Name
Title | ||
|---|---|---|
An iterative semi-explicit rating method for building collaborative recommender systems |
Abstract | ||
|---|---|---|
Collaborative filtering plays the key role in recent recommender systems. It uses a user-item preference matrix rated either explicitly (i.e., explicit rating) or implicitly (i.e., implicit feedback). Despite the explicit rating captures the preferences better, it often results in a severely sparse matrix. The paper presents a novel iterative semi-explicit rating method that extrapolates unrated elements in a semi-supervised manner. Extrapolation is simply an aggregation of neighbor ratings, and iterative extrapolations result in a dense preference matrix. Preliminary simulation results show that the recommendation using the semi-explicit rating data outperforms that of using the pure explicit data only. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.eswa.2008.07.085 | Expert Syst. Appl. |
Keywords | Field | DocType |
collaborative filtering,semi-explicit rating,explicit rating,user-item preference matrix,recommender system,data sparsity,pure explicit data,iterative semi-explicit rating method,sparse matrix,collaborative recommender system,iterative extrapolations result,neighbor rating,novel iterative semi-explicit rating,implicit feedback,semi-explicit rating data,dense preference matrix | Recommender system,Data mining,Collaborative filtering,Matrix (mathematics),Computer science,Extrapolation,Artificial intelligence,Sparse matrix,Machine learning | Journal |
Volume | Issue | ISSN |
36 | 3 | Expert Systems With Applications |
Citations | PageRank | References |
26 | 0.79 | 19 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Buhwan Jeong | 1 | 146 | 6.85 |
| Jaewook Lee | 2 | 735 | 50.24 |
| Hyunbo Cho | 3 | 258 | 23.62 |