Heterogeneous information network based adaptive social influence learning for recommendation and explanation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Collaborative filtering (CF)-based recommendation systems that rely on user-item history interactions often suffer from the data sparsity problem. Social-based recommendation methods have become one of the successful methods to address this problem. However, few works have focused on the sparsity problem of social data. As real-world social networks are usually sparse, the observed relationships in social networks can only represent a limited part of a person's real social network. The sparse social data will degrade the performance of the existing social-based algorithms. Also, the influence of a user's friends on their friends is dynamic: even the same friend may impact the target user in different decision-making processes. It is difficult for an the end-to-end deep learning-based model to provide underlying reasons for the recommendation results. To this end, we propose a novel deep learning-based model to extract useful missing links as auxiliary social information to enrich the users' features for item recommendation. The framework is composed of two major components: a missing links identifier module that generates useful social links from a heterogeneous information network to enrich the user's social profile and enhance the social-based recommendation model, and an attention-based recommendation module that assigns different scores for each friend with regard to different candidate items to adaptively evaluate the quality of different social links. An attention-based fusion strategy is proposed to improve the interpretability of the recommendation system by assigning non-uniform weight to different factors. Extensive experiments on three published datasets show that our proposed method achieves better performance than other state-of-the-art methods.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020
EditorsJing He, Hemant Purohit, Guangyan Huang, Xiaoying Gao, Ke Deng
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages137-144
Number of pages8
ISBN (Electronic)9781665419246
DOIs
Publication statusPublished - 2020 Dec
Event2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020 - Virtual, Online
Duration: 2020 Dec 142020 Dec 17

Publication series

NameProceedings - 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020

Conference

Conference2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020
CityVirtual, Online
Period20/12/1420/12/17

Keywords

  • Attention
  • Heterogeneous Information Network
  • Missing Links
  • Recommendation System

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Software

Fingerprint

Dive into the research topics of 'Heterogeneous information network based adaptive social influence learning for recommendation and explanation'. Together they form a unique fingerprint.

Cite this