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 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.

Chapter Contents:

• 3.1 Introduction
• 3.1.1 Recommendation approaches
• 3.2 Collaborative filtering
• 3.3 Active learning in recommender systems
• 3.4 Semantic-based recommender systems
• 3.5 Recommendation based on visual features
• 3.6 Personality-based recommender systems
• 3.7 Cross-domain recommender systems
• 3.8 Conclusion
• References

Inspec keywords: collaborative filtering; recommender systems

Other keywords: recommendation systems; recommender systems; collaborative filtering systems; choice overload problem; cold-start problem; cold-start solutions

Subjects: Information retrieval techniques; Information networks