Multi-Behavior Enhanced Recommendation with Cross-Interaction Collaborative Relation Modeling

• URL: http://arxiv.org/abs/2201.02307v1
• Date: Fri, 7 Jan 2022 03:12:37 GMT
• Title: Multi-Behavior Enhanced Recommendation with Cross-Interaction Collaborative Relation Modeling
• Authors: Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Mengyin Lu, Liefeng Bo
• Abstract summary: This work proposes a Graph Neural Multi-Behavior Enhanced Recommendation framework. It explicitly models the dependencies between different types of user-item interactions under a graph-based message passing architecture. Experiments on real-world recommendation datasets show that our GNMR consistently outperforms state-of-the-art methods.
• Score: 42.6279077675585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many previous studies aim to augment collaborative filtering with deep neural network techniques, so as to achieve better recommendation performance. However, most existing deep learning-based recommender systems are designed for modeling singular type of user-item interaction behavior, which can hardly distill the heterogeneous relations between user and item. In practical recommendation scenarios, there exist multityped user behaviors, such as browse and purchase. Due to the overlook of user's multi-behavioral patterns over different items, existing recommendation methods are insufficient to capture heterogeneous collaborative signals from user multi-behavior data. Inspired by the strength of graph neural networks for structured data modeling, this work proposes a Graph Neural Multi-Behavior Enhanced Recommendation (GNMR) framework which explicitly models the dependencies between different types of user-item interactions under a graph-based message passing architecture. GNMR devises a relation aggregation network to model interaction heterogeneity, and recursively performs embedding propagation between neighboring nodes over the user-item interaction graph. Experiments on real-world recommendation datasets show that our GNMR consistently outperforms state-of-the-art methods. The source code is available at https://github.com/akaxlh/GNMR.

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