10.25440/smu.12310145.v1
Zhiyong CHENG
Zhiyong
CHENG
Personalized and context-aware music retrieval and recommendation
SMU Research Data Repository (RDR)
2020
context-aware
location-aware
topic model
music retrieval
music recommendation
personalization
Information Retrieval and Web Search
Decision Support and Group Support Systems
2020-05-15 08:45:02
Thesis
https://researchdata.smu.edu.sg/articles/thesis/Personalized_and_context-aware_music_retrieval_and_recommendation/12310145
<table><tr>
<td><p>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.</p></td></tr></table>