Recommender systems are an important part of the information and e-commerce ecosystem. They represent a powerful method for enabling users to filter through large information and product spaces. Nearly two decades of research on collaborative filtering have led to a varied set of algorithms and a rich collection of tools for evaluating their performance. Research in the field is moving in the direction of a richer understanding of how recommender technology may be embedded in specific domains.
The differing personalities exhibited by different recommender algorithms show that recommendation is not a one-size fits-all problem. Here is the situation where personalised recommendation using collaborative filtering comes into play. We apply this collaborative filtering technique for suggesting personalised songs to user on the basis of their taste of listening we suggest user with songs on their listening of genres, artist and various other segmentations. At last we also present a statistical comparison between our model and the pre-existing popularity-based model to show how our model suggest more precise userfriendly recommendation than suggesting common songs on the basis of popularity.
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