCF) framework to tackle the above issues. First, a dual channel learning strategy, which holistically leverages the divide-and-conquer strategy, is introduced to learn the representation of users and items so that these two types of data can be elegantly interconnected while still maintaining their specific properties. Second, the hypergraph structure is employed for modeling users and items with explicit hybrid high-order correlations. The jump hypergraph convolution (JHConv) method is proposed to support the explicit and efficient embedding propagation of high-order correlations. Comprehensive experiments on two public benchmarks and two new real-world datasets demonstrate that DHCF can achieve significant and consistent improvements against other state-of-the-art methods.
TEM: Tree-enhanced embedding model for explainable recommendation
Authors: Xiang Wang, Xiangnan He, Fuli Feng, Liqiang Nie, Tat-Seng Chua
Venue: Conference WWW 2018
Year: 2018

While collaborative filtering is the dominant technique in personalized recommendation, it models user-item interactions only and cannot provide concrete reasons for a recommendation. Meanwhile, the rich side information affiliated with user-item interactions (e.g., user demographics and item attributes), which provide valuable evidence that why a recommendation is suitable for a user, has not been fully explored in providing explanations. On the technical side, embedding-based methods, such as Wide&Deep and neural factorization machines, provide state-of-the-art recommendation performance. However, they work like a black-box, for which the reasons underlying a prediction cannot be explicitly presented. On the other hand, tree-based methods like decision trees predict by inferring decision rules from data. While being explainable, they cannot generalize to unseen feature interactions thus fail in collaborative filtering applications. In this work, we propose a novel solution named Tree-enhanced Embedding Method that combines the strengths of embedding-based and tree-based models. We first employ a tree-based model to learn explicit decision rules (aka. cross features) from the rich side information. We next design an embedding model that can incorporate explicit cross features and generalize to unseen cross features on user ID and item ID. At the core of our embedding method is an easy-to-interpret attention network, making the recommendation process fully transparent and explainable. We conduct experiments on two datasets of tourist attraction and restaurant recommendation, demonstrating the superior performance and explainability of our solution.
Item silk road: Recommending items from information domains to social users
Authors: Xiang Wang, Xiangnan He, Liqiang Nie, Tat-Seng Chua
Venue: Conference SIGIR 2017
Year: 2017

Online platforms can be divided into information-oriented and social-oriented domains. The former refers to forums or E-commerce sites that emphasize user-item interactions, like Trip.com and Amazon; whereas the latter refers to social networking services (SNSs) that have rich user-user connections, such as Facebook and Twitter. Despite their heterogeneity, these two domains can be bridged by a few overlapping users, dubbed as bridge users. In this work, we address the problem of cross-domain social recommendation, i.e., recommending relevant items of information domains to potential users of social networks. To our knowledge, this is a new problem that has rarely been studied before. Existing cross-domain recommender systems are unsuitable for this task since they have either focused on homogeneous information domains or assumed that users are fully overlapped. Towards this end, we present a novel Neural Social Collaborative Ranking (NSCR) approach, which seamlessly sews up the user-item interactions in information domains and user-user connections in SNSs. In the information domain part, the attributes of users and items are leveraged to strengthen the embedding learning of users and items. In the SNS part, the embeddings of bridge users are propagated to learn the embeddings of other non-bridge users. Extensive experiments on two real-world datasets demonstrate the effectiveness and rationality of our NSCR method.
Hypergraph contrastive collaborative filtering
Authors: Lianghao Xia, Chao Huang, Yong Xu, Jiashu Zhao, Dawei Yin, Jimmy X. Huang
Venue: Conference SIGIR 2022
Year: 2022

