Cold-start Recommendations | Martin Saveski
Recommender systems suggest to users items that they might like (e.g., news articles, songs, movies) and, in doing so, they help users deal with information overload and enjoy a personalized experience. One of the main problems of these systems is the cold-start, i.e., when a new item or user is introduced in the system and no past information is available, no effective recommendation can be produced. The cold-start is a very common problem in practice: modern online platforms have hundreds of new items and millions of visits from logged-out users every day. Despite the importance of this problem not many solutions have been proposed in the literature. We contribute to closing this gap by studying whether it could be overcome without sacrificing performance. We do so by exploiting two aspects: the combination of the content and collaborative information, and the users' location.
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