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To personalize or not
2013
Proceedings of the 7th ACM conference on Recommender systems - RecSys '13
Personalization techniques have been widely adopted in many recommender systems. However, experiments on real-world datasets show that for some users in certain contexts, personalized recommendations do not necessarily perform better than recommendations that rely purely on popularity. Broadly, this can be interpreted by the fact that the parameters of a personalization model are usually estimated from sparse data; the resulting personalized prediction, despite of its low bias, is often
doi:10.1145/2507157.2507167
dblp:conf/recsys/ZhangWCZ13
fatcat:be33yyzfqrfbffmv3oqdkech44