Rapid advances in mobile devices and cloud-based music services have brought about a fundamental change in the way people consume music. Cloud-based music streaming platforms like Pandora and Last.fm host an increasing huge volume of music contents. Meanwhile, the ubiquity of wireless infrastructure and advanced mobile devices enable users to access such abundant music content anytime and anywhere. Consequently, there has been an increasing demand for the development of intelligent techniques to facilitate personalized and context-aware music retrieval and recommendation. Most of existing music retrieval systems have not considered users' music preferences, and traditional music recommender systems have not considered the influence of local contexts. As a result, search and recommendation results may not best suit users' music preference influenced by the dynamically changed contexts, when users listen to music using mobile devices on the move. Current mobile devices are equipped with various sensors and typically for personal use. Thus, rich user information (e.g., age, gender, listening logs) and various types of contexts (e.g., time. location) can be obtained and detected with the mobile devices, which provide an opportunity to develop personalized and context-aware music retrieval and recommender systems.
context-aware, location-aware, topic model, music retrieval, music recommendation, personalization
PhD in Information Systems
Theory and Algorithms
Singapore Management University
City or Country
Personalized and Context-aware Music Retrieval and Recommendation. (2016). Dissertations and Theses Collection (SMU Access Only).
Available at: http://ink.library.smu.edu.sg/etd_coll_smu/47
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