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Behavior Pattern Mining-based Multi-Behavior Recommendation

Haojie Li, Zhiyong Cheng, Xu Yu, Jinhuan Liu, Guanfeng Liu, Junwei Du

TL;DR

This work addresses data sparsity and complex user--item interactions in multi-behavior recommendations by introducing BPMR, a pattern-centric Bayesian approach. BPMR separately mines behavior patterns across multiple interaction types and then applies a pattern-based Bayesian ranking, avoiding graph neural network over-smoothing. Empirical results on three real-world datasets show substantial gains over state-of-the-art baselines and robustness to sparse target behaviors and noisy auxiliary data. The approach offers a scalable, interpretable alternative to GNN-based methods with practical impact for accurate recommendations in multi-behavior environments.

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.

Behavior Pattern Mining-based Multi-Behavior Recommendation

TL;DR

This work addresses data sparsity and complex user--item interactions in multi-behavior recommendations by introducing BPMR, a pattern-centric Bayesian approach. BPMR separately mines behavior patterns across multiple interaction types and then applies a pattern-based Bayesian ranking, avoiding graph neural network over-smoothing. Empirical results on three real-world datasets show substantial gains over state-of-the-art baselines and robustness to sparse target behaviors and noisy auxiliary data. The approach offers a scalable, interpretable alternative to GNN-based methods with practical impact for accurate recommendations in multi-behavior environments.

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.
Paper Structure (17 sections, 2 equations, 4 figures, 3 tables)

This paper contains 17 sections, 2 equations, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. PRELIMIARIES
  3. METHODOLOGY
  4. Behavior pattern mining
  5. Behavior pattern-based recommendation
  6. Experiments
  7. Experimental Setting
  8. Datasets.
  9. Baselines.
  10. Evaluation Metrics.
  11. Parameter Settings.
  12. Experimental Results and Analysis
  13. Overall Performance.
  14. Performance on sparse target behavior.
  15. Performance on noisy auxiliary behaviors.
  16. ...and 2 more sections

Figures (4)

  • Figure 1: The user-item interaction graph.
  • Figure 2: Different types of behavior patterns between nodes.
  • Figure 3: Performance on sparse target behavior.
  • Figure 4: Performance on noisy auxiliary behaviors.