Joint relational dependency learning for sequential recommendation

Xiangmeng WANG, Qian LI, Wu ZHANG, Guandong XU, Shaowu LIU, Wenhao ZHU

Research output: Chapter in Book/Report/Conference proceedingChapters

7 Citations (Scopus)

Abstract

Sequential recommendation leverages the temporal information of users’ transactions as transition dependencies for better inferring user preference, which has become increasingly popular in academic research and practical applications. Short-term transition dependencies contain the information of partial item orders, while long-term transition dependencies infer long-range user preference, the two dependencies are mutually restrictive and complementary. Although some work investigates unifying both long-term and short-term dependencies for better performance, they still neglect the fact that short-term interactions are multi-folds, which are either individual-level interactions or union-level interactions. Existing sequential recommendations mainly focus on user’s individual (i.e., individual-level) interactions but ignore the important collective influence at union-level. Since union-level interactions can reflect that human decisions are made based on multiple items he/she has already interacted, ignoring such interactions can result in the disability of capturing the collective influence between items. To alleviate this issue, we proposed a Joint Relational Dependency learning (JRD-L) for sequential recommendation that exploits both long-term and short-term preferences at individual-level and union-level. Specifically, JRD-L combines long-term user preferences with short-term interests by measuring short-term pair relations at individual-level and union-level. Moreover, JRD-L can alleviate the sparsity problem of union-level interactions by adding more descriptive details to each item, which is carried by individual-level relations. Extensive numerical experiments demonstrate JRD-L outperforms state-of-the-art baselines for the sequential recommendation. Copyright © 2020 Springer Nature Switzerland AG

Original languageEnglish
Title of host publicationAdvances in knowledge discovery and data mining: 24th Pacific-Asia Conference, PAKDD 2020, Singapore, May 11–14, 2020, proceedings, part I
EditorsHady W. LAUW, Raymond Chi-Wing WONG, Alexandros NTOULAS, Ee-Peng LIM, See-Kiong NG, Sinno Jialin PAN
Place of PublicationCham
PublisherSpringer
Pages168-180
ISBN (Electronic)9783030474263
ISBN (Print)9783030474256
DOIs
Publication statusPublished - 2020

Citation

Wang, X., Li, Q., Zhang, W., Xu, G., Liu, S., & Zhu, W. (2020). Joint relational dependency learning for sequential recommendation. In H. W. Lauw, R. C.-W. Wong, A. Ntoulas, E.-P. Lim, S.-K. Ng, & S. J. Pan (Eds.), Advances in knowledge discovery and data mining: 24th Pacific-Asia Conference, PAKDD 2020, Singapore, May 11–14, 2020, proceedings, part I (pp. 168-180). Springer. https://doi.org/10.1007/978-3-030-47426-3_14

Keywords

  • Sequential recommendation
  • Long-term user preference
  • Short-term user preference
  • Multi-relational dependency

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