Modelling temporal dynamics and repeated behaviors for recommendation

Xin Zhou, Zhu Sun, Guibing Guo, Yuan Liu

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

1 Citation (Scopus)

Abstract

Personalized recommendation has yield immense success in predicting user preference with heterogeneous implicit feedback (HIF), i.e., various user behaviors. However, existing studies consider less about the temporal dynamics and repeated patterns of HIF. They simply suppose: (1) a hard rule among user behaviors (e.g., add-to-cart must come before purchase and after view); (2) merge repeated behaviors into one (e.g., view several times is considered as view once only), thus failing to unveil user preferences from their real behaviors. To ease these issues, we, therefore, propose a novel end-to-end neural framework – TDRB, which automatically models the Temporal Dynamics and Repeated Behaviors to assist in capturing user preference, thus achieving more accurate recommendations. Empirical studies on three real-world datasets demonstrate the superiority of our proposed TDRB against other state-of-the-arts.
Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
Subtitle of host publication24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020
EditorsHady W. Lauw, Raymond Chi-Wing Wong, Alexandros Ntoulas, Ee-Peng Lim, See-Kiong Ng, Sinno Jialin Pan
Place of PublicationCham, Switzerland
PublisherSpringer, Springer Nature
Pages181-193
Number of pages13
ISBN (Electronic)978-3-030-47426-3
ISBN (Print)978-3-030-47425-6
DOIs
Publication statusPublished - 2020
Event24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020 - Singapore, Singapore
Duration: 11 May 202014 May 2020

Publication series

NameLecture Notes in Artificial Intelligence
Volume12084
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020
CountrySingapore
CitySingapore
Period11/05/2014/05/20

Keywords

  • Temporal dynamics
  • Repeated behaviors
  • Heterogeneous implicit feedback
  • Recommendation

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