Coarse-to-Fine Sparse Sequential Recommendation

Jiacheng Li, Tong Zhao, Jin Li, Jim Chan, Christos Faloutsos, George Karypis, Soo Min Pantel, Julian McAuley

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Sequential recommendation aims to model dynamic user behavior from historical interactions. Self-attentive methods have proven effective at capturing short-term dynamics and long-term preferences. Despite their success, these approaches still struggle to model sparse data, on which they struggle to learn high-quality item representations. We propose to model user dynamics from shopping intents and interacted items simultaneously. The learned intents are coarse-grained and work as prior knowledge for item recommendation. To this end, we present a coarse-to-fine self-attention framework, namely CaFe, which explicitly learns coarse-grained and fine-grained sequential dynamics. Specifically, CaFe first learns intents from coarse-grained sequences which are dense and hence provide high-quality user intent representations. Then, CaFe fuses intent representations into item encoder outputs to obtain improved item representations. Finally, we infer recommended items based on representations of items and corresponding intents. Experiments on sparse datasets show that CaFe outperforms state-of-the-art self-attentive recommenders by 44.03% NDCG@5 on average.

Original languageEnglish (US)
Title of host publicationSIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages2082-2086
Number of pages5
ISBN (Electronic)9781450387323
DOIs
StatePublished - Jul 6 2022
Event45th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2022 - Madrid, Spain
Duration: Jul 11 2022Jul 15 2022

Publication series

NameSIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

Conference45th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2022
Country/TerritorySpain
CityMadrid
Period7/11/227/15/22

Bibliographical note

Publisher Copyright:
© 2022 ACM.

Keywords

  • coarse-to-fine framework
  • sequential recommendation
  • sparse data
  • transformer

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