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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林守德(Shou-De Lin) | |
| dc.contributor.author | Yin-Hsuan Wei | en |
| dc.contributor.author | 魏吟軒 | zh_TW |
| dc.date.accessioned | 2021-06-15T00:48:27Z | - |
| dc.date.available | 2011-08-17 | |
| dc.date.copyright | 2011-08-17 | |
| dc.date.issued | 2011 | |
| dc.date.submitted | 2011-08-14 | |
| dc.identifier.citation | [1] O. Celma: Music Recommendation and Discovery in the Long Tail, Springer, 2010.
[2] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl: “Item-based Collaborative Filtering Recommendation Algorithms,” Proceedings of the 10th International Conference on World Wide Web, 2001. [3] N. Lathia, S. Hailes, and L. Capra: “kNN CF: A Temporal Social Network,” ACM Conference on Recommender Systems, pp. 227–234, 2008. [4] Y. Koren, R. Bell, and C. Volinsky: “Matrix Factorization Techniques for Recommender Systems,” IEEE Computer, Vol. 42, Issue. 8, pp. 30–37, 2009. [5] S. Rendle, and L. Schmidt-Thieme: “Online-Updating Regularized Kernel Matrix Factorization Models for Large-Scale Recommender Systems,” ACM Conference on Recommender Systems, pp. 251–258, 2008. [6] Y. Koren: “Collaborative Filtering with Temporal Dynamics,” International Conference on Knowledge Discovery and Data Mining, pp. 447–456, 2009. [7] L. Baltrunas and X. Amatriain: “Towards Time-Dependent Recommendation based on Implicit Feedback,” Context-aware Recommender Systems Workshop at ACM RecSys, 2009. [8] Z. Resa: “Towards Time-aware Contextual Music Recommendation: An Exploration of Temporal Patterns of Music Listening Using Circular Statistics,” Master Thesis, 2010. [9] C. Ding, T. Li, and W. Peng: “Nonnegative Matrix Factorization and Probabilistic Latent Semantic Indexing: Equivalence, Chi-Square Statistic, and a Hybrid Method,” Proceedings of the National Conference on Artificial Intelligence, 2006. [10] G.-R. Xue, C. Lin, Q. Yang, et al.: “Scalable Collaborative Filtering Using Cluster-based Smoothing,” Proceedings of the ACM SIGIR Conference, pp. 114–121, 2005. [11] A. Flexer, D. Schnitzer, M. Gasser, and G. Widmer: “Playlist Generation using Start and End Songs,” International Society for Music Information Retrieval Conference, pp. 173–178, 2008. [12] L. Baltrunas, M. Kaminskas, F. Ricci, L. Rokach, B. Shapira, and K. Luke: “Best Usage Context Prediction for Music Tracks,” In Proceedings of the 2nd Workshop on Context Aware Recommender Systems at ACM RecSys, 2010. [13] L. Barrington, R. Oda, and G. Lanckriet: “Smarter Than Genius? Human Evaluation of Music Recommender Systems,” International Society for Music Information Retrieval Conference, 2009. [14] C.-Y. Chi, Y.-S. Wu, W.-R. Chu, D. C Wu Jr, J. Y.-J. Hsu, and R. T.-H. Tsai: “The Power of Words: Enhancing Music Mood Estimation with Textual Input of Lyrics,” International Conference on Affective Computing and Intelligent Interaction, 2009. [15] F. Maillet, D. Eck, G. Desjardins, and P. Lamere: “Steerable Playlist Generation by Learning Song Similarity from Radio Station Playlists,” International Society for Music Information Retrieval Conference, 2009. [16] S. Pauws and B. Eggen: “PATS: Realization and User Evaluation of an Automatic Playlist Generator,” International Conference on Music Information Retrieval, 2002. [17] S. Pauws and S. van de Wijdeven: “User Evaluation of a New Interactive Playlist Generation Concept,” International Society for Music Information Retrieval Conference, 2005. [18] E. Pampalk, T. Pohle, and G. Widmer: “Dynamic Playlist Generation Based on Skipping Behavior,” International Society for Music Information Retrieval Conference, 2005. [19] L. Baltrunas and F. Ricci: “Context-Dependent Items Generation in Collaborative Filtering,” Context-aware Recommender Systems Workshop at ACM RecSys, 2009. [20] Y.-X. Chen, S. Boring, and A. Butz: “How Last.fm Illustrates the Musical World: User Behavior and Relevant User-Generated Content,” Workshop on Visual Interfaces to the Social and Semantic Web, 2010. [21] C. H. Park and M. Kahng: “Temporal Dynamics in Music Listening Behavior: A Case Study of Online Music Service,” International Conference on Computer and Information Science, 2010. [22] D. Lee, S. E. Park, M. Kahng, S.-K. Lee, and S.