Battle of the Music Recommender Systems: User-Centered Evaluation of Collaborative Filtering, Content-Based Analysis and Hybrid 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.
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
ISBN:
Date of Publication:11/15/2007