@inproceedings{508a183f2ad74fe99f2529eed3861f4d,
title = "Leveraging aggregate ratings for better recommendations",
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.",
keywords = "Aggregate ratings, Hierarchical Bayesian models, OLAP, Predictive models, Recommender systems",
author = "Akhmed Umyarov and Alexander Tuzhilin",
year = "2007",
doi = "10.1145/1297231.1297261",
language = "English (US)",
isbn = "9781595937308",
series = "RecSys'07: Proceedings of the 2007 ACM Conference on Recommender Systems",
pages = "161--164",
booktitle = "RecSys'07",
note = "RecSys'07: 2007 1st ACM Conference on Recommender Systems ; Conference date: 19-10-2007 Through 20-10-2007",
}