An Emotion-oriented Music Recommendation Algorithm Fusing Rating and Trust

DOI

10.1080/18756891.2013.865405How to use a DOI?

Keywords

Similarity weight, Emotion, Trust, Music recommendation

Abstract

With the overwhelming increase of music, it has become difficult to find music which suits the taste of a listener who is in a certain state of emotion. Focusing on the listener's emotional state, this paper presents an emotion-oriented music recommendation algorithm. First, the listener's similarity is calculated by the rating value and the trust value. More specifically, based on the number of ratings, two thresholds are set to extend the calculation strategy of listener similarity weight to selectively use the trust value and correlation to the rating value. Second, because the music and the listener's emotional state are different objects, there is no obvious way to match one with the other. The listener's preference for emotional connotation of music is introduced to bridge the gap between the music and the listener, and that solves the issue of how to match the listener's emotion with music. Lastly, considering the difference of listener's perception of musical content and the complexity of the listener's emotional response, we propose a comprehensive measure to evaluate the accuracy, the coverage and the listener's satisfaction degree with the recommendation. Experimental results show that the presented algorithm comparing the collaborative filtering and trust-based recommendation, results in a tiny loss of accuracy with the improvement of larger coverage, thus not only obtaining a perfect tradeoff between accuracy and coverage, but also increasing the degree of listener satisfaction.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Jiwei Qin
AU  - Qinghua Zheng
AU  - Feng Tian
AU  - Deli Zheng
PY  - 2014
DA  - 2014/04/01
TI  - An Emotion-oriented Music Recommendation Algorithm Fusing Rating and Trust
JO  - International Journal of Computational Intelligence Systems
SP  - 371
EP  - 381
VL  - 7
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.865405
DO  - 10.1080/18756891.2013.865405
ID  - Qin2014
ER  -