Abstract
Recommendation systems are a backbone for promoting products and services on social networking and e-commerce websites. Major platforms such as Netflix, Amazon Prime, Spotify, and YouTube use recommendation systems to promote different commodities. The current systems are mainly based on metadata and collaborative techniques. Over the past few years, these systems have evolved with keyword filtering, item feature-based filtering, finding aggregates, and commonalities between users. Online streaming music applications nowadays dominate music consumption. Popular websites such as YouTube or social media applications allow users to listen to songs and recommend the songs based on user history. Thus, the need is identified to provide the user personalized, enhanced musical experience. This chapter covers the overview of recommendation systems and the challenges involved. Various challenges include cold start issues, relevant data in-availability, overspecialization, lack of freshness, data sparsity, and unreliable metadata. These issues apply to recommendation systems, in general, to address user needs effectively.
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