Name
Abstract | ||
|---|---|---|
Among the recommender system technologies, Collaborative filtering system, which employs statistical techniques to find a set of customers who have a history of agreeing with the target user, has achieved widespread success on the E-commerce site. Although collaborative filtering system overcomes almost all the shortcomings of content-based systems, it is still reported having some limitations just like sparsity and scalability. In this paper, Clustering Using Representatives Algorithm is used to generate a new cluster-product matrix from original matrix. Based on the new matrix, traditional way is adopted to find the nearest neighbors. And at last a formula is given to generate the top-N recommendations. The experiment results suggest that the improved Collaborative Filtering Method can increase the accuracy of the recommendations and the efficiency of the system. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/ICSMC.2004.1401179 | 2004 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN & CYBERNETICS, VOLS 1-7 |
Keywords | Field | DocType |
e-commerce, recommender system, collaborative filtering, MAE | Recommender system,Customer relationship management,Data mining,Collaborative filtering,Collaborative software,Computer science,Matrix (mathematics),Artificial intelligence,Cluster analysis,Machine learning,Scalability | Conference |
ISSN | Citations | PageRank |
1062-922X | 16 | 1.37 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wujian Yang | 1 | 16 | 1.37 |
| Zebing Wang | 2 | 17 | 2.76 |
| Mingyu You | 3 | 160 | 16.22 |