Collaborative Filtering (CF) has emerged as fundamental paradigms for parameterizing users and items into latent representation space, with their correlative patterns from interaction data. Among various CF techniques, the development of GNN-based recommender systems, e.g., PinSage and LightGCN, has offered the state-of-the-art performance. However, two key challenges have not been well explored in existing solutions: i) The over-smoothing effect with deeper graph-based CF architecture, may cause the indistinguishable user representations and degradation of recommendation results. ii) The supervision signals (i.e., user-item interactions) are usually scarce and skewed distributed in reality, which limits the representation power of CF paradigms. To tackle these challenges, we propose a new self-supervised recommendation framework Hypergraph Contrastive Collaborative Filtering (HCCF) to jointly capture local and global collaborative relations with a hypergraph-enhanced cross-view contrastive learning architecture. In particular, the designed hypergraph structure learning enhances the discrimination ability of GNN-based CF paradigm, so as to comprehensively capture the complex high-order dependencies among users. Additionally, our HCCF model effectively integrates the hypergraph structure encoding with self-supervised learning to reinforce the representation quality of recommender systems, based on the hypergraph-enhanced self-discrimination. Extensive experiments on three benchmark datasets demonstrate the superiority of our model over various state-of-the-art recommendation methods, and the robustness against sparse user interaction data. Our model implementation codes are available at https://github.com/akaxlh/HCCF.
Knowledge Enhancement for Contrastive Multi-Behavior Recommendation
Authors: Hongrui Xuan, Yi Liu, Bohan Li, Hongzhi Yin
Venue: Conference WSDM 2023
Year: 2023

A well-designed recommender system can accurately capture the attributes of users and items, reflecting the unique preferences of individuals. Traditional recommendation techniques usually focus on modeling the singular type of behaviors between users and items. However, in many practical recommendation scenarios (e.g., social media, e-commerce), there exist multi-typed interactive behaviors in user-item relationships, such as click, tag-as-favorite, and purchase in online shopping platforms. Thus, how to make full use of multi-behavior information for recommendation is of great importance to the existing system, which presents challenges in two aspects that need to be explored: (1) Utilizing users' personalized preferences to capture multi-behavioral dependencies; (2) Dealing with the insufficient recommendation caused by sparse supervision signal for target behavior. In this work, we propose a Knowledge Enhancement Multi-Behavior Contrastive Learning Recommendation (KMCLR) framework, including two Contrastive Learning tasks and three functional modules to tackle the above challenges, respectively. In particular, we design the multi-behavior learning module to extract users' personalized behavior information for user-embedding enhancement, and utilize knowledge graph in the knowledge enhancement module to derive more robust knowledge-aware representations for items. In addition, in the optimization stage, we model the coarse-grained commonalities and the fine-grained differences between multi-behavior of users to further improve the recommendation effect. Extensive experiments and ablation tests on the three real-world datasets indicate our KMCLR outperforms various state-of-the-art recommendation methods and verify the effectiveness of our method.
Scalable Instance Reconstruction in Knowledge Bases via Relatedness Affiliated Embedding
Authors: Richong Zhang, Junpeng Li, Jiajie Mei, Yongyi Mao
Venue: Conference WWW 2018
Year: 2018

The knowledge base (KB) completion problem is usually formulated as a link prediction problem. Such formulation is incapable of capturing certain application scenarios when the KB contains multi-fold relations. In this paper, we present a new formulation of KB completion, called instance reconstruction. Unlike its link-prediction counterpart, which has linear complexity in the size of the KB, this problem has its complexity behave as a high-degree polynomial. This presents a significant challenge in developing scalable instance reconstruction algorithms. In this paper, we present a novel knowledge embedding model (RAE) and build on it an instance reconstruction algorithm (SIR). The SIR algorithm utilizes schema-based filtering as well as "relatedness" filtering for complexity reduction. Here relatedness refers to the likelihood that two entities co-participate in a common instance, and the relatedness metric is learned from the RAE model. We show experimentally that SIR significantly reduces computation complexity without sacrificing reconstruction performance. The complexity reduction corresponds to reducing the KB size by 100 to 1000 folds.
Data Poisoning Attacks on Cross-domain Recommendation
Authors: Huiyuan Chen, Jing Li
Venue: Conference CIKM 2019
Year: 2019

