Latent Factor Modeling with Expert Network for Multi-Behavior Recommendation

TL;DR

This paper introduces a multi-behavior recommendation model using an expert network that dynamically selects latent factors for better user preference representation, addressing data sparsity and behavioral entanglement issues.

Contribution

The proposed MBLFE method employs a gating expert network with self-supervised learning to improve multi-behavior recommendation accuracy and interpretability.

Findings

Outperforms state-of-the-art baselines on three datasets

Effectively captures user preferences across multiple behaviors

Enhances recommendation accuracy with expert network design

Abstract

Traditional recommendation methods, which typically focus on modeling a single user behavior (e.g., purchase), often face severe data sparsity issues. Multi-behavior recommendation methods offer a promising solution by leveraging user data from diverse behaviors. However, most existing approaches entangle multiple behavioral factors, learning holistic but imprecise representations that fail to capture specific user intents. To address this issue, we propose a multi-behavior method by modeling latent factors with an expert network (MBLFE). In our approach, we design a gating expert network, where the expert network models all latent factors within the entire recommendation scenario, with each expert specializing in a specific latent factor. The gating network dynamically selects the optimal combination of experts for each user, enabling a more accurate representation of user preferences. To ensure independence among experts and factor consistency of a particular expert, we incorporate self-supervised learning during the training process. Furthermore, we enrich embeddings with multi-behavior data to provide the expert network with more comprehensive collaborative information for factor extraction. Extensive experiments on three real-world datasets demonstrate that our method significantly outperforms state-of-the-art baselines, validating its effectiveness.