Smart-DJ: Context-Aware Personalization for Music Recommendation on Smartphones

Providing personalized content on smartphones is significant in ensuring user experience and making mobile applications profitable. The existing approaches mostly ignore the rich personalized information from user interaction with smartphones. In this paper, we address the issue of recommending personalized music to smartphone users and propose Smart-DJ. Smart-DJ incorporates an evolutionary model called Incremental Regression Tree, which incrementally collects contextual data, music data and user feedback to characterize his/her personal taste of music. An efficient recommending algorithm is designed to make accurate recommendations within bounded latency. We implement Smart-DJ and evaluate its performance through analysis and real-world experiments. The results demonstrate that Smart-DJ outperforms the state-of-arts approaches in terms of recommendation accuracy and overhead.

[1]  Francesco Ricci,et al.  Context-aware music recommender systems: workshop keynote abstract , 2012, WWW.

[2]  Sung-Bae Cho,et al.  A Context-Aware Music Recommendation System Using Fuzzy Bayesian Networks with Utility Theory , 2006, FSKD.

[3]  Gregory D. Abowd,et al.  Towards a Better Understanding of Context and Context-Awareness , 1999, HUC.

[4]  Paul E. Utgoff,et al.  Incremental Induction of Decision Trees , 1989, Machine Learning.

[5]  Francesco Ricci,et al.  Location-aware music recommendation , 2013, International Journal of Multimedia Information Retrieval.

[6]  Alfred Kobsa.  User Modeling and User-Adapted Interaction , 2005, User Modeling and User-Adapted Interaction.

[7]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[8]  Jae Sik Lee,et al.  Context Awareness by Case-Based Reasoning in a Music Recommendation System , 2007, UCS.

[9]  Tim Oates,et al.  A Human Activity Aware Learning Mobile Music Player , 2007, AITamI@IJCAI.

[10]  Donghai Guan,et al.  A music recommender based on audio features , 2004, SIGIR '04.

[11]  Francesco Ricci,et al.  Location-adapted music recommendation using tags , 2011, UMAP'11.

[12]  Chong Wang,et al.  MusicSense: contextual music recommendation using emotional allocation modeling , 2007, ACM Multimedia.

[13]  Ada Wai-Chee Fu,et al.  Dynamic vp-tree indexing for n-nearest neighbor search given pair-wise distances , 2000, The VLDB Journal.

[14]  David S. Rosenblum,et al.  Context-aware mobile music recommendation for daily activities , 2012, ACM Multimedia.

[15]  Jeroen Breebaart,et al.  Features for audio and music classification , 2003, ISMIR.

[16]  Philip S. Yu,et al.  Music Recommendation Using Content and Context Information Mining , 2010, IEEE Intelligent Systems.

[17]  Arto Lehtiniemi.  Evaluating SuperMusic: streaming context-aware mobile music service , 2008, ACE '08.

[18]  Seungmin Rho,et al.  SVR-based music mood classification and context-based music recommendation , 2009, ACM Multimedia.

[19]  Michael J. Pazzani,et al.  Content-Based Recommendation Systems , 2007, The Adaptive Web.

[20]  Guanling Chen,et al.  A Survey of Context-Aware Mobile Computing Research , 2000 .

[21]  Y. Song,et al.  A Survey of Music Recommendation Systems and Future Perspectives , 2012 .

[22]  Mehrbakhsh Nilashi,et al.  Collaborative filtering recommender systems , 2013 .

[23]  Francesco Ricci,et al.  Contextual music information retrieval and recommendation: State of the art and challenges , 2012, Comput. Sci. Rev..