research-article

Social Recommendation with Cross-Domain Transferable Knowledge

Authors:

Shiqiang YangAuthors Info & Claims

IEEE Transactions on Knowledge and Data Engineering, Volume 27, Issue 11

Pages 3084 - 3097

https://doi.org/10.1109/TKDE.2015.2432811

Published: 01 November 2015 Publication History

Abstract

Recommender systems can suffer from data sparsity and cold start issues. However, social networks, which enable users to build relationships and create different types of items, present an unprecedented opportunity to alleviate these issues. In this paper, we represent a social network as a star-structured hybrid graph centered on a social domain, which connects with other item domains. With this innovative representation, useful knowledge from an auxiliary domain can be transferred through the social domain to a target domain. Various factors of item transferability, including popularity and behavioral consistency, are determined. We propose a novel Hybrid Random Walk (HRW) method, which incorporates such factors, to select transferable items in auxiliary domains, bridge cross-domain knowledge with the social domain, and accurately predict user-item links in a target domain. Extensive experiments on a real social dataset demonstrate that HRW significantly outperforms existing approaches.

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Index Terms

Social Recommendation with Cross-Domain Transferable Knowledge
1. Information systems

Index terms have been assigned to the content through auto-classification.

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Published: 01 November 2015

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https://dl.acm.org/doi/10.1109/TKDE.2023.3273782
Fan WZhao XLi QDerr TMa YLiu HWang JTang J(2023)Adversarial Attacks for Black-Box Recommender Systems via Copying Transferable Cross-Domain User ProfilesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.327265235:12(12415-12429)Online publication date: 1-Dec-2023
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