Multi-Behavior Recommender Systems: A Survey
It addresses the need for more accurate recommendations in real-world scenarios where users exhibit multiple behaviors, but it is incremental as it synthesizes existing research rather than introducing new methods.
This survey reviews multi-behavior recommender systems, which tackle the problem of leveraging diverse user interactions like clicks and cart additions to improve recommendation quality, by categorizing existing methods based on data modeling, encoding, and training steps.
Traditional recommender systems primarily rely on a single type of user-item interaction, such as item purchases or ratings, to predict user preferences. However, in real-world scenarios, users engage in a variety of behaviors, such as clicking on items or adding them to carts, offering richer insights into their interests. Multi-behavior recommender systems leverage these diverse interactions to enhance recommendation quality, and research on this topic has grown rapidly in recent years. This survey provides a timely review of multi-behavior recommender systems, focusing on three key steps: (1) Data Modeling: representing multi-behaviors at the input level, (2) Encoding: transforming these inputs into vector representations (i.e., embeddings), and (3) Training: optimizing machine-learning models. We systematically categorize existing multi-behavior recommender systems based on the commonalities and differences in their approaches across the above steps. Additionally, we discuss promising future directions for advancing multi-behavior recommender systems.