Infusing latent user-concerns from user reviews into collaborative filtering

Ligaj Pradhan, Chengcui Zhang, Steven Bethard

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

Abstract

Traditionally, Collaborative Filtering (CF) based recommendation employs past rating behaviors of users on items to discover similar users and similar items. We can further improve on discovering user similarities by better understanding user behaviors through analyzing user reviews. In their reviews, users generally mention about things that are of greater interest to them, and these cues can provide an effective medium to discover users with similar interests and concerns. In this paper, we extract latent User-Concerns from user reviews and construct their hierarchical tree (UC-Tree). By associating each user with the corresponding concerns in the UC-Tree, we then generate vectors that represent intricate user behaviors. Finally, we infuse such additional knowledge about the users into the conventional CF-based rating prediction process. Our experiments and results show that such additional behavioral knowledge assists the discovery of similar users and improves the accuracy of conventional CF-based rating prediction.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Information Reuse and Integration, IRI 2017
EditorsLatifur Khan, Balaji Palanisamy, Chengcui Zhang, Sahra Sedigh Sarvestani
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages471-477
Number of pages7
ISBN (Electronic)9781538615621
DOIs
StatePublished - Nov 8 2017
Externally publishedYes
Event18th IEEE International Conference on Information Reuse and Integration, IRI 2017 - San Diego, United States
Duration: Aug 4 2017Aug 6 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Information Reuse and Integration, IRI 2017
Volume2017-January

Conference

Conference18th IEEE International Conference on Information Reuse and Integration, IRI 2017
Country/TerritoryUnited States
CitySan Diego
Period8/4/178/6/17

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems and Management
  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems

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