Disentangled hypergraph collaborative filtering for social recommendation

Xiao Liu, Shunmei Meng*, Qianmu Li*, Xiaolong Xu, Lianyong Qi, Wanchun Dou, Jing Zhang, Xuyun Zhang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

In the current era of information overload, service recommendations have emerged as a valuable tool for enhancing the user experience. Among them, social recommendation models have shown promising results by incorporating social relationships to improve representation learning. However, most of these models lack fine-grained modeling of social user behavior, leading to a unified representation of users and a loss of expressiveness in user representations. To address this issue, we propose DisenHGCF, a new social recommendation approach based on disentangled hypergraph collaborative filtering, to disentangle the representations of users and items at the granularity of social users' intents. This approach aims to provide a more nuanced understanding of the user, leading to more accurate and personalized recommendations. To be specific, DisenHGCF leverages hypergraphs to represent the complex relationships among users, friends, and items. By using the hypergraph disentangling module based on attention, it is able to disentangle the user's intents and generate users representations in cooperating their intents for recommendation tasks. Additionally, a contrastive learning task based on intent-weight perturbation is designed to enhance representational learning. The experimental results obtained from the BeiBei and Beidian datasets demonstrate the superiority of our proposed approach in comparison to previous baseline methods, as evidenced by higher Recall and NDCG scores.

Original languageEnglish
Title of host publication2023 IEEE International Conference on Web Services IEEE ICWS 2023
Subtitle of host publicationproceedings
EditorsClaudio Ardagna, Boualem Benatallah, Hongyi Bian, Carl K. Chang, Rong N. Chang, Jing Fan, Geoffrey C. Fox, Zhi Jin, Xuanzhe Liu, Heiko Ludwig, Michael Sheng, Jian Yang
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages475-482
Number of pages8
ISBN (Electronic)9798350304855
ISBN (Print)9798350304862
DOIs
Publication statusPublished - 2023
Event2023 IEEE International Conference on Web Services, ICWS 2023 - Hybrid, Chicago, United States
Duration: 2 Jul 20238 Jul 2023

Publication series

Name
ISSN (Print)2836-3876
ISSN (Electronic)2836-3868

Conference

Conference2023 IEEE International Conference on Web Services, ICWS 2023
Country/TerritoryUnited States
CityHybrid, Chicago
Period2/07/238/07/23

Fingerprint

Dive into the research topics of 'Disentangled hypergraph collaborative filtering for social recommendation'. Together they form a unique fingerprint.

Cite this