臺灣博碩士論文加值系統

隨著網路及通訊技術的進步，始得行動資訊檢索技術得以迅速發展。如何讓使用者從大量的音樂資料中與多變的情境條件下，找出使用者自己喜好的音樂是一件困難的事情。近幾十年來，大多數的研究方向著重於協同式音樂推薦技術。由於只考量使用者的評分行為並未考量情境感知與音樂內涵，始得目前的音樂推薦技術再也無法滿足現代使用者的需求。為了解決上述的問題，在本研究中提出兼具情境感知與音樂內涵探勘之智慧型音樂推薦技術。為了有效利用複雜的音樂內涵，我們提出二階段叢集法(two-stage clustering)，藉由同時考量聽覺與時間的特性，將原先複雜的音樂內涵有效率的轉換成知覺樣式。透過情境感知探勘，使用者在相似情境底下過去評分行為會被收集並挖掘出喜好的音樂樣式(preference patterns)。藉由考量情境感知與音樂內涵，我們提出的方法可以準確的預測使用者在不同情境條件下喜好的音樂。經由實驗分析顯示，我們所提出的方法比其他推薦技術在推薦準確率上具有更高之準確度。

Advanced networking and telecommunication technologies make the ease of mobile information retrieval. However, it is hard for the users to find what she/he prefers under large music databases and variant context conditions. To solve this problem, some previous music recommenders have been proposed for providing preferable music automatically. Unfortunately, current music recommenders only based on user’s rating log cannot earn the user’s satisfaction in finding the preferred music due to the lacked consideration of context information and sufficient rating data. To solve above problems, in this thesis, we propose a novel Music Recommendation technique by integrating Context information and musical Contents (called MRCC). For context information, the users in similar context conditions are grouped to mine the similar preference patterns. For musical contents, we propose two-stage clustering to convert musical contents into perceptual patterns with considering acoustical and temporal features simultaneously. Finally, the user’s preference can be accurately predicted by integrated mining of context information and musical contents. Experimental results show that our proposed MRCC can capture the user’s preference effectively and outperform existing collaborative filtering approaches in terms of accuracy on context-aware recommendation.

中文摘要…................................................................................................. I
ABSTRACT............................................................................................. II
誌謝……….............................................................................................. IV
List of Tables..........................................................................................VII
List of Figures......................................................................................VIII
Chapter 1 Introduction ..........................................................................1
1.1 Background................................................................................................1
1.2 Motivation..................................................................................................3
1.3 Preview of Our Proposed Method..............................................................6
1.4 Contributions..............................................................................................8
1.5 Thesis Organization ...................................................................................9
Chapter 2 Related Work......................................................................10
2.1 Collaborative Filtering.............................................................................10
2.2 Content-based Filtering............................................................................16
2.3 Hybrid Collaborative Filtering and Content-based Filtering...................17
2.4 Context-Aware-based Recommendation..................................................18
Chapter 3 The Proposed Context-Aware Music Recommender......23
3.1 Overview of The Proposed Recommender ..............................................23
3.2 Preliminaries ............................................................................................26
3.3 Offline Preprocessing – Music Feature Extraction..................................29
3.4 Online Prediction .....................................................................................40
3.5 Example for Online Prediction ................................................................56
Chapter 4 Experimental Evaluation...................................................60
4.1 Experimental Environment ......................................................................60
4.2 Experimental Settings ..............................................................................67
4.3 Study of Experimental Evaluations .........................................................77
4.4 Experimental Discussions........................................................................86
Chapter 5 Conclusions and Future Work ..........................................87
5.1 Conclusion ...............................................................................................87
5.2 Future Work .............................................................................................88
5.3 Applications .............................................................................................89
Reference…..............................................................................................90
VITA……….............................................................................................97
Publications .............................................................................................98

[1]G. Adomavicius and A. Tuzhilin. “Multidimensional Recommender Systems: A Data Warehousing Approach,” In Proceedings of the 2ed International Workshop on Electronic Commerce, pages 180-192, London, UK, November 2001.
[2]G. Adomavicius, R. Sankaranarayanan, S. Sen and A. Tuzhilin. “Incorporating contextual information in recommender systems using a multidimensional approach,” ACM Transactions on Information Systems, vol. 23, no. 1, pages 103-145, January 2005.
[3]R. Agrawal, T. Imielinski and A. Swami. “Mining Association Rules between Sets of Items in Large Databases,” In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pages 207-216, Washington, D.C., USA, May 1993.
[4]Amazon, http://www.amazon.com
[5]C. Annie. “Context-aware collaborative filtering system: predicting the user's preferences in ubiquitous computing,” In Proceedings of the 2005 Conference on Human Factors in Computing Systems, pages 1110-1111, Portland, Oregon, USA, April 2005.
[6]D. Arthur and S. Vassilvitskii. “k-means++: the advantages of careful seeding,” In Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, pages 1027-1035, Philadelphia, PA, USA, January 2007.
