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Userrank for item-based collaborative filtering recommendation. (English) Zbl 1260.68442
Summary: With the recent explosive growth of the Web, recommendation systems have been widely accepted by users. Item-based collaborative filtering (CF) is one of the most popular approaches for determining recommendations. A common problem of current item-based CF approaches is that all users have the same weight when computing the item relationships. To improve the quality of recommendations, we incorporate the weight of a user, userrank, into the computation of item similarities and differentials. In this paper, a data model for userrank calculations, a PageRank-based user ranking approach, and a userrank-based item similarities/differentials computing approach are proposed. Finally, the userrank-based approaches improve the recommendation results of the typical adjusted cosine and slope one item-based CF approaches.
MSC:
68U35 Computing methodologies for information systems (hypertext navigation, interfaces, decision support, etc.)
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