Leveraging aggregate ratings for better recommendations
RecSys（2007）
摘要
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.
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关键词
better recommendation,aggregate rating,rating estimation error,better rating prediction,better estimation,rating estimation model,additional constraint,various category,various segment,unknown individual rating,recommender systems,predictive models,olap,recommender system,prediction model
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