Abstract
Sequential recommendation is an important task in the research of recommendation methods for heterogeneous information networks, which is a technique of predicting next user behavior according to completed behavior features. Traditional recommendation methods for heterogeneous information networks only consider a kind of user behavior feature, ignoring the heterogeneity of user behavior in heterogeneous information networks and the correlation relationship among user behavior. In this paper, we propose a sequential recommendation method based on multi-behavior features fusion (MBFF) for heterogeneous information networks, which makes full use of the heterogeneity and temporal features of user behavior to fuse multi-behavior features and effectively ensure the effectiveness of sequential recommendation. We propose an embedding fusion method of graph convolutional networks (GCN) to achieve the fusion of different behavior features. Moreover, A multi-head attention embedding fusion method based on dynamic heterogeneous networks is proposed to realize the fusion of user embedding and behavior embedding. Our method has experimented on real datasets, and the feasibility and effectiveness of our proposed method are demonstrated via experiments.
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Acknowledgment
This work was supported by the Science and Technology Program Major Project of Liaoning Province of China under Grant No. 2022JH1/10400009, the Natural Science Foundation of Liaoning Province of China under Grant No. 2022-MS-171, 2020-BS-082, the Science Research Fund of Liaoning Province of China under Grant No. LJKZ0094, LJKQZ2021023.
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Li, D., Wang, Y., Chen, T., Sun, X., Wang, K., Wu, G. (2023). A GCN-Based Sequential Recommendation Model for Heterogeneous Information Networks. In: Yang, S., Islam, S. (eds) Web and Big Data. APWeb-WAIM 2022 International Workshops. APWeb-WAIM 2022. Communications in Computer and Information Science, vol 1784. Springer, Singapore. https://doi.org/10.1007/978-981-99-1354-1_6
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DOI: https://doi.org/10.1007/978-981-99-1354-1_6
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