A phrase-based model to discover hidden factors and hidden topics in recommender systems

Liping Guan, Md Hijbul Alam, Woo Jong Ryu, Sang-Geun Lee

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

1 Citation (Scopus)

Abstract

The majority of existing recommender systems focus on modeling the ratings; however, these systems ignore a large number of reviews. Existing rating based recommender systems are hard to discover the hidden dimensions in human feedback that can identify user preferences. In this study, we combine collaborative filtering with latent review topics to generate a new model called phraseHFT. We apply reviews to the phrase-level document, and use the phrase-based topic model to discover the review topics that are embedded in the review text. The interpretable topics which are learned and presented as phrases can help us understand characteristics of users or items. The conducted experiment shows that our approach outperforms the state-of-the-art techniques in perplexity and topic visualization due to the strong topic learning functionality.

Original languageEnglish
Title of host publication2016 International Conference on Big Data and Smart Computing, BigComp 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages337-340
Number of pages4
ISBN (Print)9781467387965
DOIs
Publication statusPublished - 2016 Mar 3
EventInternational Conference on Big Data and Smart Computing, BigComp 2016 - Hong Kong, China
Duration: 2016 Jan 182016 Jan 20

Other

OtherInternational Conference on Big Data and Smart Computing, BigComp 2016
CountryChina
CityHong Kong
Period16/1/1816/1/20

Fingerprint

Recommender systems
Collaborative filtering
Visualization
Factors
Feedback
Experiments
Rating

Keywords

  • Collaborative filtering
  • Recommender system
  • Topic modeling

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management

Cite this

Guan, L., Alam, M. H., Ryu, W. J., & Lee, S-G. (2016). A phrase-based model to discover hidden factors and hidden topics in recommender systems. In 2016 International Conference on Big Data and Smart Computing, BigComp 2016 (pp. 337-340). [7425942] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIGCOMP.2016.7425942

A phrase-based model to discover hidden factors and hidden topics in recommender systems. / Guan, Liping; Alam, Md Hijbul; Ryu, Woo Jong; Lee, Sang-Geun.

2016 International Conference on Big Data and Smart Computing, BigComp 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 337-340 7425942.

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

Guan, L, Alam, MH, Ryu, WJ & Lee, S-G 2016, A phrase-based model to discover hidden factors and hidden topics in recommender systems. in 2016 International Conference on Big Data and Smart Computing, BigComp 2016., 7425942, Institute of Electrical and Electronics Engineers Inc., pp. 337-340, International Conference on Big Data and Smart Computing, BigComp 2016, Hong Kong, China, 16/1/18. https://doi.org/10.1109/BIGCOMP.2016.7425942
Guan L, Alam MH, Ryu WJ, Lee S-G. A phrase-based model to discover hidden factors and hidden topics in recommender systems. In 2016 International Conference on Big Data and Smart Computing, BigComp 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 337-340. 7425942 https://doi.org/10.1109/BIGCOMP.2016.7425942
Guan, Liping ; Alam, Md Hijbul ; Ryu, Woo Jong ; Lee, Sang-Geun. / A phrase-based model to discover hidden factors and hidden topics in recommender systems. 2016 International Conference on Big Data and Smart Computing, BigComp 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 337-340
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