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Recommender systems are an integral part of how we experience the Web today and they have become so ubiquitous that we do not even notice them anymore. However, today's recommender systems mostly treat items they recommend as black boxes and primarily focus on extracting correlations and co-counts from user behavior data. In this talk I argue that next generation recommender systems will require deep understanding of items being recommended as well as modeling the relationships between those items. I will present examples how auxiliary data about items (descriptions, reviews, product specifications) can be used to improve recommendations.
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Jure Leskovec (2015) studied this question.