TY - JOUR
T1 - Music Recommendation Based on Acoustic Features and User Access Patterns
AU - Shao, Bo
AU - Wang, Dingding
AU - Li, Tao
AU - Ogihara, Mitsunori
N1 - Funding Information:
Manuscript received July 16, 2008; revised February 06, 2009. Current version published September 04, 2009. The work of T. Li was supported in part by the National Sciences Foundation under Grant IIS-0546280 and in part by IBM Faculty Research Awards. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Sylvain Marchand.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2009/11
Y1 - 2009/11
N2 - Music recommendation is receiving increasing attention as the music industry develops venues to deliver music over the Internet. The goal of music recommendation is to present users lists of songs that they are likely to enjoy. Collaborative-filtering and content-based recommendations are two widely used approaches that have been proposed for music recommendation. However, both approaches have their own disadvantages: collaborative-filtering methods need a large collection of user history data and content-based methods lack the ability of understanding the interests and preferences of users. To overcome these limitations, this paper presents a novel dynamic music similarity measurement strategy that utilizes both content features and user access patterns. The seamless integration of them significantly improves the music similarity measurement accuracy and performance. Based on this strategy, recommended songs are obtained by a means of label propagation over a graph representing music similarity. Experimental results on a real data set collected from http://www.newwisdom.net demonstrate the effectiveness of the proposed approach.
AB - Music recommendation is receiving increasing attention as the music industry develops venues to deliver music over the Internet. The goal of music recommendation is to present users lists of songs that they are likely to enjoy. Collaborative-filtering and content-based recommendations are two widely used approaches that have been proposed for music recommendation. However, both approaches have their own disadvantages: collaborative-filtering methods need a large collection of user history data and content-based methods lack the ability of understanding the interests and preferences of users. To overcome these limitations, this paper presents a novel dynamic music similarity measurement strategy that utilizes both content features and user access patterns. The seamless integration of them significantly improves the music similarity measurement accuracy and performance. Based on this strategy, recommended songs are obtained by a means of label propagation over a graph representing music similarity. Experimental results on a real data set collected from http://www.newwisdom.net demonstrate the effectiveness of the proposed approach.
KW - Dynamic audio similarity
KW - music recommendation
KW - user access patterns
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U2 - 10.1109/TASL.2009.2020893
DO - 10.1109/TASL.2009.2020893
M3 - Article
AN - SCOPUS:85008010081
VL - 17
SP - 1602
EP - 1611
JO - IEEE Transactions on Speech and Audio Processing
JF - IEEE Transactions on Speech and Audio Processing
SN - 1558-7916
IS - 8
ER -