Conference Proceeding Article
The fast growth of online communities and increasing popularity of internet-accessing smart devices have significantly changed the way people consume and share music. As an emerging technology to facilitate effective music retrieval on the move, intelligent recommendation has been recently received great attentions in recent years. While a large amount of efforts have been invested in the field, the technology is still in its infancy. One of the major reasons for this stagnation is due to inability of the existing approaches to comprehensively take multiple kinds of contextual information into account. In the paper, we present a novel recommender system called Just-for-Me to facilitate effective social music recommendation by considering users’ location related contexts as well as global music popularity trends. We also develop an unified recommendation model to integrate the contextual factors as well as music contents simultaneously. Furthermore, pseudo-observations are proposed to overcome the cold-start and sparsity problems. An extensive experimental study based on different test collections demonstrates that Just-for-Me system can significantly improve the recommendation performance at various geo-locations.
Music Information Retrieval, Location-Aware, Recommendation, Empirical Study
Databases and Information Systems
Data Management and Analytics
ICMR Glasgow 2014: Proceedings of the ACM International Conference on Multimedia Retrieval 2014: April 1-4, 2014, Glasgow
City or Country
CHENG, Zhiyong and Shen, Jialie.
Just-for-Me: An Adaptive Personalization System for Location-Aware Social Music Recommendation. (2014). ICMR Glasgow 2014: Proceedings of the ACM International Conference on Multimedia Retrieval 2014: April 1-4, 2014, Glasgow. 185-194. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2490
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