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Generating Recommendations Based on Robust Term Extraction from Users' Reviews

Published: 18 November 2014 Publication History

Abstract

In this paper, we propose a technique to automatically describe items based on users' reviews in order to be used by recommender systems. For that, we extract items' features using a robust term extraction method that applies transductive semi-supervised learning to automatically identify aspects that represent the different subjects of the reviews. Then, we apply sentiment analysis in a sentence level to indicate the polarities, yielding a consensus of users regarding the features of items. Our approach is evaluated using a collaborative filtering method, and comparisons using structured metadata as baselines show promising results.

References

[1]
G. Adomavicius and A. Tuzhilin. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6):734--749, 2005.
[2]
C. Aggarwal and C. Zhai. A survey of text clustering algorithms. In C. C. Aggarwal and C. Zhai, editors, Mining Text Data, pages 77--128. Springer US, 2012.
[3]
M. T. Cabré and R. E. J. Vivaldi. Automatic term detection: a review of current systems. In D. Bourigault, C. Jacquemin, and M.-C. L'Homme, editors, Recent Advances in Computational Terminology, pages 53--88, Amsterdam/Philadelphia, 2001. John Benjamins.
[4]
M. S. Conrado, A. Di Felippo, T. S. Pardo, and S. O. Rezende. A survey of automatic term extraction for brazilian portuguese. Journal of the Brazilian Computer Society, 20(1):12, 2014.
[5]
M. S. Conrado, R. G. Rossi, T. A. S. Pardo, and S. O. Rezende. Applying transductive learning for automatic term extraction: The case of the ecology domain. In Proceedings of the IEEE 2nd INT CNF on Informatics and Applications (ICIA), pages 264--269, Lodz, Poland, 2013.
[6]
M. S. Conrado-Laguna. Extraçao automática de termos simples baseada em aprendizado de máquina. PhD thesis, University of Sao Paulo (ICMC-USP), SP, Brazil, 2014.
[7]
R. D'Addio and M. Manzato. A Collaborative Filtering Approach based on User's Reviews. In Brazilian Conference on Intelligent Systems and Encontro Nacional de Inteligência Artificial e Computacional 2014 (BRACIS/ENIAC 2014), 2014 (to appear).
[8]
C. Desrosiers and G. Karypis. A comprehensive survey of neighborhood-based recommendation methods. In F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, editors, Recommender Systems Handbook, pages 107--144. Springer US, 2011.
[9]
Z. Gantner, S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme. MyMediaLite: A free recommender system library. In PROC of the 5th ACM CNF on Recommender Systems (RecSys), 2011.
[10]
G. Ganu, Y. Kakodkar, and A. Marian. Improving the quality of predictions using textual information in online user reviews. Inf. Syst., 38(1):1--15, Mar. 2013.
[11]
H. Kim, K. Han, M. Yi, J. Cho, and J. Hong. Moviemine: personalized movie content search by utilizing user comments. IEEE Transactions on Consumer Electronics, 58(4):1416--1424, 2012.
[12]
P. Lops, M. de Gemmis, and G. Semeraro. Content-based recommender systems: State of the art and trends. In F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, editors, Recommender Systems Handbook, pages 73--105. Springer US, 2011.
[13]
R. Qumsiyeh and Y.-K. Ng. Predicting the ratings of multimedia items for making personalized recommendations. In PROC of the 35th Annual INT ACM CNF on Research and Development in Information Retrieval (SIGIR), pages 475--484, New York, NY, USA, 2012.
[14]
F. Ricci, L. Rokach, and B. Shapira. Introduction to recommender systems handbook. In F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, editors, Recommender Systems Handbook, pages 1--35. Springer US, 2011.
[15]
R. G. Rossi, A. A. Lopes, and S. O. Rezende. A parameter-free label propagation algorithm using bipartite heterogeneous networks for text classification. In PROC of ACM - Symposium on Applied Computing (SAC). ACM, 2014.
[16]
R. Socher, J. Bauer, C. D. Manning, and A. Y. Ng. Parsing with compositional vector grammars. In PROC of the ACL CNF, 2013.
[17]
R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts. Recursive deep models for semantic compositionality over a sentiment treebank. In PROC of the CNF on Empirical Methods in Natural Language Processing, pages 1631--1642, Stroudsburg, PA, October 2013. Association for Computational Linguistics.
[18]
P.-N. Tan, M. Steinbach, and V. Kumar. Introduction to Data Mining. Pearson Education, 2 edition, 2014.
[19]
Z. Zhang, J. Iria, C. Brewster, and F. Ciravegna. A comparative evaluation of term recognition algorithms. In N. Calzolari, K. Choukri, B. Maegaard, J. Mariani, J. Odjik, S. Piperidis, and D. Tapias, editors, Proc 6th on INT CNF on Language Resources and Evaluation (LREC), pages 2108--2113, Marrakech, Morocco, 2008. ELRA.
[20]
D. Zhou, O. Bousquet, T. N. Lal, J. Weston, and B. Schölkopf. Learning with local and global consistency. In Advances in Neural Information Processing Systems (NIPS), volume 16, 2004.

