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Sequential Recommendation Based on Pairs of Interest and Graph Structure

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Web Services – ICWS 2022 (ICWS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13736))

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Abstract

The recommendation system can recommend information to users efficaciously, which helps many users to obtain information in different fields. The chronological order of user-item interactions can reveal time-evolving and sequential user behaviors in many recommender systems. The difficulty of modeling user interests has always been a challenge. The recommendation is a research topic to provide users with personalized items of interest. However, most existing approaches equally treat simple textual information as the input to learn the representation of an item, ignoring the user’s interest and structure information of the network. In the recommendation system, users and items and the interaction of their information have a crucial impact on the efficiency and accuracy of the recommendations. However, most recommendation systems are usually designed based only on users. with little consideration given to obtaining other factors that contribute to recommendation behavior. Therefore, we propose a method based on the user’s periodic pairs of interest and graph structure to obtain as much effective information as possible to recommend items. Extensive offline experiments on large-scale real data show that our method outperforms the representative baselines.

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Authors and Affiliations

  1. Shandong University, Jinan, Shandong, 250101, China

    Hao Liu

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  1. Hao Liu
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Correspondence to Hao Liu .

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Editors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Yuchao Zhang

  2. Kingdee International Software Group Co., Ltd., Shenzhen, China

    Liang-Jie Zhang

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Liu, H. (2022). Sequential Recommendation Based on Pairs of Interest and Graph Structure. In: Zhang, Y., Zhang, LJ. (eds) Web Services – ICWS 2022. ICWS 2022. Lecture Notes in Computer Science, vol 13736. Springer, Cham. https://doi.org/10.1007/978-3-031-23579-5_6

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  • DOI: https://doi.org/10.1007/978-3-031-23579-5_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23578-8

  • Online ISBN: 978-3-031-23579-5

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