AMBR: boosting the performance of personalized recommendation via learning from multi-behavior data

Chen Wang, Shilu Lin, Zhicong Zhong, Yipeng Zhou, Di Wu*

*Corresponding author for this work

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

Abstract

The performance of personalized recommendation can be further improved by exploiting multiple user behaviors (e.g., browsing, adding-to-cart, product purchasing) to predict items of user interests. However, the challenge lies in how to accurately model the relations among multiple user behaviors. The commonly adopted cascade relation over-simplifies the problem and cannot model the real user behavior patterns. In this paper, we propose a novel multi-behavior recommendation algorithm called AMBR (Attentive Multi-Behavior Recommendation), which can well capture the complicated relations among multiple behaviors. AMBR integrates the representation learning module and the matching function learning module into one framework. By utilizing the modern neural network techniques, AMBR is more flexible in modeling the relations of multiple behaviors without presuming a fixed cascade relation. Finally, we also conduct a set of experiments based on two real-world datasets, and the results show that our AMBR algorithm significantly outperforms other state-of-the-art algorithms by over 8.6%, 9.3% in terms of HR and NDCG.

Original languageEnglish
Title of host publicationNeural Information Processing - 27th International Conference, ICONIP 2020, Proceedings
EditorsHaiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King
Place of PublicationCham, Switzerland
PublisherSpringer, Springer Nature
Pages395-406
Number of pages12
ISBN (Electronic)9783030638368
ISBN (Print)9783030638351
DOIs
Publication statusPublished - 2020
EventInternational Conference on Neural Information Processing (27th : 2020) - Bangkok, Thailand
Duration: 18 Nov 202022 Nov 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12534 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Neural Information Processing (27th : 2020)
Abbreviated titleICONIP 2020
CountryThailand
CityBangkok
Period18/11/2022/11/20

Keywords

  • Multi-behavior data
  • Neural networks
  • Personalized recommendation

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