| Title | Composition-Enhanced Graph Collaborative Filtering for Multi-behavior Recommendation |
| Authors | Wu, Daqing Luo, Xiao Ma, Zeyu Chen, Chong Wang, Pengfei Deng, Minghua Ma, Jinwen |
| Affiliation | Peking Univ, Sch Math Sci, Beijing, Peoples R China Damo Acad, Alibaba Grp, Hangzhou, Peoples R China Harbin Inst Technol, Shenzhen Grad Sch, Sch Comp Sci & Technol, Shenzhen, Peoples R China |
| Issue Date | 2021 |
| Publisher | 2021 21ST IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2021) |
| Abstract | Rapid and accurate prediction of user preferences is the ultimate goal of today's recommender systems. More and more researchers pay attention to multi-behavior recommender systems which utilize the auxiliary types of user-item interaction data, such as page view and add-to-cart to help estimate user preferences. Recently, graph-based methods were proposed to showcase an advanced capability in representation learning and capturing collaborative signals. However, we argue that these methods ignore the intrinsic difference between the two types of nodes in the bipartite graph and aggregate information from neighboring nodes with the same functions. Besides, these models do not fully explore the collaborative signals implied by the metapath across different types of behavior, which causes a huge loss of the potential semantic information across behaviors. To address the above limitations, we present a unified graph model named SaGCN (short for Semantic-aware Graph Convolutional Networks). Specifically, we construct separate user-user and itemitem graphs by meta-path, and apply separate aggregation and transformation functions to propagate user and item information. To perform better semantic propagation, we design a relation composition function and a semantic propagation architecture for heterogeneous collaborative filtering signals learning. Extensive experiments on two real-world datasets show that SaGCN outperforms a wide range of state-of-the-art methods in multibehavior scenarios. |
| URI | http://hdl.handle.net/20.500.11897/642159 |
| ISBN | 978-1-6654-2398-4 |
| ISSN | 1550-4786 |
| DOI | 10.1109/ICDM51629.2021.00183 |
| Indexed | EI CPCI-S(ISTP) |
| Appears in Collections: | 数学科学学院 |
