ABSTRACT
Existing content-based music recommendation systems typically employ a \textit{two-stage} approach. They first extract traditional audio content features such as Mel-frequency cepstral coefficients and then predict user preferences. However, these traditional features, originally not created for music recommendation, cannot capture all relevant information in the audio and thus put a cap on recommendation performance. Using a novel model based on deep belief network and probabilistic graphical model, we unify the two stages into an automated process that simultaneously learns features from audio content and makes personalized recommendations. Compared with existing deep learning based models, our model outperforms them in both the warm-start and cold-start stages without relying on collaborative filtering (CF). We then present an efficient hybrid method to seamlessly integrate the automatically learnt features and CF. Our hybrid method not only significantly improves the performance of CF but also outperforms the traditional feature mbased hybrid method.
- P. Cano, M. Koppenberger, and N. Wack, "Content-based music audio recommendation," in Proceedings of the 13th annual ACM international conference on Multimedia, MULTIMEDIA '05, (New York, NY, USA), pp. 211--212, ACM, 2005. Google ScholarDigital Library
- K. Yoshii, M. Goto, K. Komatani, T. Ogata, and H. G. Okuno, "Hybrid Collaborative and Content-based Music Recommendation Using Probabilistic Model with Latent User Preferences," in ISMIR, pp. 296--301, 2006.Google Scholar
- X. Wang, D. Rosenblum, and Y. Wang, "Context-Aware Mobile Music Recommendation for Daily Activities," in ACM Multimedia 2012, Oct. 2012. Google ScholarDigital Library
- P. Mermelstein, "Distance measures for speech Recognition--Psychological and instrumental," in Joint Workshop on Pattern Recognition and Artificial Intelligence, 1976.Google Scholar
- M. A. Casey, R. Veltkamp, M. Goto, M. Leman, C. Rhodes, and M. Slaney, "Content-Based Music Information Retrieval: Current Directions and Future Challenges," Proceedings of the IEEE, vol. 96, pp. 668--696, Mar. 2008.Google ScholarCross Ref
- Y. Bengio, A. Courville, and P. Vincent, "Representation Learning: A Review and New Perspectives," Oct. 2012.Google Scholar
- P. Hamel and D. Eck, "Learning features from music audio with deep belief networks," in 11th International Society for Music Information Retrieval Conference, 2010.Google Scholar
- E. M. Schmidt and Y. E. Kim, "Learning emotion-based acoustic features with deep belief networks," in ISMIR, 2011.Google Scholar
- B. McFee, T. Bertin-Mahieux, D. P. W. Ellis, and G. R. G. Lanckriet, "The million song dataset challenge," in 21st International Conference Companion on World Wide Web, pp. 909--916, 2012. Google ScholarDigital Library
- G. E. Hinton, S. Osindero, and Y. W. Teh, "A fast learning algorithm for deep belief nets," Neural Comput., vol. 18, pp. 1527--1554, July 2006. Google ScholarDigital Library
- Y. Koren, R. Bell, and C. Volinsky, "Matrix Factorization Techniques for Recommender Systems," Computer, vol. 42, pp. 30--37, Aug. 2009. Google ScholarDigital Library
- H. C. Chen and A. L. P. Chen, "A Music Recommendation System Based on Music Data Grouping and User Interests," in Proceedings of the Tenth International Conference on Information and Knowledge Management, CIKM '01, (New York, NY, USA), pp. 231--238, ACM, 2001. Google ScholarDigital Library
- B. Zhang, J. Shen, Q. Xiang, and Y. Wang, "CompositeMap: a Novel Framework for Music Similarity Measure," SIGIR, 2009. Google ScholarDigital Library
- B. Logan and A. Salomon, "A Content-Based music similarity function," tech. rep., Cambridge Research Laboratory, 2001.Google Scholar
- D. Bogdanov, M. Haro, F. Fuhrmann, E. Gómez, and P. Herrera, "Content-based music recommendation based on user preference examples," in The 4th ACM Conference on Recommender Systems. Workshop on Music Recommendation and Discovery (Womrad 2010), 2010.Google Scholar
- D. Bogdanov, M. Haro, F. Fuhrmann, A. Xambó, E. GóMez, and P. Herrera, "Semantic audio content-based music recommendation and visualization based on user preference examples," Inf. Process. Manage., vol. 49, pp. 13--33, Jan. 2013. Google ScholarDigital Library
- B. McFee, L. Barrington, and G. Lanckriet, "Learning content similarity for music recommendation," Audio, Speech, and Language Processing, IEEE Transactions on, vol. 20, pp. 2207--2218, Oct. 2012. Google ScholarDigital Library
- N.-H. Liu, "Comparison of content-based music recommendation using different distance estimation methods," Applied Intelligence, vol. 38, pp. 160--174, June 2013. Google ScholarDigital Library
- M. Schedl and D. Schnitzer, "Location-Aware Music Artist Recommendation," in MultiMedia Modeling (C. Gurrin, F. Hopfgartner, W. Hurst, H. Johansen, H. Lee, and N. O'Connor, eds.), vol. 8326 of Lecture Notes in Computer Science, pp. 205--213, Springer International Publishing, 2014.Google Scholar
- I. Porteous, A. Asuncion, and M. Welling, "Bayesian Matrix Factorization with Side Information and Dirichlet Process Mixtures," in Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10).Google Scholar
- L. M. de Campos, J. M. Fernández-Luna, J. F. Huete, and M. A. Rueda-Morales, "Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks," International Journal of Approximate Reasoning, vol. 51, pp. 785--799, Sept. 2010. Google ScholarDigital Library
- H. Shan and A. Banerjee, "Generalized Probabilistic Matrix Factorizations for Collaborative Filtering," in Data Mining (ICDM), 2010 IEEE 10th International Conference on, pp. 1025--1030, IEEE, Dec. 2010. Google ScholarDigital Library
- S. Park, Y. D. Kim, and S. Choi, "Hierarchical Bayesian Matrix Factorization with Side Information," in Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI'13, pp. 1593--1599, AAAI Press, 2013. Google ScholarDigital Library
- D. Agarwal and B. C. Chen, "Regression-based latent factor models," in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '09, (New York, NY, USA), pp. 19--28, ACM, 2009. Google ScholarDigital Library
- R. Popescul and L. H. Ungar, "Probabilistic models for unified collaborative and content-based recommendation in sparsedata environments," in UAI 2011, 2001. Google ScholarDigital Library
- Q. Li, S. H. Myaeng, and B. M. Kim, "A probabilistic music recommender considering user opinions and audio features," Information Processing and Management, vol. 43, pp. 473--487, Mar. 2007. Google ScholarDigital Library
- H. S. Del Castillo, "Hybrid Content-Based Collaborative-Filtering music recommendations," Master's thesis, University of Twente, The Netherlands, 2007.Google Scholar
- M. Tiemann and S. Pauws, "Towards ensemble learning for hybrid music recommendation," in Proceedings of the 2007 ACM conference on Recommender systems, RecSys '07, (New York, NY, USA), pp. 177--178, ACM, 2007. Google ScholarDigital Library
- J. Shruthi, S. Sneha, U. R. Shetty, and D. Jayalakshmi, "A hybrid music recommender system.".Google Scholar
- J. Bu, S. Tan, C. Chen, C. Wang, H. Wu, L. Zhang, and X. He, "Music recommendation by unified hypergraph: Combining social media information and music content," in Proceedings of the International Conference on Multimedia, MM '10, (New York, NY, USA), pp. 391--400, ACM, 2010. Google ScholarDigital Library
- B. Shao, D. Wang, T. Li, and M. Ogihara, "Music Recommendation Based on Acoustic Features and User Access Patterns," Audio, Speech, and Language Processing, IEEE Transactions on, vol. 17, pp. 1602--1611, Nov. 2009. Google ScholarDigital Library
- M. A. Domingues, F. Gouyon, A. M. Jorge, J. P. Leal, J. . Vinagre, L. Lemos, and M. Sordo, "Combining usage and content in an online music recommendation system for music in the long-tail," in Proceedings of the 21st International Conference Companion on World Wide Web, WWW '12 Companion, (New York, NY, USA), pp. 925--930, ACM, 2012. Google ScholarDigital Library
- Y. Bengio, "Learning Deep Architectures for AI," Foundations and Trends in Machine Learning, vol. 2, pp. 1--127, Jan. 2009. Google ScholarDigital Library
- D. Erhan, Y. Bengio, A. Courville, P. A. Manzagol, P. Vincent, and S. Bengio, "Why Does Unsupervised Pre-training Help Deep Learning?," J. Mach. Learn. Res., vol. 11, pp. 625--660, Mar. 2010. Google ScholarDigital Library
