A Survey on User Behavior Modeling in Recommender Systems

TL;DR

This survey comprehensively reviews User Behavior Modeling in recommender systems, categorizing existing research into four main directions and discussing their applications, strengths, and future prospects.

Contribution

It provides a systematic taxonomy of UBM research, covering conventional, long-sequence, multi-type, and side information-based models, with insights into industrial practices.

Findings

Categorized UBM research into four main directions.

Analyzed strengths and weaknesses of representative models.

Discussed industrial applications and future research directions.

Abstract

User Behavior Modeling (UBM) plays a critical role in user interest learning, which has been extensively used in recommender systems. Crucial interactive patterns between users and items have been exploited, which brings compelling improvements in many recommendation tasks. In this paper, we attempt to provide a thorough survey of this research topic. We start by reviewing the research background of UBM. Then, we provide a systematic taxonomy of existing UBM research works, which can be categorized into four different directions including Conventional UBM, Long-Sequence UBM, Multi-Type UBM, and UBM with Side Information. Within each direction, representative models and their strengths and weaknesses are comprehensively discussed. Besides, we elaborate on the industrial practices of UBM methods with the hope of providing insights into the application value of existing UBM solutions. Finally, we summarize the survey and discuss the future prospects of this field.