Estimate Features Relevance for Groups of Users

In item cold-start, collaborative filtering techniques cannot be used directly since newly added items have no interactions with users. Hence, content-based filtering is usually the only viable option left. In this paper we propose a feature-based machine learning model that addresses the item cold-start problem by jointly exploiting item content features, past user preferences and interactions of similar users. The proposed solution learns a relevance of each content feature referring to a community of similar users. In our experiments, the proposed approach outperforms classical content-based filtering on an enriched version of the Netflix dataset.