COLLABORATIVE FILTERING RECOMMENDER SYSTEMS IN MUSIC RECOMMENDATION
Urszula Kużelewska, R. Ducki · Advances in computer science research · 2013
Nowadays, the primary place of information exchange is the internet. Its features, such as: availability, unlimited capacity and diversity of information influenced its unrivalled popularity, making the internet a powerful platform for storage, dissemination and retrieval of information. On the other hand, the internet data are highly dynamic and unstructured. As a result, the internet users face the problem of data overload. Recommender systems help the users to find the products, services or information they are looking for. The article presents a recommender system for music artist recommendation. It is composed of user-based as well as item-based procedures, which can be selected dynamically during a user's session. This also includes different similarity measures. The following measures are used to assess the recommendations and adapt the appropriate procedure: RMSE, MAE, Precision and Recall. Finally, the generated recommendations and calculated similarities among artists are compared with the results from LastFM service.