Cd-MBRec: Enhancing multi-behavior recommendation by explicitly modeling commonality and diversity

Abstract

Multi-behavior recommendation models excel in extracting abundant information from user-item interactions to enhance performance; however, they encounter challenges in accuracy due to noise disturbance and ambiguous weight allocation. In this paper, we propose cd-MBRec, a novel model designed to amplify commonality among various behaviors, thereby minimizing noise interference while preserving behavior diversity to highlight semantic variations in feedback across distinct scenarios. Specifically, the model begins by constructing behavior matrices that models separate behaviors, along with an interaction matrix offering a broad overview of user behaviors. It employs graph neural networks to extract higher-order semantic and structural information from input data. Concurrently, the model integrates principles of Weber-Fechner Law for the adaptive allocation of initial weights to the multiple behaviors and utilizes matrix factorization techniques for efficient behavior embedding. Extensive experiments on two real-world datasets demonstrate that cd-MBRec surpasses existing state-of-the-art models in recommendation performance, achieving notable average improvements of 4.96% in HR@10 and 7.75% in NDCG@10.