Leveraging Aggregate Ratings for Better Recommendations
|Keywords:||Recommender systems;Hierarchical Bayesian models;predictive models;aggregate ratings;OLAP|
|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.|
|Appears in Collections:||CeDER Working Papers|
IOMS: Information Systems Working Papers
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