Machine Learning
Demographic prediction based on user's browsing behavior
Proceedings of the 16th international conference on World Wide Web
Robust De-anonymization of Large Sparse Datasets
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
Tango or Waltz?: putting ballroom dance style into tempo detection
EURASIP Journal on Audio, Speech, and Music Processing - Intelligent Audio, Speech, and Music Processing Applications
Impact of user context on song selection
MM '08 Proceedings of the 16th ACM international conference on Multimedia
MusicBox: personalized music recommendation based on cubic analysis of social tags
IEEE Transactions on Audio, Speech, and Language Processing
Exploring automatic music annotation with "acoustically-objective" tags
Proceedings of the international conference on Multimedia information retrieval
Music Recommendation and Discovery: The Long Tail, Long Fail, and Long Play in the Digital Music Space
Web-Scale Multimedia Analysis: Does Content Matter?
IEEE MultiMedia
Music Emotion Recognition
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Information seeking: convergence of search, recommendations, and advertising
Communications of the ACM
Predicting age and gender in online social networks
Proceedings of the 3rd international workshop on Search and mining user-generated contents
Discriminating gender on Twitter
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Chroma Binary Similarity and Local Alignment Applied to Cover Song Identification
IEEE Transactions on Audio, Speech, and Language Processing
Temporal Feature Integration for Music Genre Classification
IEEE Transactions on Audio, Speech, and Language Processing
Proceedings of the 20th ACM international conference on Multimedia
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Nowadays, we often leave our personal information on the Internet without noticing it. People could learn things about you from these information. It has been reported that it is possible to infer some personal information from the web browsing records or from blog articles. As the music streaming services become increasingly popular, the music listening history of one person could be acquired easily. This paper investigates the possibility for a computer to automatically infer personal traits such as gender and age from the music listening history. Specifically, we consider three types of features for building the machine learning models, including 1) statistics of the listening timestamps, 2) song/artist metadata, and 3) song signal features, and evaluate the accuracy of binary age classification and gender classification utilizing a 1K-user dataset obtained from the online music service Last.fm. Our study brings about new insights into the human behavior of music listening, but also raises concern over the privacy issues involved in music streaming services.