A Survey of Music Recommendation System

Authors

  • Puja Deshmukh  Department of Computer Engineering, Pune University, Pune, Maharashtra, India
  • Dr. Geetanjali Kale  Department of Computer Engineering, Pune University, Pune, Maharashtra, India

Keywords:

Recommendation System, Feature Extraction, Collaborative Filtering, Content Based Filtering

Abstract

A recommendation system is a system that tries to predict the rating or preference that a user would give to any item. Recommender systems reduces human efforts by recommending items and hence have become increasingly popular in recent years and are utilized in a variety of areas including movies, music, books, research articles, social tags etc. Various techniques have been proposed for performing recommendation, including content based, collaborative, knowledge based and other techniques. To improve the performance, these methods sometimes have been combined to form hybrid recommenders. This paper surveys a general framework on the music recommendation system and various recommendation model. The main motivation behind music recommendation systems is the rapid expansion of digital music formats, managing and searching for songs relevant to the user’s requirements from a huge collection of music available since decades.

References

  1. Rentfrow, Peter J., and Samuel D. Gosling. "The do re mi's of everyday life: The structure and personality correlates of music preferences." Journal of personality and social psychology 84.6 (2003): 1236.
  2. Song, Yading, Simon Dixon, and Marcus Pearce. "A survey of music recommendation systems and future perspectives." 9th International Symposium on Computer Music Modeling and Retrieval. Vol. 4. 2012.
  3. Ricci, Francesco, Lior Rokach, and Bracha Shapira. "Introduction to recommender systems handbook." Recommender systems handbook. springer US, 2011. 1-35.
  4. Bogdanov, Dmitry, and Perfecto Herrera. "How Much Metadata Do We Need in Music Recommendation? A Subjective Evaluation Using Preference Sets." ISMIR. 2011.
  5. Burke, Robin. "Hybrid recommender systems: Survey and experiments." User modeling and user-adapted interaction 12.4 (2002): 331-370.
  6. Song, Yading, Simon Dixon, and Marcus Pearce. "A survey of music recommendation systems and future perspectives." 9th International Symposium on Computer Music Modeling and Retrieval. Vol. 4. 2012.
  7. Kim, Youngmoo E., et al. "Music emotion recognition: A state of the art review." Proc. ISMIR. 2010.
  8. Eerola, Tuomas, and Jonna K. Vuoskoski. "A comparison of the discrete and dimensional models of emotion in music." Psychology of Music 39.1 (2011): 18-49.
  9. Content Based Music Retrieval - Music Formats, Retrieval tasks, searching symbolic music, Searching musical audio, Feature extraction, Audio Fingerprinting, Concluding Remarks. Information on http://encyclopedia.jrank.org/articles/pages/669 8/Content-Based-Music-Retrieval.html.
  10. Clausen, Michael, et al. "PROMS: A Web-based Tool for Searching in Polyphonic Music." ISMIR. 2000.
  11. Kornstadt. Themefinder: A web-based melodic search tool. In W. Hewlett and E. Selfridge-Field, editors, Melodic Similarity: Concepts, Procedures, and Applications, Computing in Musicology, volume 11. MIT Press, Cambridge, 1998.
  12. S. Downie. Evaluating a simple approach to music information retrieval: Conceiving melodic n-grams as text. PhD thesis, University of Western Ontario, London, Ontario, Canada, 1999.
  13. Michael TANG, YIP Chi Lap and Ben KAO, "Selection of Melody Lines from Music Databases," IEEE Multimedia, Page 243-248,2000.
  14. Gao, Zhanchun, Yuting Liu, and Yanjun Jiang. "An effective method on content based music feature extraction." Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2015 IEEE. IEEE, 2015.
  15. Cataltepe, Zehra, and Berna Altinel. "Hybrid music recommendation based on different dimensions of audio content and an entropy measure." Signal Processing Conference, 2007 15th European. IEEE, 2007.
  16. Wang, Fei, et al. "Tag Integrated Multi-Label Music Style Classification with Hypergraph." ISMIR. 2009.
  17. Panagakis, Yannis, and Constantine Kotropoulos. "Music classification by low-rank semantic mappings." EURASIP Journal on Audio, Speech, and Music Processing 2013.1 (2013): 13.
  18. Qi, Jun, et al. "Auditory features based on gammatone filters for robust speech recognition." Circuits and Systems (ISCAS), 2013 IEEE International Symposium on. IEEE, 2013.
  19. Mesgarani, Nima, Malcolm Slaney, and Shihab A. Shamma. "Discrimination of speech from nonspeech based on multiscale spectro-temporal modulations." IEEE Transactions on Audio, Speech, and Language Processing 14.3 (2006): 920-930.
  20. Turnbull, Douglas, Luke Barrington, and Gert RG Lanckriet. "Five Approaches to Collecting Tags for Music." ISMIR. Vol. 8. 2008.
  21. Rentfrow, Peter J., and Samuel D. Gosling. "Message in a ballad: The role of music preferences in interpersonal perception." Psychological science 17.3 (2006): 236-242.
  22. O. Celma Herrada. Music Recommendation and Discovery in the Long Tail. PhD Thesis, 2009.
  23. Shao, Bo, et al. "Music recommendation based on acoustic features and user access patterns." IEEE Transactions on Audio, Speech, and Language Processing 17.8 (2009): 1602-1611.
  24. Ghias, Asif, et al. "Query by humming: musical information retrieval in an audio database." Proceedings of the third ACM international conference on Multimedia. ACM, 1995.
  25. Lamere, Paul. "Social tagging and music information retrieval." Journal of new music research 37.2 (2008): 101-114.
  26. Cano, Pedro, Markus Koppenberger, and Nicolas Wack. "Content-based music audio recommendation." Proceedings of the 13th annual ACM international conference on Multimedia. ACM, 2005.
  27. Downie, J. Stephen. "Music information retrieval." Annual review of information science and technology 37.1 (2003): 295-340.
  28. Su, Xiaoyuan, and Taghi M. Khoshgoftaar. "A survey of collaborative filtering techniques." Advances in artificial intelligence 2009 (2009): 4
  29. Nilashi, Mehrbakhsh, Othman Ibrahim, and Karamollah Bagherifard. "A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques." Expert Systems with Applications 92 (2018): 507-520.
  30. Jiang, Zhengshen, et al. "Recommendation in Heterogeneous Information Networks Based on Generalized Random Walk Model and Bayesian Personalized Ranking." Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. ACM, 2018.
  31. Appalla, Padmaja, et al. "Hybrid Fuzzy Recommendation System for Enhanced E-learning." Advances in Systems, Control and Automation. Springer, Singapore, 2018. 21-32.
  32. Herlocker, Jonathan L., et al. "Evaluating collaborative filtering recommender systems." ACM Transactions on Information Systems (TOIS) 22.1 (2004): 5-53.
  33. Ferretti, Stefano. "Clustering of Musical Pieces through Complex Networks: an Assessment over Guitar Solos." IEEE MultiMedia (2018).
  34. Wang, Chung-Che, Jyh-Shing Roger Jang, and Wennen Wang. "An Improved Query by Singing/Humming System Using Melody and Lyrics Information." ISMIR. 2010.
  35. Chen, Jinpeng, Pinguang Ying, and Ming Zou. "Improving music recommendation by incorporating social influence." Multimedia Tools and Applications (2018): 1-21.
  36. Janto Skowronek and M McKinney. Ground Truth for Automatic Music Mood Classification. In Proc. ISMIR, pages 4–5, 2006.
  37. Katarya, Rahul, and Om Prakash Verma. "Efficient music recommender system using context graph and particle swarm." Multimedia Tools and Applications 77.2 (2018): 2673-2687.
  38. Chen, Jinpeng, Pinguang Ying, and Ming Zou. "Improving music recommendation by incorporating social influence." Multimedia Tools and Applications (2018): 1-21.
  39. Wang, Dongjing, Shuiguang Deng, and Guandong Xu. "Sequence-based context-aware music recommendation." Information Retrieval Journal (2017): 1-23.
  40. Vall, Andreu, et al. "The Importance of Song Context in Music Playlists." Proceedings of the Poster Track of the 11th ACM Conference on Recommender Systems (Rec-Sys), RecSys. Vol. 17. 2017.
  41. Sassi, Imen Ben, Sadok Ben Yahia, and Sehl Mellouli. "User-based context modeling for music recommender systems." International Symposium on Methodologies for Intelligent Systems. Springer, Cham, 2017.
  42. Schedl, Markus, and Dominik Schnitzer. "Location-aware music artist recommendation." International Conference on Multimedia Modeling. Springer, Cham, 2014.
  43. Burke, Robin. "Hybrid recommender systems: Survey and experiments." User modeling and user-adapted interaction 12.4 (2002): 331-370.
  44. Katarya, Rahul, and Om Prakash Verma. "Efficient music recommender system using context graph and particle swarm." Multimedia Tools and Applications 77.2 (2018): 2673-2687.
  45. Wang, Xinxi, and Ye Wang. "Improving content-based and hybrid music recommendation using deep learning." Proceedings of the 22nd ACM international conference on Multimedia. ACM, 2014.
  46. Horsburgh, Ben, Susan Craw, and Stewart Massie. "Learning pseudo-tags to augment sparse tagging in hybrid music recommender systems." Artificial Intelligence 219 (2015): 25-39.
  47. Cheng, Rui, and Boyang Tang. "A music recommendation system based on acoustic features and user personalities." Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Cham, 2016.
  48. Deng, Shuiguang, et al. "Exploring user emotion in microblogs for music recommendation." Expert Systems with Applications 42.23 (2015): 9284-9293.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-04-30

Issue

Volume 3, Issue 3, March-April-2018

Section

Research Articles

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How to Cite

[1]
Puja Deshmukh, Dr. Geetanjali Kale, " A Survey of Music Recommendation System , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.1721-1729, March-April-2018.