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Proceedings of the Recommender Systems Challenge 2017 on ZZZ - RecSys Challenge '17
Cold start remains a prominent problem in recommender systems. While rich content information is often available for both users and items few existing models can fully exploit it for personalization. Slow progress in this area can be partially attributed to the lack of publicly available benchmarks to validate and compare models. This year's ACM Recommender Systems Challenge'17 aimed to address this gap by providing a standardized framework to benchmark cold start models. The challengedoi:10.1145/3124791.3124792 fatcat:kbr3n2ojhrgvdngz7uhlloakl4