ISSN 1000-1239 CN 11-1777/TP

### Graph Embedding Based Session Perception Model for Next-Click Recommendation

Zeng Yifu1,2,3, Mu Qilin2,3, Zhou Le1, Lan Tian1, Liu Qiao1

1. 1(School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054);2(Big Data Application on Improving Government Governance Capabilities National Engineering Laboratory (CETC Big Data Research Institute Co., Ltd.), Guiyang 550022);3(CETC Big Data Research Institute Co., Ltd., Guiyang 550022)
• Online:2020-03-01
• Supported by:
This work was supported by the National Science Foundation of China (U19B2028, 61772117), the Big Data Application on Improving Government Governance Capabilities National Engineering Laboratory Open Fund Project (10-2018039), the Sichuan Hi-Tech Industrialization Program (2018GFW0150), and the Fundamental Research Funds for the Central Universities (ZYGX2019J077).

Abstract: Predicting users’ next-click according to their historical session records, also known as session-based recommendation, is an important and challenging task and has led to a considerable amount of work towards this aim. Several significant progresses have been made in this area, but some fundamental problems still remain open, such as the trade-off between users’ satisfaction and predictive accuracy of the models. In this study, we consider the problem of how to alleviate user interests drift without sacrificing the predictive accuracy. For this purpose, we first set up an item dependency graph to represent the click sequence of items from a global, statistical perspective. Then an efficient graph embedding learning algorithm is proposed to produce item embeddings which preserve the information flow properties of the system and the structural dependency between each pair of items. Finally, the proposed model is capable of capturing the users’ general interests and their temporal browsing interests simultaneously by using of a BiLSTM based long/short term memory mechanism. Experimental results on two real-world data sets show that the proposed model not only performs better in terms of predictive accuracy but also demonstrates better diversity and novelty in its recommendations as compared with other state-of-the-art methods.

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