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A hybrid collaborative filtering recommender system using a new similarity measure
| Content Provider | Semantic Scholar |
|---|---|
| Author | Ahn, Hyung Jun |
| Copyright Year | 2007 |
| Abstract | This paper presents a hybrid recommender system using a new heuristic similarity measure for collaborative filtering that focuses on improving performance under cold-start conditions where only a small number of ratings are available for similarity calculation for each user. The new measure is based on the domain-specific interpretation of rating differences in user data. Experiments using three datasets show the superiority of the measure in new user cold-start conditions. |
| Starting Page | 494 |
| Ending Page | 498 |
| Page Count | 5 |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.wseas.us/e-library/conferences/2007hangzhou/papers/560-401.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |