1 / 22

Context-Aware Mobile Music Recommendation for Daily Activities

Context-Aware Mobile Music Recommendation for Daily Activities. Xinxi Wang, David Rosenblum, Ye Wang School of Computing, National University of Singapore. A simple question. Do you prefer the same or different music when running or sleeping?. Motivation - short-term needs.

kadeem
Download Presentation

Context-Aware Mobile Music Recommendation for Daily Activities

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Context-Aware Mobile Music Recommendation for Daily Activities Xinxi Wang, David Rosenblum, Ye Wang School of Computing, National University of Singapore

  2. A simple question Do you prefer the same or different music when running or sleeping?

  3. Motivation - short-term needs • Users’ short-termmusic information needs are influenced by users’ activities[A. C. North 2004] • Traditional recommender systems model user long-term preferences only Sleeping Running Girlfriend Sleepsong Girlfriend This song is generally good, but I’m going to sleep. It’s too noisy! Collaborative filtering (CF) . ..

  4. Motivation - the cold-start problem How to recommend this new song? new song problem How to recommend songs to this new user? new user problem • Collaborative Filtering (CF) cannot handle both • Content-based filtering cannot solve the new user problem

  5. The Main Idea Our system detects users’ daily activities in real-time and recommends suitable music automatically Running Sleeping … … … … • Audio content analysis • Sensor based activity detection • Personalization and adaptation

  6. System Architecture Music audio feature extraction Binaryclassifiers (Adaboost) Backend Running Walking Sleeping Music Database Working Studying Shopping Classification results Recommendation ACACF Probabilistic Graphical Model Frontend User feedback Sensor signal features feature extraction

  7. Prototype interface Activities Listened completely Ranked songs list Skipped Playback Controls automatic mode manual mode

  8. ACACF – Adaptive Context-Aware Content Filtering Given the sensor feature f, a song s is scored as: Sensor-Context Model Music-Context Model

  9. Music-Context Model Initialization • Different people have agreement on suitable music for an activity. • Initialization. Prior beta(a, b) is initialized from music content analysis results.

  10. Music-Context Model Adaptation by Approximate Inference Approximate prior update: User preference update:

  11. Sensor-Context Model Selection • Six models are compared based on three criteria: (1) Energy consumption (2) Accuracy; (3) Incremental learning. Energy consumption and incremental learning

  12. Sensor-Context Model Selection (cont) • Accuracy of different models:

  13. Sensor-Context Model Learning and Updating Naïve Bayes: Training and incremental training by MLE:

  14. Accuracy of music content analysis • Retrieval performance

  15. Recommendation Accuracy

  16. Effectiveness of Adaptation

  17. User needs study With existing technologies, their short-term needs cannot be satisfied well.

  18. Usability Study

  19. Conclusion • The first context-aware mobile music recommendation system for daily activities • It satisfies users’ short-term needs better • A solution the cold-start problem • Unified probabilistic model

  20. Future Work • Let more people use it • Exploration/exploitation tradeoff using reinforcement learning • Incorporate collaborative filtering into the system

  21. Thank You!

  22. References • A. C. North, D. J. Hargreaves, and J. J. Hargreaves, “Uses of Music in Everyday Life,” Music Perception: An Interdisciplinary Journal, vol. 22, no. 1, 2004. • A. I. Schein, A. Popescul, L. H. Ungar, and D. M. Pennock, “Methods and metrics for cold-start recommendations,” in SIGIR, 2002.

More Related