In this paper, we present a Web recommender system for recommending, predicting and personalizing music playlists based on a user model. We have developed a hybrid similarity matching method that combines collaborative filtering with ontology-based semantic distance measurements. We dynamically generate a personalized music playlist, from a selection of recommended playlists, which comprises the most relevant tracks to the user. Our Web recommender system features three functionalities: (1)  predict  the likability of a user towards a specific music playlist, (2)  recommend  a set of music playlists, and (3)  compose  a new personalized music playlist. Our experimental results will show the efficacy of our hybrid similarity matching approach and the information personalization method.
['Zeina Chedrawy', 'Syed Sibte Raza Abidi']
A Web Recommender System for Recommending, Predicting and Personalizing Music Playlists