-G. Lee: “Exploiting Contextual Information from Event Logs for Personalized Recommendation,” International Conference on Computer and Information Science, 2010. [23] B. Fields, M. Casey, K. Jacobson, and C. Rhodes: “Social Playlists and Bottleneck Measurements: Exploiting Musician Social Graphs using Content-based Dissimilarity and Pairwise Maximum Flow Values,” International Conference on Music Information Retrieval, 2008. [24] Castro, A., Vanhoof, S., Van, W., and Onghena, P.: “The Non-Transitivity of Pearson's Correlation Coefficient: An Educational Perspective,” Proceedings of the 56th Session of the International Statistical Institute, pp. 22–29, 2007. [25] R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang and C.-J. Lin: “LIBLINEAR: A Library for Large Linear Classification,” Journal of Machine Learning Research, Vol. 9, pp. 1871–1874, 2008. [26] T. Joachims: “Training Linear SVMs in Linear Time,” Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), 2006. [27] Kevin P. Murphy: “The Bayes Net Toolbox for Matlab,” Computing Science and Statistics: Proceedings of the Interface, 2001. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/42131 | - |
| dc.description.abstract | Recommendation is essential to the issue of information overload. Many literatures have proposed great models for this problem, but the majority still focuses on explicit rating data. Due to the characteristics of music, we think that implicit user feedback is more valuable and useful. In this thesis, we process the problem of recommendation in a different aspect, out of the ordinary matrix factorization or k-nearest neighbor methods. We treat recommendation as a binary classification and ranking problem, and apply features covering three parts: global, cluster-based, and temporal features. Moreover, we merge recommendation into playlist because ranking by scores is not suitable for listening. When it comes to playlist generation, most previous approaches just consider whether the playlist is smooth. However, people often listen to music when engaging in another activity, so we believe a good playlist should also be time-dependent and personalized. To achieve this goal, we construct a temporal Bayesian network to mine the listening pattern at specific time for each user. Besides, putting obvious and non-obvious recommendations together in playlist can balance the familiarity and novelty, and let it more attractive to listeners. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T00:48:27Z (GMT). No. of bitstreams: 1 ntu-100-R98922027-1.pdf: 1077674 bytes, checksum: 7969bc9bca287ca705d6fa5ad993c549 (MD5) Previous issue date: 2011 | en |
| dc.description.tableofcontents | 誌謝 ii
中文摘要 iii ABSTRACT iv CONTENTS v LIST OF FIGURES vii LIST OF TABLES viii Chapter 1 Introduction 1 1.1 Background 1 1.2 Contributions 3 1.3 Thesis Organization 4 Chapter 2 Related Work 5 2.1 Recommendation 5 2.1.1 User-based Collaborative Filtering 5 2.1.2 Matrix Factorization 6 2.1.3 Collaborative Filtering with Temporal Effect 7 2.1.4 Discussion 8 2.2 Playlist Generation 9 2.2.1 Similarity-based Approaches 9 2.2.2 Context-dependent and Personalized Playlist 10 2.2.3 Evaluation 10 Chapter 3 Methodology 12 3.1 Binary Recommendation and Ranking 12 3.1.1 Supervised Learning for Recommendation 12 3.1.2 Feature Engineering 13 3.2 Playlist Generation 19 3.2.1 Time-dependent Personalized Pattern Mining 19 3.2.2 Most probable assignment 21 Chapter 4 Experiment 22 4.1 Dataset 22 4.2 Results and Discussion 24 4.2.1 Recommendation 24 4.2.2 Playlist Generation 27 Chapter 5 Conclusion & Future Work 31 REFERENCE 32 | |
| dc.language.iso | en | |
| dc.title | 利用隱含式使用者回饋產生時間相依的個人化音樂播放清單 | zh_TW |
| dc.title | Time-dependent Personalized Music Playlist Generation from Implicit User Feedback | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 99-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 許永真(Yung-Jen Hsu),林軒田(Hsuan-Tien Lin),王新民(Hsin-Min Wang),鄭士康(Shyh-Kang Jeng) | |
| dc.subject.keyword | 音樂推薦,時間相依之播放清單, | zh_TW |
| dc.subject.keyword | music recommendation,time-dependent playlist generation, | en |
| dc.relation.page | 35 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2011-08-15 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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| ntu-100-1.pdf 目前未授權公開取用 | 1.05 MB | Adobe PDF |
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