Abstract
The cold start problem is a long-standing problem in recommender systems where systems are unable to recommend relevant items to the users due to unavailability of adequate information about them. In literature, several researchers have addressed this problem by collecting missing information, but their approaches differ in the way they collect missing information. According to literature study on this problem, the solutions can be classified into two classes based on the nature of collecting missing information: (1) explicit solutions and (ii) implicit solutions. This article presents an insight on cold start problem in recommender systems and discusses techniques to deal with them.
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