Usage-based PageRank for Web personalization

Recommendation algorithms aim at proposing "next" pages to a user based on her current visit and the past users' navigational patterns. In the vast majority of related algorithms, only the usage data are used to produce recommendations, whereas the structural properties of the Web graph are ignored. 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 novel 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 its usage data we produce personalized navigational graph synopsis (prNG) to be used for applying UPR and produce personalized recommendations. Experimental results show that the accuracy of the recommendations is superior to pure usage-based approaches.

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