Collaborative filtering-based recommender systems are known to suffer from the item cold-start problem. Most recent attempts to mitigate this problem presented parametric approaches, such as deep content based models. In this paper, we show that a straightforward application of parametric models may lead to discrepancies between the cold and warm items' distributions in the CF space. As a remedy, we propose to combine parametric with non-parametric estimation for robust cold item placement. Extensive evaluation indicates that our method is competitive with other baselines, while producing cold items placement that better resembles the distribution of warm items in the collaborative filtering space.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.