Battle of the Music Recommender Systems: User-Centered Evaluation of Collaborative Filtering, Content-Based Analysis and Hybrid Systems

by Fox, Alexandra E.

Abstract (Summary)
This study analyzes five music recommender systems that are also internet radio systems

from a user-centric perspective. Ten artists were chosen and ten songs allowed to play

for each artist on each of the five systems. Using a 10 point scale on each of five

attributes established by the researcher, an overall score for each system was computed

and used to rank the systems. Using the rankings, an attempt was made to establish

which method (collaborative filtering, content-based analysis, or a hybrid of the two)

provides the best music recommendations. Although the results were somewhat

inconclusive, collaborative filtering is shown to play an important role in music

recommender systems.

Bibliographical Information:

Advisor:Robert M. Losee

School:University of North Carolina at Chapel Hill

School Location:USA - North Carolina

Source Type:Master's Thesis

Keywords:music internet resources information retrieval systems recommender collaborative filtering content based analysis


Date of Publication:11/15/2007

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