Xinxi Wang
David Rosenblum
ABSTRACT
Existing music recommendation systems rely on collaborative filtering or content-based technologies to satisfy users' long-term music playing needs. Given the popularity of mobile music devices with rich sensing and wireless communication capabilities, we present in this paper a novel approach to employ contextual information collected with mobile devices for satisfying users' short-term music playing needs. We present a probabilistic model to integrate contextual information with music content analysis to offer music recommendation for daily activities, and we present a prototype implementation of the model. Finally, we present evaluation results demonstrating good accuracy and usability of the model and prototype.
- G. Adomavicius and A. Tuzhilin, "Toward the next generation of recommender systems: A survey of the State-of-the-Art and possible extensions," IEEE Trans. on Knowl. and Data Eng., vol. 17, pp. 734--749, June 2005. Google Scholar
Digital Library
- 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.Google ScholarCross Ref
- D. J. Levitin and J. McGill, "Life Soundtracks: The uses of music in everyday life." 2007.Google Scholar
- G. Reynolds, D. Barry, T. Burke, and E. Coyle, "Interacting with large music collections: Towards the use of environmental metadata," in ICME, June 2008.Google Scholar
- T. S. Saponas, J. Lester, J. Froehlich, J. Fogarty, and J. Landay, "iLearn on the iPhone: Real-Time Human Activity Classification on Commodity Mobile Phones," in UW CSE Tech Report, 2008.Google Scholar
- T. Brezmes, J.-L. Gorricho, and J. Cotrina, "Activity Recognition from Accelerometer Data on a Mobile Phone," in IWANN, 2009. Google ScholarDigital Library
- M. Berchtold, M. Budde, D. Gordon, H. R. Schmidtke, and M. Beigl, "ActiServ: Activity Recognition Service for mobile phones," in ISWC, pp. 1--8, Oct. 2010.Google Scholar
- M. Khan, S. I. Ahamed, M. Rahman, and R. O. Smith, "A Feature Extraction Method for Real time Human Activity Recognition on Cell Phones," in isQoLT, 2011.Google Scholar
- J. R. Kwapisz, G. M. Weiss, and S. A. Moore, "Activity recognition using cell phone accelerometers," SIGKDD Explor. Newsl., vol. 12, pp. 74--82, Mar. 2011. Google ScholarDigital Library
- Y. S. Lee and S. B. Cho, "Activity recognition using hierarchical hidden markov models on a smartphone with 3D accelerometer," in HAIS, pp. 460--467, 2011. Google ScholarDigital Library
- G. Wijnalda, S. Pauws, F. Vignoli, and H. Stuckenschmidt, "A Personalized Music System for Motivation in Sport Performance," IEEE Pervasive Computing, vol. 4, pp. 26--32, July 2005. Google ScholarDigital Library
- H.-S. Park, J.-O. Yoo, and S.-B. Cho, "A Context-Aware Music Recommendation System Using Fuzzy Bayesian Networks with Utility Theory," in FSKD, 2006. Google ScholarDigital Library
- J.-H. Kim, C.-W. Song, K.-W. Lim, and J.-H. Lee, "Design of Music Recommendation System Using Context Information," in LNCS, vol. 4088, ch. 83, pp. 708--713, 2006. Google ScholarDigital Library
- G. T. Elliott and B. Tomlinson, "PersonalSoundtrack: context-aware playlists that adapt to user pace," in SIGCHI, 2006. Google ScholarDigital Library
- S. Reddy and J. Mascia, "Lifetrak: music in tune with your life," in HCM, 2006. Google ScholarDigital Library
- S. Dornbush, A. Joshi, Z. Segall, and T. Oates, "A Human Activity Aware Learning Mobile Music Player," in Proc. of the 2007 conference on Advances in Ambient Intelligence, 2007. Google ScholarDigital Library
- R. D. Oliveira and N. Oliver, "TripleBeat: enhancing exercise performance with persuasion," in MobileHCI, 2008. Google ScholarDigital Library
- S. Cunningham, S. Caulder, and V. Grout, "Saturday Night or Fever? Context-Aware Music Playlists," in AM '08, 2008.Google Scholar
- A. Lehtiniemi, "Evaluating SuperMusic: streaming context-aware mobile music service," in ACE, 2008. Google ScholarDigital Library
- J. Seppänen and J. Huopaniemi, "Interactive and context-aware mobile music experiences," in DAFx-08, Sept. 2008.Google Scholar
- J. Lee and J. Lee, "Context Awareness by Case-Based Reasoning in a Music Recommendation System," Ubiquitous Computing Systems, pp. 45--58, 2008. Google ScholarDigital Library
