Behavior Pattern Mining-based Multi-Behavior Recommendation

TL;DR

This paper introduces BPMR, a novel multi-behavior recommendation algorithm that explores user-item interaction patterns using a Bayesian approach, outperforming existing methods significantly on real-world datasets.

Contribution

BPMR is a new algorithm that leverages behavior pattern mining and Bayesian methods to improve multi-behavior recommendation accuracy, addressing limitations of graph neural network approaches.

Findings

BPMR outperforms state-of-the-art algorithms by over 268% in Recall@10.

BPMR achieves approximately 248% improvement in NDCG@10.

Experimental results on three real-world datasets validate BPMR's effectiveness.

Abstract

Multi-behavior recommendation systems enhance effectiveness by leveraging auxiliary behaviors (such as page views and favorites) to address the limitations of traditional models that depend solely on sparse target behaviors like purchases. Existing approaches to multi-behavior recommendations typically follow one of two strategies: some derive initial node representations from individual behavior subgraphs before integrating them for a comprehensive profile, while others interpret multi-behavior data as a heterogeneous graph, applying graph neural networks to achieve a unified node representation. However, these methods do not adequately explore the intricate patterns of behavior among users and items. To bridge this gap, we introduce a novel algorithm called Behavior Pattern mining-based Multi-behavior Recommendation (BPMR). Our method extensively investigates the diverse interaction patterns between users and items, utilizing these patterns as features for making recommendations. We employ a Bayesian approach to streamline the recommendation process, effectively circumventing the challenges posed by graph neural network algorithms, such as the inability to accurately capture user preferences due to over-smoothing. Our experimental evaluation on three real-world datasets demonstrates that BPMR significantly outperforms existing state-of-the-art algorithms, showing an average improvement of 268.29% in Recall@10 and 248.02% in NDCG@10 metrics. The code of our BPMR is openly accessible for use and further research at https://github.com/rookitkitlee/BPMR.