- H. Lee, Y. Largman, P. Pham, and A. Y. Ng, "Unsupervised feature learning for audio classification using convolutional deep belief networks," in Advances in Neural Information Processing Systems, 2009.Google Scholar
- E. J. Humphrey, J. P. Bello, and Y. LeCun, "Moving Beyond Feature Design: Deep Architectures and Automatic Feature Learning in Music Informatics," in 13th International Society for Music Information Retrieval Conference, 2012.Google Scholar
- E. Humphrey, J. Bello, and Y. LeCun, "Feature learning and deep architectures: new directions for music informatics," Journal of Intelligent Information Systems, vol. 41, no. 3, pp. 461--481, 2013. Google ScholarDigital Library
- A. Pikrakis, "A deep learning approach to rhythm modelling with applications," in 6th International Workshop on Machine Learning and Music (MML13), 2013.Google Scholar
- E. M. Schmidt and Y. E. Kim, "Learning rhythm and melody features with deep belief networks," in ISMIR, 2013.Google Scholar
- M. Henaff, K. Jarrett, K. Kavukcuoglu, and Y. LeCun, "Unsupervised Learning of Sparse Features for Scalable Audio Classification," in International Society for Music Information Retrieval Conference, 2011.Google Scholar
- A. van den Oord, S. Dieleman, and B. Schrauwen, "Deep content-based music recommendation," in NIPS, 2013.Google Scholar
- R. Salakhutdinov and A. Mnih, "Probabilistic Matrix Factorization," in Advances in Neural Information Processing Systems, 2008.Google Scholar
- K. Hornik, M. Stinchcombe, and H. White, "Multilayer feedforward networks are universal approximators," Neural Networks, vol. 2, pp. 359--366, Jan. 1989. Google ScholarDigital Library
- R. Pan, Y. Zhou, B. Cao, N. N. Liu, R. Lukose, M. Scholz, and Q. Yang, "One-Class Collaborative Filtering," in Data Mining, 2008. ICDM. Eighth IEEE International Conference on, vol. 0, (Los Alamitos, CA, USA), pp. 502--511, IEEE, Dec. 2008. Google ScholarDigital Library
- Y. Hu, Y. Koren, and C. Volinsky, "Collaborative Filtering for Implicit Feedback Datasets," in Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, vol. 0 of ICDM '08, (Washington, DC, USA), pp. 263--272, IEEE Computer Society, Dec. 2008. Google ScholarDigital Library
- G. Hinton, "A Practical Guide to Training Restricted Boltzmann Machines," in Neural Networks: Tricks of the Trade (G. Montavon, G. Orr, and K.-R. Müller, eds.), vol. 7700 of Lecture Notes in Computer Science, pp. 599--619, Springer Berlin Heidelberg, 2012.Google Scholar
- G. Tzanetakis and P. Cook, "MARSYAS: a framework for audio analysis," Org. Sound, vol. 4, pp. 169--175, Dec. 1999. Google ScholarDigital Library
- M. Hermans and B. Schrauwen, "Training and analyzing deep recurrent neural networks," in Advances in Neural Information Processing Systems, 2013.Google Scholar
- J. Lee, S. Kim, G. Lebanon, and Y. Singer, "Local Low-Rank matrix approximation," in Proceedings of the 30th Annual International Conference on Machine Learning, 2013.Google Scholar
Index Terms
- Improving Content-based and Hybrid Music Recommendation using Deep Learning
Recommendations
Effective social content-based collaborative filtering for music recommendation
Recently, music recommender systems have been proposed to help users obtain the interested music. Traditional recommender systems making attempts to discover users' musical preferences by ratings always suffer from problems of rating diversity, rating ...
Music recommendation hybrid system for improving recognition ability using collaborative filtering and impression words
Music therapy for improving recognition ability may be more effective when the favorite music of each person is adopted. In the proposed system, first, the recommendation process using collaborative filtering is terminated when no users in the reference ...
Real-world mood-based music recommendation
AIRS'08: Proceedings of the 4th Asia information retrieval conference on Information retrieval technologyWe present a music recommendation system that incorporates both collaborative filtering and mood-based recommendations. The benefits of incorporating mood-based recommendations over both content/genre-based and collaborative filtering-based ...
Comments