Cited By

View all
  • (2023)Metadata Based Cross-Domain Recommender Framework Using Neighborhood Mapping2023 International Conference on Sustainable Technology and Engineering (i-COSTE)10.1109/i-COSTE60462.2023.10500780(1-8)Online publication date: 4-Dec-2023
  • (2023)Metadata based Cross-Domain Recommender Framework using Neighborhood Mapping2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)10.1109/CSDE59766.2023.10487686(1-8)Online publication date: 4-Dec-2023
  • (2021)An interpretable mechanism for personalized recommendation based on cross featureJournal of Intelligent & Fuzzy Systems10.3233/JIFS-202308(1-12)Online publication date: 19-Feb-2021
  • Show More Cited By

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Published In

cover image ACM Other conferences
WebMedia '14: Proceedings of the 20th Brazilian Symposium on Multimedia and the Web
November 2014
256 pages
ISBN:9781450332309
DOI:10.1145/2664551
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]

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  • SBC: Brazilian Computer Society

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 November 2014

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Author Tags

  1. recommender systems
  2. sentiment analysis
  3. term extraction

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  • Research-article

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WebMedia'14
Sponsor:
  • SBC
WebMedia'14: 20th Brazilian Symposium on Multimedia and the Web
November 18 - 21, 2014
João Pessoa, Brazil

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WebMedia '14 Paper Acceptance Rate 25 of 86 submissions, 29%;
Overall Acceptance Rate 270 of 873 submissions, 31%

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Cited By

View all
  • (2023)Metadata Based Cross-Domain Recommender Framework Using Neighborhood Mapping2023 International Conference on Sustainable Technology and Engineering (i-COSTE)10.1109/i-COSTE60462.2023.10500780(1-8)Online publication date: 4-Dec-2023
  • (2023)Metadata based Cross-Domain Recommender Framework using Neighborhood Mapping2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)10.1109/CSDE59766.2023.10487686(1-8)Online publication date: 4-Dec-2023
  • (2021)An interpretable mechanism for personalized recommendation based on cross featureJournal of Intelligent & Fuzzy Systems10.3233/JIFS-202308(1-12)Online publication date: 19-Feb-2021
  • (2021)Multi Criteria Decisions—A Modernistic Approach to Designing Recommender SystemsIntelligent Computing Paradigm and Cutting-edge Technologies10.1007/978-3-030-65407-8_20(231-243)Online publication date: 22-Apr-2021
  • (2019)Multi-Criteria Review-Based Recommender System–The State of the ArtIEEE Access10.1109/ACCESS.2019.29548617(169446-169468)Online publication date: 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
  • (2017)Exploiting feature extraction techniques on users’ reviews for movies recommendationJournal of the Brazilian Computer Society10.1186/s13173-017-0057-823:1Online publication date: 5-Jun-2017
  • (2017)Semantic Organization of User's Reviews Applied in Recommender SystemsProceedings of the 23rd Brazillian Symposium on Multimedia and the Web10.1145/3126858.3131600(277-280)Online publication date: 17-Oct-2017
  • (2016)Exploiting Item Representations for Soft Clustering RecommendationProceedings of the 22nd Brazilian Symposium on Multimedia and the Web10.1145/2976796.2976858(271-278)Online publication date: 8-Nov-2016
  • (2016)Mining unstructured content for recommender systems: an ensemble approachInformation Retrieval Journal10.1007/s10791-016-9280-819:4(378-415)Online publication date: 24-May-2016
  • Show More Cited By

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