Long tail recommender utilizing information diffusion theory

Masayuki Ishikawa, Peter Geczy, Noriaki Izumi, Takahira Yamaguchi

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

8 Citations (Scopus)

Abstract

Our approach aims to provide a mechanism for recommending long tail items to knowledge workers. The approach employs collaborative filtering using browsing features of identified key population of the diffusion of information. We conducted analytic experiment for a novel recommendation algorithm based on the browsing features of identified selected users and discovered that the first 10 users accessing a particular page play the key role in information spread. The evaluation indicated that our approach is effective for long tail recommendation.

Original languageEnglish
Title of host publicationProceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008
Pages785-788
Number of pages4
DOIs
Publication statusPublished - 2008 Dec 1
Event2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008 - Sydney, NSW, Australia
Duration: 2008 Dec 92008 Dec 12

Publication series

NameProceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008

Other

Other2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008
CountryAustralia
CitySydney, NSW
Period08/12/908/12/12

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Electrical and Electronic Engineering

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    Ishikawa, M., Geczy, P., Izumi, N., & Yamaguchi, T. (2008). Long tail recommender utilizing information diffusion theory. In Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008 (pp. 785-788). [4740549] (Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008). https://doi.org/10.1109/WIIAT.2008.352