Location-aware neural graph collaborative filtering

Abstract

Collaborative filtering (CF) is initiated by representing users and items as vectors and seeks to describe the relationship between users and items at a profound level, thus predicting users’ preferred behavior. To address the issue that previous research ignored higher-order geographical interactions hidden in users’ historical behaviors, this paper proposes a location-aware neural graph collaborative filtering model (LA-NGCF), which incorporates location information of items for improving prediction performance. The model characterizes the interactions between items based on spatial decay law from a graph perspective and designs two strategies to capture the interaction effects of users and items considering node heterogeneity. An optimized loss function with spatial distances of items is also developed in the model. Extensive experiments are conducted on three publicly available real-world datasets to examine the effectiveness of our model. Results show that LA-NGCF achieves competitive performances compared with several state-of-the-art models, which suggests that location information of items is beneficial for improving the performance of personalized recommendations. This paper offers an approach to incorporate weighted interactions between items into CF algorithms and enriches the methods of utilizing geographical information for artificial intelligence applications.

Keywords:

• Collaborative filtering
• neural graph collaborative filtering
• geographical location

Author contributions

Shengwen Li: conceptual, writing, and revision. Chenpeng Sun: technical implementation and writing. Xinchuan Li: validation of the approach and interpretation of the results. Qinzhong Liang: validation of the approach and revision. Renyao Chen: technical implementation and revision. Junfang Gong: interpretation of the results and revision. Hong Yao: conceptual, supervision, and revision.

Acknowledgments

We sincerely acknowledge Dr. May Yuan, Dr. Stephen Hirtle, and the anonymous referees for their insightful comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data and codes availability statement

The data and code that support the findings of this study are available in Figshare with the identifier(s) at the link: https://doi.org/10.6084/m9.figshare.12826514.

Additional information

Funding

This research was supported by the National Natural Science Foundation of China, grant numbers 42071382, 61972365, and 41801378.

Notes on contributors

Shengwen Li

Shengwen Li received a B.S. degree in computer science and a Ph.D. degree in cartography and geographic information engineering from China University of Geosciences, Wuhan, China, in 2000 and 2010, respectively. He is currently an associate professor in computer science school, China University of Geosciences (Wuhan). His main interests include spatial statistics, geospatial artificial intelligence, and natural language processing.

Chenpeng Sun

Chenpeng Sun received a B.S. degree in industrial design in 2018 and an M.A. Eng. degree in computer science from the China University of Geosciences (Wuhan) in 2021. He is currently a deep learning algorithm engineer. His main interests include deep learning and GIS applications.

Renyao Chen

Renyao Chen is a Ph.D. student in the School of Computer Science at the China University of Geosciences, Wuhan, China. His research focuses on knowledge graph, natural language processing, and geographic artificial intelligence.

Xinchuan Li

Xinchuan Li is an associate professor in the School of Computer Science at the China University of Geosciences, Wuhan, China. His research interests include issues related to the knowledge graph, natural language processing, and GIS applications.

Qingzhong Liang

Qingzhong Liang is an associate professor in the School of Computer Science at the China University of Geosciences, Wuhan, China. His research interests include issues related to the knowledge graph, natural language processing, and computer vision.

Junfang Gong

Junfang Gong is an associate professor at the China University of Geosciences (Wuhan). She received her Ph.D. degree from the China University of Geosciences (Wuhan). Her research interest is spatial data analysis.

Hong Yao

Hong Yao received a B.S degree in Computer and Applications from Wuhan Technical University of Surveying and Mapping, China in 1998, and a Ph.D. degree in Computer Science and Technology from Huazhong University of Science and Technology, China in 2010. He is currently a professor at the School of Computer Science, China University of Geosciences, China. His research interests include knowledge graphs and artificial intelligence applications. He is a member of the IEEE.