NDLI: A Web Recommender System Based on Dynamic Sampling of User Information Access Behaviors

Content Provider	IEEE Xplore Digital Library
Author	Jian Chen Shtykh, R.Y. Qun Jin
Copyright Year	2009
Abstract	Abstract — 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.
Starting Page	172
Ending Page	177
File Size	301423
Page Count	6
File Format	PDF
ISBN	9780769538365
DOI	10.1109/CIT.2009.119
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-11
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	dynamic sampling Humans data mining Wikipedia Data mining Information technology information recommendation Information analysis gradual adaption Web mining Search engines Sampling methods Collaborative work Recommender systems
Content Type	Text
Resource Type	Article

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