Incorporating social relations into the recommendation system, i.e. social recommendation, has been widely studied in academic and industrial communities. While many promising results have been achieved, existing methods mostly assume that the social relations can be homogeneously applied to all the items, which is not practical for users' actually diverse preferences. In this paper, we argue that the effect of the social relations should be inhomogeneous, that is, two socially-related users may only share the same preference on some specific items, while for the other products, their preferences can be inconsistent or even contradictory. Inspired by this idea, we build a novel social recommendation model, where the traditional pair-wise "user-user'' relation is extended to the triple relation of "user-item-user''. To well handle such high-order relations, we base our framework on the hypergraph. More specifically, each hyperedge connects a user-user-item triplet, representing that the two users share similar preferences on the item. We develop a Social HyperGraph Convolutional Network (short for SHGCN) to learn from the complex triplet social relations. With the hypergraph convolutional networks, the social relations can be modeled in a more fine-grained manner, which more accurately depicts real users' preferences, and benefits the recommendation performance. Extensive experiments on two real-world datasets demonstrate our model's effectiveness. Studies on data sparsity and hyper-parameter studies further validate our model's rationality. Our codes and dataset are available at https://github.com/ziruizhu/SHGCN.
Session-based recommendation (SBR) focuses on next-item prediction at a certain time point. As user profiles are generally not available in this scenario, capturing the user intent lying in the item transitions plays a pivotal role. Recent graph neural networks (GNNs) based SBR methods regard the item transitions as pairwise relations, which neglect the complex high-order information among items. Hypergraph provides a natural way to capture beyond-pairwise relations, while its potential for SBR has remained unexplored. In this paper, we fill this gap by modeling session-based data as a hypergr…
Session-based recommender systems aim to improve recommendations in short-term sessions that can be found across many platforms. A critical challenge is to accurately model user intent with only limited evidence in these short sessions. For example, is a flower bouquet being viewed meant as part of a wedding purchase or for home decoration? Such different perspectives greatly impact what should be recommended next. Hence, this paper proposes a novel session-based recommendation system empowered by hypergraph attention networks. Three unique properties of the proposed approach are: (i) it const…
Social relations are often used to improve recommendation quality when user-item interaction data is sparse in recommender systems. Most existing social recommendation models exploit pairwise relations to mine potential user preferences. However, real-life interactions among users are very complicated and user relations can be high-order. Hypergraph provides a natural way to model complex high-order relations, while its potentials for improving social recommendation are under-explored. In this paper, we fill this gap and propose a multi-channel hypergraph convolutional network to enhance socia…
Against Filter Bubbles: Diversified Music Recommendation via Weighted Hypergraph Embedding Learning
Business School, Sichuan University（四川大学商学院）；School of Computer Science and Software Engineering, Southwest Petroleum University（西南石油大学计算机科学与工程学院）；The Hong Kong Polytechnic University（香港理工大学）；University of Illinois at Chicago（芝加哥大学伊利诺伊分校）
PHKT:Personalized Dynamic Hypergraph-enhanced KAN-Transformer for Multi-behavior Sequential Recommendation
Hangzhou Dianzi University（杭州电子科技大学）；School of Computer Science and Technology, Hangzhou Dianzi University（杭州电子科技大学计算机科学与技术学院）；Sanya Traditional Chinese Medicine Hospital（三亚市中医医院）；Combat Support College, Rocket Force University of Engineering（火箭军工程大学勤务学院）；Fuxi AI Lab, NetEase Games, NetEase Inc.（网易伏羲实验室，网易游戏，网易公司）
Chongqing University（重庆大学）；The University of Queensland（昆士兰大学）；Shandong Normal University（山东师范大学）；University of Virginia（弗吉尼亚大学）
With the prevalence of social media, there has recently been a proliferation of recommenders that shift their focus from individual modeling to group recommendation. Since the group preference is a mixture of various predilections from group members, the fundamental challenge of group recommendation is to model the correlations among members. Existing methods mostly adopt heuristic or attention-based preference aggregation strategies to synthesize group preferences. However, these models mainly focus on the pairwise connections of users and ignore the complex high-order interactions within and…
Multi-Modal Hypergraph Enhanced LLM Learning for Recommendation
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；Shituoyun (Nanjing) Technology Co., Ltd（石图云（南京）科技有限公司）；School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）
FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal Decoupling
Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Peng Cheng Laboratory（鹏城实验室）；Sun Yat-sen University（中山大学）；Chongqing University（重庆大学）；Shanghai Pudong Development Bank（上海浦东发展银行）
Unified Pretraining for Recommendation via Task Hypergraphs
University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Salesforce AI Research（赛富时人工智能研究院）；Beihang University（北京航空航天大学）；Kunming University of Science and Technology（昆明理工大学）
University of Hong Kong（香港大学）；National University of Singapore（新加坡国立大学）；Ocean University of China（中国海洋大学）；Wuhan University（武汉大学）
Learning dynamic user preference has become an increasingly important component for many online platforms (e.g., video-sharing sites, e-commerce systems) to make sequential recommendations. Previous works have made many efforts to model item-item transitions over user interaction sequences, based on various architectures, e.g., recurrent neural networks and self-attention mechanism. Recently emerged graph neural networks also serve as useful backbone models to capture item dependencies in sequential recommendation scenarios. Despite their effectiveness, existing methods have far focused on ite…
