Web Page Recommendation in a Social Tagging System

نویسندگان

  • Maryam Ramezani
  • Daniela Raicu
  • Bamshad Mobasher
چکیده

In this paper we propose to use social tagging systems for Web page recommendation. Using social bookmarking systems allows the recommender to recommend web pages from various domains as opposed to web usage recommenders which are able to recommend pages in a very restricted domain. In this paper we use clustering and principle component analysis to construct the recommendations for each user and evaluate our system using a real data.

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تاریخ انتشار 2008