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A hybrid collaborative filtering recommender system using a new similarity measure.
| Content Provider | CiteSeerX |
|---|---|
| Abstract | 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. Key-Words: Hybrid recommender systems; collaborative filtering; similarity measure 1 |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Hybrid Collaborative Filtering Recommender System New Similarity Measure Collaborative Filtering Hybrid Recommender System Rating Difference New User Cold-start Condition Similarity Calculation Cold-start Condition Domain-specific Interpretation New Measure User Data Similarity Measure New Heuristic Similarity Measure Small Number |
| Content Type | Text |
| Resource Type | Article |
