Recommender systems using category correlations based on WordNet similarity

Sang Min Choi, Da Jung Cho, Yo Sub Han, Ka Lok Man, Yan Sun

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

3 Citations (Scopus)

Abstract

Recently, many internet users are not only information consumers but also information providers. There is lots of information on the Web and most people can search information what they want through the Web. One problem of the large number of data in the Web is that we often spend most of our time to find a correct result from search results. Thus, people start looking for a better system that can suggest relevant information instead of letting users go through all search results: We call such systems recommendation systems. Conventional recommendation systems are based on collaborative filtering (CF) approaches. The CF approaches have two problems: sparsity and cold-start. Some researchers have studied to alleviate the problems in CF approaches. One of them is the recommendation algorithm based on category correlations. In this study, researchers utilize genre information in movie domain as category. They have drawn genre correlations using genre counting method. This approach can alleviate the user-side cold-start problems, however, there exists one problem that extensions of the approach are less likely. If a domain has singular category, then we cannot apply previous approaches. It means that we cannot draw category correlations. Because of this reason, we propose a novel approach that can draw category correlations for not only multiple categories but also singular one. We utilize word similarities provided by WordNet.

Original languageEnglish
Title of host publicationProceedings - 2015 International Conference on Platform Technology and Service, PlatCon 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5-6
Number of pages2
ISBN (Electronic)9781479918881
DOIs
Publication statusPublished - 2015 Apr 3
Event2015 2nd International Conference on Platform Technology and Service, PlatCon 2015 - Jeju, Korea, Republic of
Duration: 2015 Jan 262015 Jan 28

Publication series

NameProceedings - 2015 International Conference on Platform Technology and Service, PlatCon 2015

Other

Other2015 2nd International Conference on Platform Technology and Service, PlatCon 2015
CountryKorea, Republic of
CityJeju
Period15/1/2615/1/28

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Human-Computer Interaction
  • Information Systems
  • Software

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    Choi, S. M., Cho, D. J., Han, Y. S., Man, K. L., & Sun, Y. (2015). Recommender systems using category correlations based on WordNet similarity. In Proceedings - 2015 International Conference on Platform Technology and Service, PlatCon 2015 (pp. 5-6). [7079614] (Proceedings - 2015 International Conference on Platform Technology and Service, PlatCon 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PlatCon.2015.26