Cold Start Solutions For Recommendation Systems
Bakhshandegan Moghaddam F
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Recommendation systems are essential tools to overcome the choice overload problem by suggesting items of interest to users. However, they suffer from a major challenge which is the so-called cold-start problem. The cold-start problem typically happens when the system does not have any form of data on new users and on new items. In this chapter, we describe the cold start problem in recommendation systems. We mainly focus on Collaborative Filtering (CF) systems which are the most popular approaches to build recommender systems and have been successfully employed in many real-world applications. Moreover, we discuss multiple scenarios that cold-start may happen in these systems and explain different solutions for them.