Sain: Self-attentive integration network for recommendation

Seoungjun Yun, Raehyun Kim, Miyoung Ko, Jaewoo Kang

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

4 Citations (Scopus)

Abstract

With the growing importance of personalized recommendation, numerous recommendation models have been proposed recently. Among them, Matrix Factorization (MF) based models are the most widely used in the recommendation field due to their high performance. However, MF based models suffer from cold start problems where user-item interactions are sparse. To deal with this problem, content based recommendation models which use the auxiliary attributes of users and items have been proposed. Since these models use auxiliary attributes, they are effective in cold start settings. However, most of the proposed models are either unable to capture complex feature interactions or not properly designed to combine user-item feedback information with content information. In this paper, we propose Self-Attentive Integration Network (SAIN) which is a model that effectively combines user-item feedback information and auxiliary information for recommendation task. In SAIN, a self-attention mechanism is used in the feature-level interaction layer to effectively consider interactions between multiple features, while the information integration layer adaptively combines content and feedback information. The experimental results on two public datasets show that our model outperforms the state-of-the-art models by 2.13%.

Original languageEnglish
Title of host publicationSIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages1205-1208
Number of pages4
ISBN (Electronic)9781450361729
DOIs
Publication statusPublished - 2019 Jul 18
Event42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019 - Paris, France
Duration: 2019 Jul 212019 Jul 25

Publication series

NameSIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

Conference42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019
CountryFrance
CityParis
Period19/7/2119/7/25

Keywords

  • Datasets
  • Gaze detection
  • Neural networks
  • Text tagging

ASJC Scopus subject areas

  • Information Systems
  • Applied Mathematics
  • Software

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  • Cite this

    Yun, S., Kim, R., Ko, M., & Kang, J. (2019). Sain: Self-attentive integration network for recommendation. In SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1205-1208). (SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval). Association for Computing Machinery, Inc. https://doi.org/10.1145/3331184.3331342