Cross-domain recommendation has attracted growing interests given their simplicity and effectiveness. In the cross-domain scenarios, we may improve predictive accuracy in one domain by transferring knowledge from the other, which alleviates the data sparsity issue. However, the relatedness of these domains can be exploited by a malicious party to launch data poisoning attacks. Here we study the vulnerability of cross-domain recommendation under data poisoning attacks. We show that data poisoning attacks can be formulated as a bilevel optimization problem. Our experimental results show that cross-domain system can be compromised under attacks, highlighting the need for countermeasures against data poisoning attacks in cross-domain recommendation.
Efficient Fault- Tolerant Group Recommendation Using alpha-beta-core
Authors: Danhao Ding, Hui Li, Zhipeng Huang, Nikos Mamoulis
Venue: Conference CIKM 2017
Year: 2017

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.
Attacking recommender systems with augmented user profiles
Authors: Chen Lin, Si Chen, Hui Li, Yanghua Xiao, Lianyun Li, Qian Yang
Venue: Conference CIKM 2020
Year: 2020

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.
Esam: Discriminative domain adaptation with non-displayed items to improve long-tail performance
Authors: Zhihong Chen, Rong Xiao 0005, Chenliang Li 0005, Gangfeng Ye, Haochuan Sun, Hongbo Deng
Venue: Conference SIGIR 2020
Year: 2020

Most of ranking models are trained only with displayed items (most are hot items), but they are utilized to retrieve items in the entire space which consists of both displayed and non-displayed items (most are long-tail items). Due to the sample selection bias, the long-tail items lack sufficient records to learn good feature representations, i.e. data sparsity and cold start problems. The resultant distribution discrepancy between displayed and non-displayed items would cause poor long-tail performance. To this end, we propose an entire space adaptation model (ESAM) to address this problem from the perspective of domain adaptation (DA). ESAM regards displayed and non-displayed items as source and target domains respectively. Specifically, we design the attribute correlation alignment that considers the correlation between high-level attributes of the item to achieve distribution alignment. Furthermore, we introduce two effective regularization strategies, i.e. \textit{center-wise clustering} and \textit{self-training} to improve DA process. Without requiring any auxiliary information and auxiliary domains, ESAM transfers the knowledge from displayed items to non-displayed items for alleviating the distribution inconsistency. Experiments on two public datasets and a large-scale industrial dataset collected from Taobao demonstrate that ESAM achieves state-of-the-art performance, especially in the long-tail space. Besides, we deploy ESAM to the Taobao search engine, leading to significant improvement on online performance. The code is available at \url{https://github.com/A-bone1/ESAM.git}
ICAI-SR: Item Categorical Attribute Integrated Sequential Recommendation
Authors: Xu Yuan 0006, Dongsheng Duan, Lingling Tong, Lei Shi, Cheng Zhang
Venue: Conference SIGIR 2021
Year: 2021

Sequential recommendation (SR) has attracted much research attention in the past few years. Most existing attribute integrated SR models do not directly model the complex relations between items and categorical attributes, as well do not exploit the power of attribute sequence in predicting the next item. In this paper, we propose an Item Categorical Attribute Integrated Sequential Recommendation (ICAI-SR) framework, which consists of an Item-Attribute Aggregation (IAA) model and Entity Sequential (ES) models. In IAA model, we employ a heterogeneous graph to represent the complex relations between items and different types of categorical attributes, then the attention mechanism based neighborhood aggregation is designed to model the correlations between items and attributes. For ES models, there are one Item Sequential (IS) model and one or more Attribute Sequential (AS) models. With IS and AS models, not only the item sequence but also the attribute sequence are used to predict the next item during model training. ICAI-SR is instantiated by taking Gated Recurrent Unit (GRU) and Bidirectional Encoder Representations from Transformers (BERT) as ES models, resulting in ICAI-GRU and ICAI-BERT respectively. Extensive experiments have been conducted on three public datasets to validate the performance of ICAI-SR. Experimental Results show that ICAI-SR performs better than both basic SR models and a competitive attribute integrated SR model.
Popularity bias in dynamic recommendation
Authors: Ziwei Zhu, Yun He, Xing Zhao, James Caverlee
Venue: Conference KDD 2021
Year: 2021

