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

  • Zeina Chedrawy
  • Syed Sibte Raza Abidi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5802)

Abstract

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.

Keywords

Web personalization Web recommender systems music recommendation semantic similarity matching 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Zeina Chedrawy
    • 1
  • Syed Sibte Raza Abidi
    • 1
  1. 1.Faculty of Computer ScienceDalhousie UniversityHalifaxCanada

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