NDLI: Evaluating music recommendation in a real-world setting: On data splitting and evaluation metrics

Content Provider	IEEE Xplore Digital Library
Author	Szu-Yu Chou Yi-Hsuan Yang Yu-Ching Lin
Copyright Year	2015
Description	Author affiliation: Res. Center for IT innovation, Taipei, Taiwan (Szu-Yu Chou; Yi-Hsuan Yang; Yu-Ching Lin)
Abstract	Evaluation is important to assess the performance of a computer system in fulfilling a certain user need. In the context of recommendation, researchers usually evaluate the performance of a recommender system by holding out a random subset of observed ratings and calculating the accuracy of the system in reproducing such ratings. This evaluation strategy, however, does not consider the fact that in a real-world setting we are actually given the observed ratings of the past and have to predict for the future. There might be new songs, which create the cold-start problem, and the users' musical preference might change over time. Moreover, the user satisfaction of a recommender system may be related to factors other than accuracy. In light of these observations, we propose in this paper a novel evaluation framework that uses various time-based data splitting methods and evaluation metrics to assess the performance of recommender systems. Using millions of listening records collected from a commercial music streaming service, we compare the performance of collaborative filtering (CF) and content-based (CB) models with low-level audio features and semantic audio descriptors. Our evaluation shows that the CB model with semantic descriptors obtains a better trade-off among accuracy, novelty, diversity, freshness and popularity, and can nicely deal with the cold-start problems of new songs.
Starting Page	1
Ending Page	6
File Size	662734
Page Count	6
File Format	PDF
ISBN	9781479970827
DOI	10.1109/ICME.2015.7177456
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-29
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mel frequency cepstral coefficient Silicon carbide Encoding Density estimation robust algorithm Computational modeling FCC evaluation metrics Collaborative filtering content-based recommendation cold-start data splitting
Content Type	Text
Resource Type	Article

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