Name
Abstract | ||
|---|---|---|
In this work, we apply a clustering tech- nique to integrate the contents of items into the item-based collaborative filtering framework. The group rating information that is obtained from the clustering result provides a way to introduce content in- formation into collaborative recommenda- tion and solves the cold start problem. Extensive experiments have been con- ducted on MovieLens data to analyze the characteristics of our technique. The re- sults show that our approach contributes to the improvement of prediction quality of the item-based collaborative filtering, especially for the cold start problem. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.3115/1118935.1118938 | International Workshop on Information Retrieval with Asia Languages |
Keywords | Field | DocType |
collaborative recommendation,group rating information,content information,cold start problem,clustering technique,item-based collaborative,clustering result,prediction quality,extensive experiment,movielens data,collaborative filter,collaborative filtering | Recommender system,Data mining,Collaborative filtering,Information retrieval,Cold start,Computer science,MovieLens,Cluster analysis,Information filtering system | Conference |
Citations | PageRank | References |
21 | 1.55 | 18 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qing Li | 1 | 452 | 30.64 |
| Byeong Man Kim | 2 | 277 | 20.88 |