- H. Liu, J. Hu, and M. Rauterberg, "Music Playlist Recommendation Based on User Heartbeat and Music Preference," in ICCTD, 2009. Google ScholarDigital Library
- S. Rho, B. J. Han, and E. Hwang, "SVR-based music mood classification and context-based music recommendation," in ACM MM, 2009. Google ScholarDigital Library
- A. Camurri, G. Volpe, H. Vinet, R. Bresin, M. Fabiani, G. Dubus, E. Maestre, J. Llop, J. Kleimola, S. Oksanen, V. V\"alim\"aki, and J. Seppanen, "User-Centric Context-Aware Mobile Applications for Embodied Music Listening User Centric Media," in LNICST, pp. 21--30, 2010.Google Scholar
- J.-H. Su, H.-H. Yeh, P. S. Yu, and V. S. Tseng, "Music Recommendation Using Content and Context Information Mining," Intelligent Systems, IEEE, vol. 25, pp. 16--26, Jan. 2010. Google ScholarDigital Library
- Z. Resa, "Towards Time-aware Contextual Music Recommendation: An Exploration of Temporal Patterns of Music Listening Using Circular Statistics," Master's thesis, 2010.Google Scholar
- B. J. Han, S. Rho, S. Jun, and E. Hwang, "Music emotion classification and context-based music recommendation," Multimedia Tools Appl., vol. 47, pp. 433--460, May 2010. Google ScholarDigital Library
- M. Kaminskas and F. Ricci, "Location-Adapted Music Recommendation Using Tags.," in UMAP, 2011. Google ScholarDigital Library
- D. Leake, A. Maguitman, and T. Reichherzer, "Cases, Context, and Comfort: Opportunities for Case-Based Reasoning in Smart Homes," in Designing Smart Homes, LNCS, 2006. Google ScholarDigital Library
- L. Baltrunas and X. Amatriain, "Towards Time-Dependant Recommendation based on Implicit Feedback," in CARS, 2009.Google Scholar
- A. I. Schein, A. Popescul, L. H. Ungar, and D. M. Pennock, "Methods and metrics for cold-start recommendations," in SIGIR, 2002. Google ScholarDigital Library
- Y. Hu and M. Ogihara, "Nextone player: A music recommendation system based on user behavior," in ISMIR, 2011.Google Scholar
- T. Bertin-Mahieux, D. Eck, F. Maillet, and P. Lamere, "Autotagger: A Model for Predicting Social Tags from Acoustic Features on Large Music Databases," JNMR, vol. 37, pp. 115--135, June 2008.Google ScholarCross Ref
- D. Turnbull, L. Barrington, D. Torres, and G. Lanckriet, "Towards musical query-by-semantic-description using the CAL500 data set," in SIGIR, 2007. Google ScholarDigital Library
- J. R. Landis and G. G. Koch, "The measurement of observer agreement for categorical data.," Biometrics, vol. 33, pp. 159--174, Mar. 1977.Google ScholarCross Ref
- L. Baltrunas, M. Kaminskas, F. Ricci, L. Rokach, B. Shapira, and K. H. Luke, "Best Usage Context Prediction for Music Tracks," in CARS, Sept. 2010.Google Scholar
- L. Baltrunas, M. Kaminskas, B. Ludwig, O. Moling, F. Ricci, A. Aydin, K.-H. Lüke, and R. Schwaiger, "InCarMusic: Context-Aware Music Recommendations in a Car E-Commerce and Web Technologies," in LNBIP, 2011.Google Scholar
- F. Boström, "AndroMedia - Towards a Context-aware Mobile Music Recommender," Master's thesis, 2008.Google Scholar
Index Terms
- Context-aware mobile music recommendation for daily activities
Recommendations
A daily, activity-aware, mobile music recommender system
MM '12: Proceedings of the 20th ACM international conference on MultimediaExisting music recommender systems rely on collaborative filtering or content-based technologies to satisfy users' long-term music playing needs. Given the popularity of mobile music devices with rich sensing and wireless communication capabilities, we ...
Learning to embed music and metadata for context-aware music recommendation
Contextual factors greatly influence users' musical preferences, so they are beneficial remarkably to music recommendation and retrieval tasks. However, it still needs to be studied how to obtain and utilize the contextual information. In this paper, we ...
Context-aware music recommendation in mobile smart devices
SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied ComputingMobile smart devices are now being used by many people as the primary means of listening to music. Based on the observation that a user's preferences for particular music pieces change with the contexts she/he faces, this paper proposes an approach to ...
Comments