A Novel Way for Personalized Music Recommendation using Social Media Tags

Rapid growth of online music content has opened new opportunities for implementing new effective information access services – commonly known as music recommender systems – that helps users filter and discover songs according to their tastes. It supports music navigation, discovery, sharing, and formation of user communities. This survey illustrates various tools and techniques that can be used for addressing recommendation systems. Recommendation techniques can be classified in to four major categories: Collaborative Filtering, Content Based, Hybrid and Context based Recommendations. This survey elaborates these approaches and discusses their limitations and advantages. In the past decade, Social Tagging Systems have attracted increasing attention and tag-aware recommender systems (Collaborative tagging) have emerged. Besides the underlying structure of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Finally, this survey focuses on tag based recommendation approach.