Category attentive graph neural networks for session-based recommendation

Wenjie Qin; Xian Fu; Xiao Yang; Yating Wang; Huimin Que; Shuxian Qi

doi:10.1117/12.2634400

4 May 2022 Category attentive graph neural networks for session-based recommendation

Wenjie Qin, Xian Fu, Xiao Yang, Yating Wang, Huimin Que, Shuxian Qi

Proceedings Volume 12172, International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021); 1217203 (2022) https://doi.org/10.1117/12.2634400
Event: International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021), 2021, Nanchang, China

Abstract

Session-based recommendation is a challenging field in the research network-based behavior modeling, mainly due to the complex transfer of user interests between items and the limited information. The previous methods model the session as a sequence or a graph, which takes into account the role of time attention in session-based recommendation and achieve satisfactory performances, they still ignore the latent relationship between user interest transfer and item category. In this study, we propose a category attentive graph neural network (CAGNN) model for session-based recommendation. According to the item category, the category attention is embedded into the latent vector of the items to adaptively capture different user interests, which effectively improves the expressiveness and performance of the model. The numerical results of two real datasets show that CAGNN outperforms the state-of-the-art session-based recommendation methods.

Citation Download Citation

Wenjie Qin, Xian Fu, Xiao Yang, Yating Wang, Huimin Que, and Shuxian Qi "Category attentive graph neural networks for session-based recommendation", Proc. SPIE 12172, International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021), 1217203 (4 May 2022); https://doi.org/10.1117/12.2634400

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KEYWORDS

Neural networks

Performance modeling

Data modeling

Image processing

Systems modeling

Visual process modeling

Engineering education

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