A Self-Supervised Mixture-of-Experts Framework for Multi-behavior Recommendation

TL;DR

This paper introduces MEMBER, a self-supervised mixture-of-experts framework for multi-behavior recommendation that effectively addresses the challenge of recommending both visited and unvisited items, significantly improving performance.

Contribution

It proposes a novel mixture-of-experts model with self-supervised training for each expert, enhancing multi-behavior recommendation accuracy across different item types.

Findings

Achieves up to 65.46% performance gain over competitors.

Effectively recommends both visited and unvisited items.

Addresses the gap in recommendation quality between item types.

Abstract

In e-commerce, where users face a vast array of possible item choices, recommender systems are vital for helping them discover suitable items they might otherwise overlook. While many recommender systems primarily rely on a user's purchase history, recent multi-behavior recommender systems incorporate various auxiliary user behaviors, such as item clicks and cart additions, to enhance recommendations. Despite their overall performance gains, their effectiveness varies considerably between visited items (i.e., those a user has interacted with through auxiliary behaviors) and unvisited items (i.e., those with which the user has had no such interactions). Specifically, our analysis reveals that (1) existing multi-behavior recommender systems exhibit a significant gap in recommendation quality between the two item types (visited and unvisited items) and (2) achieving strong performance on both types with a single model architecture remains challenging. To tackle these issues, we propose a novel multi-behavior recommender system, MEMBER. It employs a mixture-of-experts framework, with experts designed to recommend the two item types, respectively. Each expert is trained using a self-supervised method specialized for its design goal. In our comprehensive experiments, we show the effectiveness of MEMBER across both item types, achieving up to 65.46% performance gain over the best competitor in terms of Hit Ratio@20.