Abstract
Preference prediction is the building block of personalized services, and its implementation at the group level helps enterprises identify their target customers effectively. Existing methods for preference prediction mainly focus on behavioral interactions to extract the associations between groups and products, ignoring the importance of other auxiliary records (e.g., online reviews and social tags) in association detection. This paper proposes a novel method named GMAT for group preference prediction, aiming to collectively detect the sophisticated association patterns from user generated content (UGC) and behavioral interactions. In doing so, we construct a tripartite graph to collaborate these two types of data, and design a deep-learning algorithm with mutual attention module for generating the contextualized representations of groups and products. Extensive experiments on two real-world datasets show that GMAT is superior to other baselines in terms of group preference prediction. Additionally, GMAT is able to improve prediction accuracy compared with its different variants, further verifying the proposed method’s effectiveness on association pattern detection.
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Data Availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
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Acknowledgements
This work was partly supported by National Natural Science Foundation of China (72293561) and Research Center for Interactive Technology Industry of Tsinghua University (RCITI2022T002).
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Xiangyu Li received his Ph.D. in Management Science and Engineering from Tsinghua University in 2023. He is currently an assistant professor at International Business School, Jinan University, Guangdong, China. His research interests focus on electronic commerce, recommender system, and business intelligence.
Xunhua Guo is a professor of Information Systems at the School of Economics and Management, Tsinghua University, Beijing, China. Dr. Guo received his Ph.D. in management science and engineering from Tsinghua University in 2005. His research interests focus on electronic commerce, social networks, and business intelligence.
Guoqing Chen received his Ph.D. from the Catholic University of Leuven, Belgium, in 1992. He is currently CCB Chair Professor at the School of Economics and Management, Tsinghua University, Beijing, China. His research interests include information systems, business Intelligence and analytics, e-Business and fuzzy logic.
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Li, X., Guo, X. & Chen, G. GMAT: A Graph Modeling Method for Group Preference Prediction. J. Syst. Sci. Syst. Eng. (2024). https://doi.org/10.1007/s11518-024-5594-z
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DOI: https://doi.org/10.1007/s11518-024-5594-z