[7]J. J. Aucouturier and F. Pachet. “Music similarity measures: What’s the use?” In Proceedings of the third International Conference on Music Information Retrieval, pages 157-163, Paris, France, October 2002.
[8]J. J. Aucouturier, P. Franois and M. Sandler. “"The way it Sounds": timbre models for analysis and retrieval of music signals,” IEEE Transactions on Multimedia, vol. 7, no. 6, pages 1028-1035, December 2005.
[9]M. A. Bartsch and G. H. Wakefield. “Audio thumbnailing of popular music using chroma-based representations,” IEEE Transactions on Multimedia, vol. 7, no. 1, pages 96-104, February 2005.
[10]J. S. Breese, D. Heckerman and C. Kadie. “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pages 43-52, Madison, Wisconsin, USA, July 1998.
[11]R. Burke. “Hybrid Recommender Systems: Survey and Experiments,” User Modeling and User-Adapted Interaction, vol. 12, no. 4, pages 331-370, November 2002.
[12]R. Cai, C. Zhang, L. Zhang and W. Y. Ma. “Scalable music recommendation by search,” In Proceedings of the 15th ACM International Conference on Multimedia, pages 1065-1074, Augsburg, Germany, September 2007.
[13]Confusion Matrix, http://en.wikipedia.org/wiki/Confusion_matrix
[14]J. Donaldson. “A hybrid social-acoustic recommendation system for popular music,” In Proceedings of the 2007 ACM Conference on Recommender Systems, pages 187-190, New York, NY, USA, October 2007.
[15]M. Ester, H. P. Kriegel, J. Sander, M. Wimmer and X. Xu. “Incremental clustering for mining in a data warehousing environment,” In Proceedings of the 24rd International Conference on Very Large Data Bases, pages 323-333, New York, USA, August 1998.
[16]Flickr, http://www.flickr.com/
[17]Google, http://www.google.com
[18]S. Guha, R. Rastogi and K. Shim. “CURE: an efficient clustering algorithms for large databases,” In Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data, pages 73-84, Seattle, Washington, USA, June 1998.
[19]J. Haitsma and T. Kalker. “A highly robust audio ngerprinting system,” In Proceedings of the 3rd International Conference on Music Information Retrieval, pages 107-115, Paris, France, October 2002.
[20]J. L. Herlocker, J. A. Konstan, A. Borchers and J. T. Riedl. “An algorithmic framework for performing collaborative filtering,” In Proceedings of the 22nd annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 230-237, Berkeley, California, USA, August 1999.
[21]J. L. Herlocker, J. A. Konstan, L. G. Terveen and J. T. Riedl. “Evaluating collaborative filtering recommender systems,” ACM Transactions on Information Systems, vol. 22, no. 1, pages 5-53, January 2004.
[22]T. Hofmann. “Latent semantic models for collaborative filtering,” ACM Transactions on Information Systems, vol. 22, no. 1, pages 89-115, January 2004.
[23]J. Hong, E. H. Suh, J. Kim and S. Kim. “Context-aware system for proactive personalized service based on context history,” Expert Systems with Applications: An International Journal, vol. 36, no. 4, pages 7448-7457, May 2009.
[24]Y. Jiao, B. Yang, M. Li and X. Niu. “MDCT-based perceptual hashing for compressed audio content identification,” In Proceedings of the 9th IEEE International Workshop on Multimedia Signal Processing, pages 381-384, Chania, Crete, Greece, October 2007.
[25]T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman and A. Y. Wu. “An efficient k-means clustering algorithm: Analysis and implementation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pages 881-892, July 2002.
[26]D. Kim, K. S. Kim, K. H. Park, J. H. Lee and K. M. Lee. “A Music Recommendation System with a Dynamic K-means Clustering Algorithm,” In Proceedings of the 6th International Conference on Machine Learning and Applications, pages 399-403, Washington, DC, USA, December 2007.
[27]P. Knees, T. Pohle, M. Schedl and G. Widmer. “Combining Audio-based Similarity with Web-based Data to Accelerate Automatic Music Playlist Generation,” In Proceedings of the 8th ACM SIGMM International Workshop on Multimedia Information Retrieval, pages 147-154, Santa Barbara, California, USA, October 2006.
[28]P. Knees, T. Pohle, M. Schedl and G. Widmer. “A Music Search Engine Built upon Audio-based and Web-based Similarity Measures,” In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 447-454, Amsterdam, the Netherlands, July 2007.
[29]Last.fm, http://last.fm.
[30]B. Logan. “MUSIC RECOMMENDATION FROM SONG SETS,” In Proceedings of the 5th International Conference on Music Information Retrieval, pages 425-428, Barcelona, Spain, October 2004.
[31]L. Lu and H. Zhang. “Automated extraction of music snippets,” In Proceedings of the 11th ACM International Conference on Multimedia, pages 140-147, Berkeley, CA, USA, November 2003.
