Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.ipl.2011.02.003 | Inf. Process. Lett. |
Keywords | Field | DocType |
ranking approach,popular approach,item-based cf approach,recommendation system,pagerank-based user,item relationship,recommendation result,item-based collaborative,current item-based cf approach,userrank-based item similarity,item similarity,algorithms,data model,collaborative filtering,recommender system | Recommender system,Discrete mathematics,Similitude,PageRank,Data mining,Slope One,Information processing,Collaborative filtering,Information retrieval,Ranking,Computer science,Data model | Journal |
Volume | Issue | ISSN |
111 | 9 | 0020-0190 |
Citations | PageRank | References |
22 | 0.94 | 17 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Min Gao | 1 | 111 | 9.52 |
| Zhong-fu Wu | 2 | 193 | 23.62 |
| Feng Jiang | 3 | 22 | 0.94 |