Towards Multi-Behavior Multi-Task Recommendation via Behavior-informed Graph Embedding Learning

2026-01-12

AI Summary

Existing cascade graph paradigms in multi-behavior recommendation overly emphasize the target behavior (e.g., purchase) while neglecting the modeling of auxiliary behaviors (e.g., click, favorite), thus failing to meet the demands of multi-task personalization. To address this limitation, this work proposes BiGEL, a behavior-aware graph embedding learning framework that establishes the first unified architecture for multi-behavior, multi-task recommendation. BiGEL models behavioral dependencies through a cascaded graph neural network and incorporates a cascaded gating feedback mechanism to mitigate preference drift, enhances global contextual information to capture high-order interactions, and employs contrastive preference alignment to improve multi-task consistency. Evaluated on two real-world datasets, BiGEL significantly outperforms ten strong baselines, achieving notable improvements in both target and auxiliary behavior recommendation performance.

Technology Category

• Machine Learning: Multimodal Learning
• Game Theory and Economic Paradigms: Behavioral Game Theory
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Multi-behavior recommendation (MBR) aims to improve the performance w.r.t. the target behavior (i.e., purchase) by leveraging auxiliary behaviors (e.g., click, favourite). However, in real-world scenarios, a recommendation method often needs to process different types of behaviors and generate personalized lists for each task (i.e., each behavior type). Such a new recommendation problem is referred to as multi-behavior multi-task recommendation (MMR). So far, the most powerful MBR methods usually model multi-behavior interactions using a cascading graph paradigm. Although significant progress has been made in optimizing the performance of the target behavior, it often neglects the performance of auxiliary behaviors. To compensate for the deficiencies of the cascading paradigm, we propose a novel solution for MMR, i.e., behavior-informed graph embedding learning (BiGEL). Specifically, we first obtain a set of behavior-aware embeddings by using a cascading graph paradigm. Subsequently, we introduce three key modules to improve the performance of the model. The cascading gated feedback (CGF) module enables a feedback-driven optimization process by integrating feedback from the target behavior to refine the auxiliary behaviors preferences. The global context enhancement (GCE) module integrates the global context to maintain the user's overall preferences, preventing the loss of key preferences due to individual behavior graph modeling. Finally, the contrastive preference alignment (CPA) module addresses the potential changes in user preferences during the cascading process by aligning the preferences of the target behaviors with the global preferences through contrastive learning. Extensive experiments on two real-world datasets demonstrate the effectiveness of our BiGEL compared with ten very competitive methods.

Problem

• multi-behavior recommendation
• multi-task recommendation
• auxiliary behaviors
• target behavior
• personalized recommendation

Innovation

• multi-behavior recommendation
• multi-task learning
• graph embedding
• contrastive learning
• preference alignment