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Many previous studies aim to augment collaborative filtering with deep neuralnetwork techniques, so as to achieve better recommendation performance.However, most existing deep learning-based recommender systems are designed formodeling singular type of user-item interaction behavior, which can hardlydistill the heterogeneous relations between user and item. In practicalrecommendation scenarios, there exist multityped user behaviors, such as browseand purchase. Due to the overlook of user's multi-behavioral patterns overdifferent items, existing recommendation methods are insufficient to captureheterogeneous collaborative signals from user multi-behavior data. Inspired bythe strength of graph neural networks for structured data modeling, this workproposes a Graph Neural Multi-Behavior Enhanced Recommendation (GNMR) frameworkwhich explicitly models the dependencies between different types of user-iteminteractions under a graph-based message passing architecture. GNMR devises arelation aggregation network to model interaction heterogeneity, andrecursively performs embedding propagation between neighboring nodes over theuser-item interaction graph. Experiments on real-world recommendation datasetsshow that our GNMR consistently outperforms state-of-the-art methods. Thesource code is available at https://github.com/akaxlh/GNMR.

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