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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Leveraging aggregate ratings for better recommendations
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Collaborative filtering based on an iterative prediction method to alleviate the sparsity problem
iiWAS '09: Proceedings of the 11th International Conference on Information Integration and Web-based Applications & ServicesCollaborative filtering (CF) is one of the most popular recommender system technologies. It tries to identify users that have relevant interests and preferences by calculating similarities among user profiles. The idea behind this method is that, it may ...
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