Exploring a Topical Representation of Documents for Recommendation Systems

Israel Mendonça, Antoine Trouvé, Akira Fukuda, Kazuaki Murakami

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

1 Citation (Scopus)

Abstract

In this paper, we address the performance problems inherited when we use word embedding for recommendation. Free-text documents has no structural constructing rules, and are hard to model. Hence, the problem of having an accurate model, that conveys all the important information is a nontrivial problem. We convert the document to a numeric structure using word-embedding and test two document representations: one based in the center of this numeric representation and the other one based on pre-defined set of topics. We build a free text recommendation system and study how the performance, in terms of precision and recommendation time, is affected by both representations. We then vary the number of topics used to represent documents and verify the tradeoffs inherited from having a compact representation. The more compact the recommendation, the shorter the recommendation time, however more information is lost in the compactation process. We empirically test different possibilities for the topics and find an optimal point that is 3 times faster than a baseline and almost as accurate as it.

Original languageEnglish
Title of host publication2018 9th International Conference on Awareness Science and Technology, iCAST 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages73-78
Number of pages6
ISBN (Electronic)9781538658260
DOIs
Publication statusPublished - Oct 31 2018
Event9th International Conference on Awareness Science and Technology, iCAST 2018 - Fukuoka, Japan
Duration: Sep 19 2018Sep 21 2018

Publication series

Name2018 9th International Conference on Awareness Science and Technology, iCAST 2018

Other

Other9th International Conference on Awareness Science and Technology, iCAST 2018
CountryJapan
CityFukuoka
Period9/19/189/21/18

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Human-Computer Interaction
  • Information Systems and Management
  • Experimental and Cognitive Psychology
  • Social Psychology
  • Communication

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  • Cite this

    Mendonça, I., Trouvé, A., Fukuda, A., & Murakami, K. (2018). Exploring a Topical Representation of Documents for Recommendation Systems. In 2018 9th International Conference on Awareness Science and Technology, iCAST 2018 (pp. 73-78). [8517192] (2018 9th International Conference on Awareness Science and Technology, iCAST 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICAwST.2018.8517192