کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
428638 | 686850 | 2011 | 7 صفحه PDF | دانلود رایگان |

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.
► A user correlation graph model is presented for userrank calculations.
► A weighted PageRank algorithm is proposed for ranking users.
► We incorporate userrank into approaches for computing item similarities/differences.
► Userrank-based approaches improve the results of typical item-based CF approaches.
Journal: Information Processing Letters - Volume 111, Issue 9, 1 April 2011, Pages 440–446