IEDR: A Context-aware Intrinsic and Extrinsic Disentangled Recommender System

Yixin Su, Wei Jiang, Yunxiang Zhao, Fangquan Lin, Cheng Yang, Sarah Monazam Erfani, Junhao Gan

Keywords: Recommender Systems, Intrinsic and Extrinsic Factors, Contrastive Learning, Disentangled Representation, Mutual Information

TL;DR: We propose a recommender system that capture intrinsic and extrinsic factors from various contexts to enhance the recommendation quality.

Abstract: Intrinsic and extrinsic factors jointly affect users' decisions in item selection (e.g., click, purchase). Intrinsic factors reveal users' real interests and are invariant in different contexts (e.g., time, weather), whereas extrinsic factors can change w.r.t. different contexts. Analyzing these two factors is an essential yet challenging task in recommender systems. However, in existing studies, factor analysis is either largely neglected, or designed for a specific context (e.g., the time context in sequential recommendation), which limits the applicability of such models. In this paper, we propose a generic model, IEDR, to learn intrinsic and extrinsic factors from various contexts for recommendation. IEDR contains two key components: a contrastive learning component, and a disentangling component. The two components collaboratively enable our model to learn context-invariant intrinsic factors and context-based extrinsic factors from all available contexts. Experimental results on real-world datasets demonstrate the effectiveness of our model in factor learning and impart a significant improvement in recommendation accuracy over the state-of-the-art methods.

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