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A Data Mining Framework for Building A Web-Page Recommender System
| Content Provider | CiteSeerX |
|---|---|
| Author | Chen, Shu-Ching Haruechaiyasak, Choochart Shyu, Mei-Ling |
| Description | 2006 IEEE International Conference on Information Reuse and Integration (IRI’2004), Las Vegas |
| Abstract | In this paper, we propose a new framework based on data mining algorithms for building a Web-page recommender system. A recommender system is an intermediary program (or an agent) with a user interface that automatically and intelligently generates a list of information which suits an individual’s needs. Two information filtering methods for providing the recommended information are considered: (1) by analyzing the information content, i.e., content-based filtering, and (2) by referencing other user access behaviors, i.e., collaborative filtering. By using the data mining algorithms, the information filtering processes can be performed prior to the actual recommending process. As a result, the system response time could be improved and thus, making the framework scalable. 1. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | New Framework Collaborative Filtering Web-page Recommender System System Response Time User Interface Data Mining Algorithm Content-based Filtering Information Content Intermediary Program Information Filtering Individual Need User Access Behavior Recommender System Data Mining Framework Actual Recommending Process |
| Content Type | Text |
| Resource Type | Conference Proceedings |
