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Selecting content-based features for collaborative filtering recommenders
2013
Proceedings of the 7th ACM conference on Recommender systems - RecSys '13
We study the problem of scoring and selecting content-based features for a collaborative filtering (CF) recommender system. Content-based features play a central role in mitigating the "cold start" problem in commercial recommenders. They are also useful in other related tasks, such as recommendation explanation and visualization. However, traditional feature selection methods do not generalize well to recommender systems. As a result, commercial systems typically use manually crafted and
doi:10.1145/2507157.2507203
dblp:conf/recsys/RonenKZN13
fatcat:wt5desdcdvha3mk4hlozgfpbi4