Abstract
Considering recommendation scenarios in that user profiles are anonymous, the session-based recommendation is proposed to predict the items users are interested in from short sessions. However, most existing methods for session-based recommendation are insufficient to obtain diverse user interests. Meanwhile, they are susceptible to the negative impact of the cold start. To solve these problems, we propose a novel attentive capsule graph neural network for session-based recommendation (ACGNN) to mine more profound user preferences and minimize the impact of the cold start on the recommendation. In ACGNN, we model historical session sequences as graph-structured data and leverage graph neural networks to learn low-level item embedding to represent each session, which can capture complex transitions of items. Various low-level item embeddings are then aggregated into high-level item embeddings by an attentive capsule network, which significantly improves the expressiveness of the model. It can also enrich the features of cold-start users and items, which were difficult to be revealed by previous methods. Our experiments on two real-world datasets consistently show the superior performance of ACGNN over state-of-the-art methods.
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Chen, Y., Tang, Y. (2022). Attentive Capsule Graph Neural Networks for Session-Based Recommendation. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13368. Springer, Cham. https://doi.org/10.1007/978-3-031-10983-6_46
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DOI: https://doi.org/10.1007/978-3-031-10983-6_46
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