A Web Recommender System for Recommending, Predicting and Personalizing Music Playlists

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.

• Type: Conference
• Year: 2009
• Where: WISE
• Authors: Zeina Chedrawy, Syed Sibte Raza Abidi
• Keywords: Computer Science, Personalized Music Playlist, Similarity Matching Method, Specific Music Playlist, WISE 2009