Conference Proceeding Article
In this paper, we study the problem of personalized text based music retrieval which takes users' music preferences on songs into account via the analysis of online listening behaviours and social tags. Towards the goal, a novel Dual-Layer Music Preference Topic Model (DL-MPTM) is proposed to construct latent music interest space and characterize the correlations among (user, song, term). Based on the DL-MPTM, we further develop an effective personalized music retrieval system. To evaluate the system's performance, extensive experimental studies have been conducted over two test collections to compare the proposed method with the state-of-the-art music retrieval methods. The results demonstrate that our proposed method significantly outperforms those approaches in terms of personalized search accuracy.
personalized, semantic music retrieval, topic model
Computer Sciences | Databases and Information Systems
Data Management and Analytics
SIGIR 2016: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, July 17-21, Pisa, Italy
City or Country
CHENG, Zhiyong; SHEN, Jialie; and HOI, Steven C. H..
On effective personalized music retrieval via exploring online user behaviors. (2016). SIGIR 2016: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, July 17-21, Pisa, Italy. 125-134. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3417
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.