[32]M. Mueller, F. Kurth and M. Clausen. “Audio matching via chroma-based statistical features,” In Proceedings of 6th International Conference on Music Information Retrieval, pages 288-295, London, UK, September 2005.
[33]S. B. Needleman and C. D. Wunsch. “A general method applicable to the search for similarities in the amino acid sequence of two proteins,” Journal of Molecular Biology, vol. 48, no. 3, pages 443-453, 1970.
[34]A. T. Nguyen, N. Denos and C. Berrut. “Improving New User Recommendations with Rule-based Induction on Cold User Data,” In Proceedings of the 2007 ACM Conference on Recommender Systems, pages 121-128, Minneapolis, MN, USA, October 2007.
[35]K. Oku, S. Nakajima, J. Miyazaki and S. Uemura. “Context-Aware SVM for Context-Dependent Information Recommendation,” In Proceedings of 7th International Conference on Mobile Data Management, pages 109-109, Nara, Japan, May 2006.
[36]H. S. Park, J. O. Yoo and S. B. Cho. “A context-aware music recommendation system using fuzzy bayesian networks with utility theory,” In Proceedings of 3rd International Conference on Fuzzy Sytems and Knowledge Discovery, pages 970-979, Xi'an, China, September 2006.
[37]D. Pennock, E. Horvitz, S. Lawrence and L. C. Giles. “Collaborative Filtering by Personality Diagnosis: A Hybrid Memory- and Model-based Approach,” In Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, pages 473-480, Stanford, CA, USA, July 2000.
[38]A. M. Rashid, S. K. Lam, G. Karypis and J. Riedl. “ClustKNN: a highly scalable hybrid model-& memory-based CF algorithm,” In Proceedings of the 12th KDD Workshop on Web Mining and Web Usage Analysis, Philadelphia, PA, USA, August 2006.
[39]P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom and J. Riedl. “GroupLens: An Open Architecture for Collaborative Filtering of Netnews,” In Proceedings of ACM Conference on Computer Supported Coopertative Work, pages 175-186, Chapel Hill, NC , 1994.
[40]G. Reynolds, D. Barry, T. Burke and E. Coyle. “Interacting with large music collections: Towards the use of environmental metadata,” In Proceedings of the 2008 IEEE International Conference on Multimedia and Expo, pages 989-992, Hannover, Germany, June 2008.
[41]B. M. Sarwar, G. Karypis, J. A. Konstan and J. T. Reidl. “Application of dimensionality reduction in recommender systems - A case study,” In Proceedings of the 6th KDD Workshop on Web Mining for e-commerce-Challenges and Opportunities, Boston, MA, August 2000.
[42]B. M. Sarwar, G. Karypis, J. A. Konstan and J. T. Reidl. “Item-based collaborative filtering recommendation algorithms,” In Proceedings of the 10th International Conference on World Wide Web, pages 285-295, Hong Kong, May 2001.
[43]J. Seppanen and J. Huopaniemi. “INTERACTIVE AND CONTEXT-AWARE MOBILE MUSIC EXPERIENCES,” In Proceedings of the 11th International Conference on Digital Audio Effects, Espoo, Finland, September 2008.
[44]P. Symeonidis, A. Nanopoulos, A. Papadopoulos and Y. Manolopoulos. “Nearest-Biclusters Collaborative Filtering based on Constant and Coherent Values,” Information Retrieval, vol. 11, no.1, pages 51-75, February 2008.
[45]M. Tiemann and S. Pauws. “Towards ensemble learning for hybrid music recommendation,” In Proceedings of 8th International Conference on Music Information Retrieval, pages 179, Vienna, Austria, September 2007.
[46]J. Wang, A. P. Vries and M. J. T. Reinders. “Unifying user-based and item-based collaborative filtering approaches by similarity fusion,” In Proceedings of the 29th annual international ACM SIGIR Conference on Research and Development in Information Retrieval, pages 501-508, Seattle, Washington, USA, August 2006.
[47]K. West, S. Cox and P. Lamere. “Incorporating machine-learning into music similarity estimation,” In Proceedings of the 1st ACM Workshop on Audio and Music Computing Multimedia, pages 89-96, New York, NY, USA, July 2006.
[48]G. E. Yap, A. H. Tan and H. H. Pang. “Discovering and Exploiting Causal Dependencies for Robust Mobile Context-Aware Recommenders,” IEEE Transactions on Knowledge and Data Engineering, vol. 19, no. 7, pages 977-992, July 2007.
[49]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 Proceedings of 7th International Conference on Music Information Retrieval, pages 296-301, Victoria, Canada, October 2006.
[50]Youtube, http://www.youtube.com
[51]T. Zhang, R. Ramakrishnan and M. Linvy. “BIRCH: an efficient data clustering method for very large databases,” In Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, pages 103-114, Montreal, Quebec, Canada, June 1996.
[52]J. Zobel, A. Moffat and K. Ramamohanarao. "Inverted files versus signature files for text indexing". ACM Transactions on Database Systems, vol. 23, no. 4, pages 453-490, December 1998.