Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Multidimensional Recommender Systems: A Data Warehousing Approach
WELCOM '01 Proceedings of the Second International Workshop on Electronic Commerce
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
PolyLens: a recommender system for groups of users
ECSCW'01 Proceedings of the seventh conference on European Conference on Computer Supported Cooperative Work
Estimating rates of rare events at multiple resolutions
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Modeling relationships at multiple scales to improve accuracy of large recommender systems
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Leveraging aggregate ratings for better recommendations
Proceedings of the 2007 ACM conference on Recommender systems
Improving Collaborative Filtering Recommendations Using External Data
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
The adaptive web
Preference-based user rate correction process for interactive recommendation systems
Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services
When recommenders fail: predicting recommender failure for algorithm selection and combination
Proceedings of the sixth ACM conference on Recommender systems
Preference-based user rating correction process for interactive recommendation systems
Multimedia Tools and Applications
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Previous work on using external aggregate rating information showed that this information can be incorporated in several different types of recommender systems and improves their performance. In this paper, we propose a more general class of methods that combine external aggregate information with individual ratings in a novel way. Unlike the previously proposed methods, one of the defining features of this approach is that it takes into the consideration not only the aggregate average ratings but also the variance of the aggregate distribution of ratings. The methods proposed in this paper estimate unknown ratings by finding an optimal linear combination of individual-level and aggregate-level rating estimators in a form of a hierarchical regression (HR) model that is grounded in the theory of statistics and machine learning. The proposed HR model is general enough so that the standard individual-level recommender systems and naive aggregate methods constitute special cases of this model. We show that for the general HR model, the presence of the aggregate variance, surprisingly, does not significantly improve estimation of unknown ratings vis-a-vis the case when only aggregate average ratings are considered. In the paper, we experimentally show that the optimal linear combination approach significantly dominates all other special cases, including the classical non-aggregated case and our previously studied aggregate methods, and therefore is the method of choice.