Behavior-Aware Dual-Channel Preference Learning for Heterogeneous Sequential Recommendation

2026-04-15

AI Summary

This work addresses the challenges in heterogeneous sequential recommendation, where target behaviors are sparse, auxiliary behaviors introduce noise, and existing contrastive learning approaches overlook fine-grained user preferences. To tackle these issues, the authors propose a behavior-aware dual-channel preference learning framework. This framework models personalized behavior transitions through behavior-specific subgraphs, aggregates contextual information via cascaded graph neural networks, and enhances user representations by contrasting long-term and short-term preferences. A learnable gating mechanism adaptively fuses multi-source preferences for next-target-behavior prediction. Extensive experiments on three real-world datasets demonstrate that the proposed method significantly outperforms state-of-the-art baselines, effectively alleviating data sparsity while preserving fine-grained user preferences.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Heterogeneous sequential recommendation (HSR) aims to learn dynamic behavior dependencies from the diverse behaviors of user-item interactions to facilitate precise sequential recommendation. Despite many efforts yielding promising achievements, there are still challenges in modeling heterogeneous behavior data. One significant issue is the inherent sparsity of a real-world data, which can weaken the recommendation performance. Although auxiliary behaviors (e.g., clicks) partially address this problem, they inevitably introduce some noise, and the sparsity of the target behavior (e.g., purchases) remains unresolved. Additionally, contrastive learning-based augmentation in existing methods often focuses on a single behavior type, overlooking fine-grained user preferences and losing valuable information. To address these challenges, we have meticulously designed a behavior-aware dual-channel preference learning framework (BDPL). This framework begins with the construction of customized behavior-aware subgraphs to capture personalized behavior transition relationships, followed by a novel cascade-structured graph neural network to aggregate node context information. We then model and enhance user representations through a preference-level contrastive learning paradigm, considering both long-term and short-term preferences. Finally, we fuse the overall preference information using an adaptive gating mechanism to predict the next item the user will interact with under the target behavior. Extensive experiments on three real-world datasets demonstrate the superiority of our BDPL over the state-of-the-art models.

Problem

• heterogeneous sequential recommendation
• behavior sparsity
• auxiliary behavior noise
• fine-grained user preferences
• contrastive learning

Innovation

• behavior-aware subgraph
• cascade-structured GNN
• preference-level contrastive learning
• adaptive gating mechanism
• heterogeneous sequential recommendation