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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/23402
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| Title: | Leveraging Aggregate Ratings for Better Recommendations |
| Authors: | Umyarov, Akhmed
Tuzhilin, Alexander |
| Keywords: | Recommender systems
Hierarchical Bayesian models
predictive models
aggregate ratings
OLAP |
| Issue Date: | 13-Sep-2007 |
| Series/Report no.: | CeDER-07-03 |
| Abstract: | The paper presents a method that uses aggregate ratings provided by
various segments of users for various categories of items to derive
better estimations of unknown individual ratings. This is achieved by
converting the aggregate ratings into constraints on the parameters of a
rating estimation model presented in the paper. The paper also
demonstrates theoretically that these additional constraints reduce
rating estimation errors resulting in better rating predictions. |
| URI: | http://hdl.handle.net/2451/23402 |
| Appears in Collections: | CeDER Working Papers
IOMS: Information Systems Working Papers
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