A web recommender system based on dynamic sampling of user information access behaviors

Jian Chen*, Roman Y. Shtykh, Qun Jin

*この研究の対応する著者

研究成果

2 被引用数 (Scopus)

抄録

In this study, we propose a Gradual Adaption Model for a Web recommender system. This model is used to track users' focus of interests and its transition by analyzing their information access behaviors, and recommend appropriate information. A set of concept classes are extracted from Wikipedia. The pages accessed by users are classified by the concept classes, and grouped into three terms of short, medium and long periods, and two categories of remarkable and exceptional for each concept class, which are used to describe users' focus of interests, and to establish reuse probability of each concept class in each term for each user by Full Bayesian Estimation as well. According to the reuse probability and period, the information that a user is likely to be interested in is recommended. In this paper, we propose a new approach by which short and medium periods are determined based on dynamic sampling of user information access behaviors. We further present experimental simulation results, and show the validity and effectiveness of the proposed system.

本文言語English
ホスト出版物のタイトルProceedings - IEEE 9th International Conference on Computer and Information Technology, CIT 2009
ページ172-177
ページ数6
DOI
出版ステータスPublished - 2009 12 1
イベントIEEE 9th International Conference on Computer and Information Technology, CIT 2009 - Xiamen, China
継続期間: 2009 10 112009 10 14

出版物シリーズ

名前Proceedings - IEEE 9th International Conference on Computer and Information Technology, CIT 2009
2

Conference

ConferenceIEEE 9th International Conference on Computer and Information Technology, CIT 2009
国/地域China
CityXiamen
Period09/10/1109/10/14

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • コンピュータ サイエンスの応用
  • ハードウェアとアーキテクチャ
  • ソフトウェア

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