Name
Abstract | ||
|---|---|---|
In this paper, we present an improved method to solve the sparsity problems of collaborative filtering by defining a user model as a stereotype using demographic information based on user's characteristics and adopt stereotype information to make up for similarity correction. The similarity between users is only determined by the ratings given to co-rated items, so items that have not been rated by both users are ignored. To solve this problem, we add virtual neighbor's, rating using demographic information of neighbors for improving prediction accuracy. We show improved accuracy by comparing between the traditional Pearson correlation coefficient and the proposed method. |
Year | Venue | Keywords |
|---|---|---|
2003 | MLMTA'03: INTERNATIONAL CONFERENCE ON MACHINE LEARNING; MODELS, TECHNOLOGIES AND APPLICATIONS | collaborative filtering recommendation system,stereotype model |
Field | DocType | Citations |
Recommender system,World Wide Web,Collaborative filtering,Information retrieval,Computer science,Stereotype | Conference | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yongjun Lee | 1 | 39 | 3.49 |
| Se-Hoon Lee | 2 | 5 | 2.24 |
| Junghyun Lee | 3 | 0 | 1.35 |