Popularity bias is a long-standing challenge in recommender systems: popular items are overly recommended at the expense of less popular items that users may be interested in being under-recommended. Such a bias exerts detrimental impact on both users and item providers, and many efforts have been dedicated to studying and solving such a bias. However, most existing works situate the popularity bias in a static setting, where the bias is analyzed only for a single round of recommendation with logged data. These works fail to take account of the dynamic nature of real-world recommendation process, leaving several important research questions unanswered: how does the popularity bias evolve in a dynamic scenario? what are the impacts of unique factors in a dynamic recommendation process on the bias? and how to debias in this long-term dynamic process? In this work, we investigate the popularity bias in dynamic recommendation and aim to tackle these research gaps. Concretely, we conduct an empirical study by simulation experiments to analyze popularity bias in the dynamic scenario and propose a dynamic debiasing strategy and a novel False Positive Correction method utilizing false positive signals to debias, which show effective performance in extensive experiments.
Streaming recommender systems
Authors: Shiyu Chang, Yang Zhang, Jiliang Tang, Dawei Yin, Yi Chang, Mark A. Hasegawa-Johnson, Thomas S. Huang
Venue: Conference WWW 2017
Year: 2017

The increasing popularity of real-world recommender systems produces data continuously and rapidly, and it becomes more realistic to study recommender systems under streaming scenarios. Data streams present distinct properties such as temporally ordered, continuous and high-velocity, which poses tremendous challenges to traditional recommender systems. In this paper, we investigate the problem of recommendation with stream inputs. In particular, we provide a principled framework termed sRec, which provides explicit continuous-time random process models of the creation of users and topics, and of the evolution of their interests. A variational Bayesian approach called recursive meanfield approximation is proposed, which permits computationally efficient instantaneous on-line inference. Experimental results on several real-world datasets demonstrate the advantages of our sRec over other state-of-the-arts.
Learning contextual bandits in a non-stationary environment
Authors: Qingyun Wu, Naveen Iyer, Hongning Wang
Venue: Conference SIGIR 2018
Year: 2018

Multi-armed bandit algorithms have become a reference solution for handling the explore/exploit dilemma in recommender systems, and many other important real-world problems, such as display advertisement. However, such algorithms usually assume a stationary reward distribution, which hardly holds in practice as users' preferences are dynamic. This inevitably costs a recommender system consistent suboptimal performance. In this paper, we consider the situation where the underlying distribution of reward remains unchanged over (possibly short) epochs and shifts at unknown time instants. In accordance, we propose a contextual bandit algorithm that detects possible changes of environment based on its reward estimation confidence and updates its arm selection strategy respectively. Rigorous upper regret bound analysis of the proposed algorithm demonstrates its learning effectiveness in such a non-trivial environment. Extensive empirical evaluations on both synthetic and real-world datasets for recommendation confirm its practical utility in a changing environment.
Counteracting User Attention Bias in Music Streaming Recommendation via Reward Modification
Authors: Xiao Zhang 0034, Sunhao Dai, Jun Xu 0001, Zhenhua Dong, Quanyu Dai, Ji-Rong Wen
Venue: Conference KDD 2022
Year: 2022

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.
Latent space model for road networks to predict time-varying traffic
Authors: Dingxiong Deng, Cyrus Shahabi, Ugur Demiryurek, Linhong Zhu, Rose Yu, Yan Liu
Venue: Conference KDD 2016
Year: 2016

Real-time traffic prediction from high-fidelity spatiotemporal traffic sensor datasets is an important problem for intelligent transportation systems and sustainability. However, it is challenging due to the complex topological dependencies and high dynamics associated with changing road conditions. In this paper, we propose a Latent Space Model for Road Networks (LSM-RN) to address these challenges. In particular, given a series of road network snapshots, we learn the attributes of vertices in latent spaces which capture both topological and temporal properties. As these latent attributes are time-dependent, they can estimate how traffic patterns form and evolve. In addition, we present an incremental online algorithm which sequentially and adaptively learn the latent attributes from the temporal graph changes. Our framework enables real-time traffic prediction by 1) exploiting real-time sensor readings to adjust/update the existing latent spaces, and 2) training as data arrives and making predictions on-the-fly with given data. By conducting extensive experiments with a large volume of real-world traffic sensor data, we demonstrate the utility superiority of our framework for real-time traffic prediction on large road networks over competitors as well as a baseline graph-based LSM.