Multi-behavior Recommendation with Graph Convolutional Networks

By Bowen Jin, Chen Gao, Xiangnan He, Depeng Jin, Yong Li. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020

441 citations recorded by the source corpus.

Traditional recommendation models that usually utilize only one type of user-item interaction are faced with serious data sparsity or cold start issues. Multi-behavior recommendation taking use of multiple types of user-item interactions, such as clicks and favorites, can serve as an effective solution. Early efforts towards multi-behavior recommendation fail to capture behaviors' different influence strength on target behavior. They also ignore behaviors' semantics which is implied in multi-behavior data. Both of these two limitations make the data not fully exploited for improving the recommendation performance on the target behavior. In this work, we approach this problem by innovatively constructing a unified graph to represent multi-behavior data and proposing a new model named MBGCN (short for Multi-Behavior Graph Convolutional Network ). Learning behavior strength by user-item propagation layer and capturing behavior semantics by item-item propagation layer, MBGCN can well address the limitations of existing works. Empirical results on two real-world datasets verify the effectiveness of our model in exploiting multi-behavior data. Our model outperforms the best baseline by 25.02% and 6.51% averagely on two datasets. Further studies on cold-start users confirm the practicability of our proposed model.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/b440396ba1a92513bb32f67c7ede2fa5556b3246

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Xing Xie
3. Xiang Wang
4. Yong Li
5. Fangzhao Wu
6. Chen Gao
7. Chuhan Wu
8. Yongfeng Huang

In Information Systems

1. Xiangnan He
2. Xing Xie
3. Xiang Wang
4. Yong Li
5. Fangzhao Wu
6. Chuhan Wu
7. Chen Gao
8. Yongfeng Huang

Citations in the corpus

• Sequential Recommendation with Graph Neural Networks (2021, methodology): “GCN-based methods have achieved the state-of-the-art performance in other recommendation problems, such as social recommendation [6, 32, 40], knowledge graph-based recommendation [27, 28], multi-behavior recommendation [12], bundle recommendation [3], etc.”
• Graph Neural Networks in Recommender Systems: A Survey (2020, neutral): “h∗ u = h (0) u ⊕ h(1) u ⊕ · · · ⊕ h(L) u SpectralCF [205], NGCF [155], IC-MC [198], LR-GCCF [18], MBGCN [68]”
• Graph Neural Networks for Recommender System (2022, background): “The scenario consists of social [24], sequential [4], session-based [5], cross-domain [10], multi-behavior [15], bundle recommendation [3], etc.”
• A federated graph neural network framework for privacy-preserving personalization (2021, background): “Graph neural network (GNN) is widely used by many personalized recommendation methods in recent years [12, 31, 36], since it can capture high-order interactions between users and items on the user-item graph to enhance the user and item representations [2, 37, 38].”
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, neutral): “[178], [179], and GNN-based approaches [97], [128], [141], [170], [180], [181], [182], [183], [184], [185], [186], [187].”
• A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (2021, methodology): “[78] assign different learnable weights to different edges to model the importance of the behaviors.”