UPR : Usage-based Page Ranking for Web Personalization

Recommendation algorithms aim at proposing “next” pages to a user based on her navigational behavior. In the vast majority of related algorithms, only the usage data are used to produce recommendations. We claim that taking also into account the web structure and using link analysis algorithms ameliorates the quality of recommendations. In this paper we present UPR, a personalization algorithm which combines usage data and link analysis techniques for ranking and recommending web pages to the end user. Using the web site’s structure and previously recorded user sessions we produce personalized navigational subgraphs (prNGs) to be used for applying UPR. Experimental results show that the accuracy of the generated recommendations is superior to pure usage-based approaches.

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