Combinatorial Optimization Perspective based Framework for Multi-behavior Recommendation

2025-02-04

AI Summary

To address the limitations of single-perspective behavior fusion and severe negative transfer across tasks in multi-behavior recommendation, this paper proposes a novel framework grounded in combinatorial optimization. It formalizes multi-behavior fusion as a stage-constrained combinatorial optimization problem and designs a Stage-Constrained Optimized Graph Convolutional Network (COGCN) to precisely model behavioral temporal dynamics and inter-behavior dependencies. Additionally, a Distribution-Aware Multi-Expert module (DFME) is introduced to decouple task-specific gradients via forward/backward propagation, thereby mitigating negative transfer. The proposed approach transcends conventional GNN-based multi-task paradigms, achieving significant improvements over state-of-the-art methods on three real-world datasets. Ablation studies validate the effectiveness of both COGCN and DFME. The source code is publicly available.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

In real-world recommendation scenarios, users engage with items through various types of behaviors. Leveraging diversified user behavior information for learning can enhance the recommendation of target behaviors (e.g., buy), as demonstrated by recent multi-behavior methods. The mainstream multi-behavior recommendation framework consists of two steps: fusion and prediction. Recent approaches utilize graph neural networks for multi-behavior fusion and employ multi-task learning paradigms for joint optimization in the prediction step, achieving significant success. However, these methods have limited perspectives on multi-behavior fusion, which leads to inaccurate capture of user behavior patterns in the fusion step. Moreover, when using multi-task learning for prediction, the relationship between the target task and auxiliary tasks is not sufficiently coordinated, resulting in negative information transfer. To address these problems, we propose a novel multi-behavior recommendation framework based on the combinatorial optimization perspective, named COPF. Specifically, we treat multi-behavior fusion as a combinatorial optimization problem, imposing different constraints at various stages of each behavior to restrict the solution space, thus significantly enhancing fusion efficiency (COGCN). In the prediction step, we improve both forward and backward propagation during the generation and aggregation of multiple experts to mitigate negative transfer caused by differences in both feature and label distributions (DFME). Comprehensive experiments on three real-world datasets indicate the superiority of COPF. Further analyses also validate the effectiveness of the COGCN and DFME modules. Our code is available at https://github.com/1918190/COPF.

Problem

• Improves multi-behavior fusion efficiency
• Mitigates negative information transfer
• Enhances recommendation accuracy

Innovation

• Graph neural networks for fusion
• Combinatorial optimization enhances efficiency
• Multi-task learning mitigates negative transfer