IEDR: A CONTEXT-AWARE INTRINSIC AND EXTRIN-SIC DISENTANGLED RECOMMENDER SYSTEM

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.

1. INTRODUCTION

Recommender systems aim to predict the probability of a user selecting a given item (e.g., click, purchase). This is a challenging prediction as each decision is jointly affected by multiple factors (Ma et al., 2019) . Psychological research has revealed that users' decision making is mainly influenced by two factors: intrinsic and extrinsic factors (Bénabou & Tirole, 2003; Vallerand, 1997) . An intrinsic factor is an internal motivation for inherent satisfaction, which is often stable for an individual. In contrast, an extrinsic factor is a contextual motivation triggered by the environment (external stimulation), and it often varies among different contexts (e.g., weather, time) (Ryan & Deci, 2000) . For example, on a day with heavy rain, a user decides to take an Uber (a taxi calling app) to work. In this case, the choice of Uber over other taxi calling apps is because the user is more comfortable with this app's user interface (intrinsic factor), while the choice of taking a ride to work is motivated by the weather condition (extrinsic factor). Although the importance of capturing these factors in recommender systems has been recognized, their full potential has not been explored by the existing works. (1) Some studies neglect the intrinsic and extrinsic factor disentangling, and the final prediction mainly relies on learning entangled representations (Barkan & Koenigstein, 2016; Covington et al., 2016; Wu et al., 2019) . With the intrinsic and extrinsic factors entangled behind each decision, the real factors that derive the decision may be incorrectly inferred, resulting in a suboptimal recommendation Wang et al. (2020) . ( 2) Some studies learn intrinsic and extrinsic factors, but just under a specific context. For example, some sequential recommendation models leverage the time context (order sequence) to learn intrinsic and extrinsic factors (they call them long-and short-term interests) (Hidasi et al., 2016; Yu et al., 2019b) ; some point-of-interest recommendation models leverage the spatial context (geometric distance) to learn the two factors (Li et al., 2017; Wu et al., 2020) . In such models, the factor learning approaches are domain-specific, so it would be difficult to generalize them to other contexts. Meanwhile, the factors may be influenced by multiple contexts. Hence, focusing on a single context may result in inferior factor learning. Therefore, it is still an open question of how to effectively incorporate various context information for learning intrinsic and extrinsic factors in recommender systems. 1

