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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/28089
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| Title: | Leveraging aggregate ratings for improving predictive performance of
recommender systems |
| Authors: | Umyarov, Akhmed
Tuzhilin, Alexander |
| Keywords: | Recommender systems
collaborative filtering
hierarchical linear models
predictive models
aggregate ratings
cold-stat problem |
| Issue Date: | 27-May-2009 |
| Series/Report no.: | CeDER-08-03 |
| Abstract: | This paper describes an approach for incorporating externally specified
aggregate ratings information into certain types of recommender systems,
including two types of collaborating filtering and a hierarchical linear
regression model. First, we present a framework for incorporating
aggregate rating information and apply this framework to the
aforementioned individual rating models. Then we formally show that this
additional aggregate rating information provides more accurate
recommendations of individual items to individual users. Further, we
experimentally confirm this theoretical finding by demonstrating on
several datasets that the aggregate rating information indeed leads to
better predictions of unknown ratings. We also propose scalable methods
for incorporating this aggregate information and test our approaches on
large datasets. Finally, we demonstrate that the aggregate rating
information can also be used as a solution to the cold start problem of
recommender systems. |
| URI: | http://hdl.handle.net/2451/28089 |
| Appears in Collections: | CeDER Working Papers
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