Multi-feature Fusion Based Short Session Recommendation Model
XIA Hongbin1,2, HUANG Kai1, LIU Yuan1,2
1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122; 2. Jiangsu Key Laboratory of Media Design and Software Technology, Jiangnan University, Wuxi 214122
Abstract：Most research on session recommendation systems focuses on long session recommendation and neglects short sessions. However, in practice short session information account for majority of the information. Due to the limited information contained in short sessions, it is crucial to learn more diverse user preferences and find similar context sessions accurately from short sessions. Therefore, a multi-feature fusion based short session recommendation model(MFFSSR) is proposed. Firstly, the node features and sequence features of sessions are learned respectively via neighborhood aggregation and recurrent neural networks. Secondly, the custom similarity calculation formula is utilized to retrieve the current user history session and other user sessions as context information, which alleviate the lack of information in short sessions. Next, the location-aware multi-head self-attention network is applied to fully explore the hidden features of sessions. Finally, the model recommends the next item based on the current session of multi-feature fusion. Experiments on two real datasets show that the proposed model is superior in terms of metrics. The code for the proposed model can be found at http://github.com/ScarletHK/MFF-SRR.
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2023, Vol. 36 