Recommender systems are typically designed to fulfill end user needs. However, in some domains the users are not the only stakeholders in the system. For instance, in a news aggregator website users, authors, magazines as well as the platform itself are potential stakeholders. Most of the collaborative filtering recommender systems suffer from popularity bias. Therefore, if the recommender system only considers users' preferences, presumably it over-represents popular providers and under-represents less popular providers. To address this issue one should consider other stakeholders in the gene…
Hypergraph Enhanced Knowledge Tree Prompt Learning for Next-Basket Recommendation
Sun Yat-Sen University（中山大学）；Wens Foodstuff Group Co., Ltd.（温氏食品集团股份有限公司）；Guangdong Polytechnic Normal University（广东技术师范大学）；University of Illinois（伊利诺伊大学）
HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation
Sun Yat-sen University（中山大学）；Peng Cheng Laboratory（鹏城实验室）；South China Agricultural University（华南农业大学）；Guangdong University of Technology（广东工业大学）；Jinan University（暨南大学）
MMHCL: Multi-Modal Hypergraph Contrastive Learning for Recommendation
School of Computer Science and Engineering, Nanjing University of Science and Technology（计算机科学与工程学院，南京理工大学）；Shituoyun (Nanjing) Technology Co., Ltd（石墨云（南京）科技有限公司）；School of Artificial Intelligence, Beijing Normal University（人工智能学院，北京师范大学）
HypeMed: Enhancing Medication Recommendations with Hypergraph-Based Patient Relationships
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学耿丽学校人工智能学院）；Microsoft Research Asia（微软亚洲研究院）；Beijing Key Laboratory of Research on Large Models（北京大模型研究关键实验室）；Engineering Research Center of Next-Generation Intelligent Search（下一代智能搜索与推荐工程研究中心）；Engineering Research Center of Next-Generation Intelligent Search and Recommendation, MOE（下一代智能搜索与推荐工程研究中心，教育部）
University of Hong Kong（香港大学）；South China University of Technology（华南理工大学）；Wilfrid Laurier University（威尔弗里德·劳雷尔大学）；Baidu Inc.（百度公司）；York University（约克大学）
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 supe…
Heterogeneous Hypergraph Embedding for Recommendation Systems
Griffith University（格里菲斯大学）；Hanoi University of Science and Technology（河内科技大学）；Ecole Polytechnique Federale de Lausanne（洛桑联邦理工学院）；The University of Queensland（昆士兰大学）
University of Hong Kong（香港大学）；Brandeis University（布兰迪斯大学）
Graph Neural Networks (GNNs) have been shown as promising solutions for collaborative filtering (CF) with the modeling of user-item interaction graphs. The key idea of existing GNN-based recommender systems is to recursively perform the message passing along the user-item interaction edge for refining the encoded embeddings. Despite their effectiveness, however, most of the current recommendation models rely on sufficient and high-quality training data, such that the learned representations can well capture accurate user preference. User behavior data in many practical recommendation scenarios…
Dalian University of Technology（大连理工大学）；Wuhan University（武汉大学）；Pennsylvania State University（宾夕法尼亚州立大学）
Session-based recommendation aims to predict items that an anonymous user would like to purchase based on her short behavior sequence. The current approaches towards session-based recommendation only focus on modeling users' interest preferences, while they all ignore a key attribute of an item, i.e., the price. Many marketing studies have shown that the price factor significantly influences users' behaviors and the purchase decisions of users are determined by both price and interest preferences simultaneously. However, it is nontrivial to incorporate price preferences for session-based recom…
Towards Efficient Hypergraph and Multi-LLM Agent Recommender Systems
Research Ireland ML-LABS；Dublin City University（都柏林城市大学）；ADAPT Centre（ADAPT中心）；School of Computing（计算学院）；Insight Research Ireland Centre for Data Analytics（Insight Research Ireland 数据分析中心）；University College Dublin（都柏林大学）
In this paper B-Rank, an efficient ranking algorithm for recommender systems, is proposed. B-Rank is based on a random walk model on hypergraphs. Depending on the setup, B-Rank outperforms other state of the art algorithms in terms of precision, recall (19% - 50%), and inter list diversity (20% - 60%). B-Rank captures well the difference between popular and niche objects. The proposed algorithm produces very promising results for sparse and dense voting matrices. Furthermore, a recommendation list update algorithm is introduced,to cope with new votes. This technique significantly reduces compu…
In this work, we study group recommendation in a particular scenario, namely Occasional Group Recommendation (OGR). Most existing works have addressed OGR by aggregating group members' personal preferences to learn the group representation. However, the representation learning for a group is most complex beyond the fusion of group member representation, as the personal preferences and group preferences may be in different spaces. In addition, the learned user representation is not accurate due to the sparsity of users' interaction data. Moreover, the group similarity in terms of common group m…
Structurally Refined Graph Transformer for Multimodal Recommendation
School of Computer Science, Hubei University（湖北大学计算机学院）；Hubei Key Laboratory of Big Data Intelligent Analysis and Application, Hubei University（湖北大学大数据智能分析与应用重点实验室）；Key Laboratory of Intelligent Sensing System and Security (Hubei University), Ministry of Education（智能传感系统与安全重点实验室（湖北大学））；Institute of Vocational Education, Guangdong Industry Polytechnic University（广东职业技术学院教育学院）
To enhance the performance of the recommender system, side information is extensively explored with various features (e.g., visual features and textual features). However, there are some demerits of side information: (1) the extra data is not always available in all recommendation tasks; (2) it is only for items, there is seldom high-level feature describing users. To address these gaps, we introduce the spectral features extracted from two hypergraph structures of the purchase records. Spectral features describe the \textit{similarity} of users/items in the graph space, which is critical for …
