Temporal Conformity-aware Hawkes Graph Network for Recommendations

Chenglong Ma, Yongli Ren, Pablo Castells, Mark Sanderson

Keywords: Interest, Conformity, Temporal Graph Attention Network, Recommendations

TL;DR: The paper introduces the Temporal Conformity-aware Hawkes Network, which disentangles user self-interest and conformity behavior, addressing conformity bias in user interaction behavior and improving recommendation accuracy, diversity, and fairness.

Abstract:

Many existing recommender systems (RSs) assume user behavior is governed solely by their interests. However, the peer effect often influences individual decision-making, which leads to conformity behavior.
Conventional solutions that eliminate indiscriminately such bias may cause RSs to neglect valuable information and depersonalize the recommendation results.
Also, conformity can transform into user interest, e.g., discovering new tastes after a glance at popular music.
By better representing different forms of conformity influence, we can do a better job at interest mining and debiasing.
In certain extreme circumstances, the herd effect may be exacerbated by user anxiety with uncertainty (e.g., panic buying during the COVID-19 pandemic). RSs may thus fail to respond in time due to sudden and dramatic changes.
Moreover, many existing studies potentially conflate conformity bias with popularity bias and lump together various factors responsible for differences in popularity.
In this paper, we identify two distinct types of conformity behavior: informational conformity and normative conformity. To address this, we introduce the TCHN model, which utilizes attentional Hawkes processes to disentangle user self-interest and conformity in a personalized manner. Our approach incorporates sequence graph attention networks to capture users' stable and volatile dynamics. We conduct experiments on three real-world datasets, which uncover diverse levels of conformity among users. The results show that TCHN excels in recommendation accuracy, diversity, and fairness across various user groups.

Track: User Modeling and Recommendation

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Submission Number: 258