Long tail recommender utilizing information diffusion theory

Masayuki Ishikawa, Peter Geczy, Noriaki Izumi, Takahira Yamaguchi

研究成果: Conference contribution

9 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008
ページ785-788
ページ数4
DOI
出版ステータスPublished - 2008 12 1
イベント2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008 - Sydney, NSW, Australia
継続期間: 2008 12 92008 12 12

出版物シリーズ

名前Proceedings - 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

フィンガープリント 「Long tail recommender utilizing information diffusion theory」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル