Abstract
This article 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.
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Index Terms
Using external aggregate ratings for improving individual recommendations
Recommendations
Leveraging aggregate ratings for improving predictive performance of recommender systems
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ICDM '08: Proceedings of the 2008 Eighth IEEE International Conference on Data MiningThis paper describes an approach for incorporating externally specified aggregate ratings information into certain types of collaborative filtering (CF) methods. For a statistical model-based CF approach, we formally showed that this additional ...
Leveraging aggregate ratings for better recommendations
RecSys '07: Proceedings of the 2007 ACM conference on Recommender systemsThe 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 ...






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