@inproceedings{097d643e911647948f16205db2991587,
title = "Exploring User-Specific Information in Music Retrieval",
abstract = "With the advancement of mobile computing technology and cloud-based streaming music service, user-centered music retrieval has become increasingly important. User-specific information has a fundamental impact on personal music preferences and interests. However, existing research pays little attention to the modeling and integration of user-specific information in music retrieval algorithms/models to facilitate music search. In this paper, we propose a novel model, named User-Information-Aware Music Interest Topic (UIAMIT) model. The model is able to effectively capture the influence of user-specific information on music preferences, and further associate users' music preferences and search terms under the same latent space. Based on this model, a user information aware retrieval system is developed, which can search and re-rank the results based on age-And/or gender-specific music preferences. A comprehensive experimental study demonstrates that our methods can significantly improve the search accuracy over existing text-based music retrieval methods.",
keywords = "Reranking, Semantic Music Retrieval, Topic Model, User Demographic Information",
author = "Zhiyong Cheng and Jialie Shen and Liqiang Nie and Chua, {Tat Seng} and Mohan Kankanhalli",
year = "2017",
month = aug,
day = "7",
doi = "10.1145/3077136.3080772",
language = "English",
series = "SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval",
publisher = "Association for Computing Machinery",
pages = "655--664",
booktitle = "SIGIR 2017: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval",
address = "United States",
note = "40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017 ; Conference date: 07-08-2017 Through 11-08-2017",
}