Abstract
Sequential Recommendation (SR) can predict possible future behaviors by considering the user’s behavioral sequence. However, users’ preferences constantly change in practice and are difficult to track. The existing methods only consider neighbouring items and neglect the impact of non-adjacent items on user choices. Therefore, how to build an accurate recommendation model is a complex challenge. We propose a novel Graph Neural Network (GNN) based model, Graph-based Dynamic Preference Modeling for Personalized Recommendation (DPPR). In DPPR, the graph attention network (GAT) learns the features of long-term preference. The short-term graph computes items’ dependencies on link propagation between items and attributes. It adjusts node features under the user’s views. The module emphasizes skip features among entity nodes and incorporates time intervals of items to calculate the impact of non-adjacent items. Finally, we combine their representations to generate user preferences and aid decisions. The experimental results indicate that our model outperforms state-of-the-art methods on three public datasets.
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Acknowledgement
This work was supported in part by the “14th Five-Year Plan” Civil Aerospace Pre-Research Project of China under Grant D020101, the Natural Science Foundation of China under Grant No. 62302213, the Natural Science Foundation of Jiangsu Province under Grant No. BK20210280, Project of Hebei Key Laboratory of Software Engineering, No. 22567637H, and the Fundamental Research Funds for the Central Universities under Grant NS2022089.
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Wu, J., Xu, Y., Zhang, B., Xu, Z., Li, B. (2024). Graph-based Dynamic Preference Modeling for Personalized Recommendation. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14647. Springer, Singapore. https://doi.org/10.1007/978-981-97-2259-4_27
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DOI: https://doi.org/10.1007/978-981-97-2259-4_27
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