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Semantic Organization of User's Reviews Applied in Recommender Systems

Published: 17 October 2017 Publication History

Abstract

Recommender systems are widely used to minimize the information overload problem. A great source of information is users' reviews, since they provide both item descriptions and users' opinions. Recent works that process reviews often neglect problems such as polysemy and sinonimy. On the other hand, systems that rely on word sense disambiguation focus their efforts on items's static descriptions. In this paper, we propose a hybrid recommender system that uses word sense disambiguation and entity linking to produce concept-based item representations extracted from users' reviews. Our findings suggest that adding such semantics to items' representations have a positive impact on recommendations.

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Cited By

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  • (2019)Characterization of the discrepancies between scores and texts of movie reviewsProceedings of the 25th Brazillian Symposium on Multimedia and the Web10.1145/3323503.3360296(229-236)Online publication date: 29-Oct-2019
  • (2019)Combining different metadata views for better recommendation accuracyInformation Systems10.1016/j.is.2019.01.00883:C(1-12)Online publication date: 1-Jul-2019
  • (2018)Incorporating Semantic Item Representations to Soften the Cold Start ProblemProceedings of the 24th Brazilian Symposium on Multimedia and the Web10.1145/3243082.3243112(157-164)Online publication date: 16-Oct-2018

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cover image ACM Other conferences
WebMedia '17: Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web
October 2017
522 pages
ISBN:9781450350969
DOI:10.1145/3126858
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

  • SBC: Brazilian Computer Society
  • CNPq: Conselho Nacional de Desenvolvimento Cientifico e Tecn
  • CGIBR: Comite Gestor da Internet no Brazil
  • CAPES: Brazilian Higher Education Funding Council

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2017

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Author Tags

  1. item representation
  2. recommender systems
  3. word sense disambiguation

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  • Short-paper

Funding Sources

  • Fundação de Amparo à Pesquisa do Estado de São Paulo

Conference

Webmedia '17
Sponsor:
  • SBC
  • CNPq
  • CGIBR
  • CAPES
Webmedia '17: Brazilian Symposium on Multimedia and the Web
October 17 - 20, 2017
RS, Gramado, Brazil

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WebMedia '17 Paper Acceptance Rate 38 of 138 submissions, 28%;
Overall Acceptance Rate 270 of 873 submissions, 31%

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Cited By

View all
  • (2019)Characterization of the discrepancies between scores and texts of movie reviewsProceedings of the 25th Brazillian Symposium on Multimedia and the Web10.1145/3323503.3360296(229-236)Online publication date: 29-Oct-2019
  • (2019)Combining different metadata views for better recommendation accuracyInformation Systems10.1016/j.is.2019.01.00883:C(1-12)Online publication date: 1-Jul-2019
  • (2018)Incorporating Semantic Item Representations to Soften the Cold Start ProblemProceedings of the 24th Brazilian Symposium on Multimedia and the Web10.1145/3243082.3243112(157-164)Online publication date: 16-Oct-2018

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