Graph Meta Network for Multi-Behavior Recommendation

Abstract

Modern recommender systems often embed users and items into low-dimensional latent representations, based on their observed interactions. In practical recommendation scenarios, users often exhibit various intents which drive them to interact with items with multiple behavior types (e.g., click, tag-as-favorite, purchase). However, the diversity of user behaviors is ignored in most of the existing approaches, which makes them difficult to capture heterogeneous relational structures across different types of interactive behaviors. Exploring multi-typed behavior patterns is of great importance to recommendation systems, yet is very challenging because of two aspects: i) The complex dependencies across different types of user-item interactions; ii) Diversity of such multi-behavior patterns may vary by users due to their personalized preference. To tackle the above challenges, we propose a Multi-Behavior recommendation framework with Graph Meta Network to incorporate the multi-behavior pattern modeling into a meta-learning paradigm. Our developed MB-GMN empowers the user-item interaction learning with the capability of uncovering type-dependent behavior representations, which automatically distills the behavior heterogeneity and interaction diversity for recommendations. Extensive experiments on three real-world datasets show the effectiveness of MB-GMN by significantly boosting the recommendation performance as compared to various state-of-the-art baselines. The source code is available athttps://github.com/akaxlh/MB-GMN.

Published as a full paper at SIGIR 2021

References in corpus (4)

• KGAT: Knowledge Graph Attention Network for Recommendation
• Representation Learning for Attributed Multiplex Heterogeneous Network
• Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network
• Recent Advances in Heterogeneous Relation Learning for Recommendation

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• Knowledge Enhancement for Contrastive Multi-Behavior Recommendation
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• Graph Transformer for Recommendation
• Multi-behavior Self-supervised Learning for Recommendation
• Multi-Behavior Sequential Recommendation with Temporal Graph Transformer
• Multi-Behavior Graph Neural Networks for Recommender System
• SSLRec: A Self-Supervised Learning Framework for Recommendation
• Parallel Knowledge Enhancement based Framework for Multi-behavior Recommendation
• Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment
• Understanding and Modeling Passive-Negative Feedback for Short-video Sequential Recommendation
• Recommending on graphs: a comprehensive review from a data perspective
• Collaborative Reflection-Augmented Autoencoder Network for Recommender Systems
• Session-Based Recommendation by Exploiting Substitutable and Complementary Relationships from Multi-behavior Data
• HMAR: Hierarchical Masked Attention for Multi-Behaviour Recommendation
• Deep Situation-Aware Interaction Network for Click-Through Rate Prediction
• Hierarchical Graph Information Bottleneck for Multi-Behavior Recommendation
• Tricolore: Multi-Behavior User Profiling for Enhanced Candidate Generation in Recommender Systems
• Polynomial Selection in Spectral Graph Neural Networks: An Error-Sum of Function Slices Approach
• Partition-wise Graph Filtering: A Unified Perspective Through the Lens of Graph Coarsening