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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
