請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80199完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳宏銘(Homer H. Chen) | |
| dc.contributor.author | Chun-Han Cheng | en |
| dc.contributor.author | 鄭群瀚 | zh_TW |
| dc.date.accessioned | 2022-11-23T09:31:25Z | - |
| dc.date.available | 2021-08-06 | |
| dc.date.available | 2022-11-23T09:31:25Z | - |
| dc.date.copyright | 2021-08-06 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-07-28 | |
| dc.identifier.citation | [1] S.-Y. Chou, Y.-H. Yang, J.-S. R. Jang, and Y.-C. Lin, “Addressing cold start for next-song recommendation,” in Proc. ACM RecSys, Boston Massachusetts USA, Sep. 2016, pp. 115–118. [2] Y. Hu, Y. Koren, and C. Volinsky, “Collaborative filtering for implicit feedback datasets,” in IEEE ICDM, Pisa, Italy, Dec. 2008, pp. 263–272. [3] M. Claypool, A. Gokhale, T. Miranda, P. Murnikov, D. Netes, and M. Sartin, “Combining content-based and collaborative filters in an online newspaper,” in Proc. of ACM SIGIR Workshop on Recommender Systems, 1999. [4] A. van den Oord, S. Dieleman, and B. Schrauwen, “Deep content-based music recommendation,” in NIPS, vol. 26, 2013, pp. 2643–2651. [5] J.-Y. Liu and Y.-H. Yang, “Event localization in music auto-tagging,” in Proc. ACM MM, Amsterdam, The Netherlands, 2016, pp. 1048–1057. [6] B. Sarwar, G. Karypis, J. Konstan, and J. Reidl, “Item-based collaborative filtering recommendation algorithms,” in Proc. WWW, Hong Kong, Hong Kong, 2001, pp. 285–295. [7] C.-W. Chen, P. Lamere, M. Schedl, and H. Zamani, “Recsys challenge 2018: automatic music playlist continuation,” in Proc. ACM RecSys, Vancouver, BC, Canada, Sep. 2018, pp. 527–528. [8] M. Volkovs, H. Rai, Z. Cheng, G. Wu, Y. Lu, and S. Sanner, “Two-stage model for automatic playlist continuation at scale,” in Proc. ACM RecSys, Vancouver, BC, Canada, 2018, pp. 1–6. [9] J. Wang, A. P. de Vries, and M. J. T. Reinders, “Unifying user-based and item-based collaborative filtering approaches by similarity fusion,” in Proc. ACM SIGIR, Seattle, Washington, USA, 2006, pp. 501–508. [10] D. Billsus and M. J. Pazzani, “A hybrid user model for news story classification,” in UM99 User Modeling, vol. 407, J. Kay, Ed. Vienna: Springer Vienna, 1999, pp. 99–108. [11] E. Law, K. West, M. I. Mandel, M. Bay, and J. S. Downie, “Evaluation of algorithms using games: The case of music tagging,” in Proc. ISMIR, 2009, pp. 387–392. [12] B. McFee, T. Bertin-Mahieux, D. P. W. Ellis, and G. R. G. Lanckriet, “The million song dataset challenge,” in Proc. WWW, Lyon, France, 2012, pp. 909–916. [13] R. Burke, “Hybrid web recommender systems,” in The Adaptive Web: Methods and Strategies of Web Personalization, P. Brusilovsky, A. Kobsa, and W. Nejdl, Eds. Berlin, Heidelberg: Springer, 2007, pp. 377–408. [14] H. Abdollahpouri, R. Burke, and B. Mobasher, “Controlling popularity bias in learning-to-rank recommendation,” in Proc. ACM RecSys, Como Italy, Aug. 2017, pp. 42–46. [15] P. Knees and M. Schedl, “A survey of music similarity and recommendation from music context data,” ACM TOMM, vol. 10, no. 1 Dec. 2013, pp. 1–21. [16] K. M. Ibrahim, J. Royo-Letelier, E. V. Epure, G. Peeters, and G. Richard, “Audio-based auto-tagging with contextual tags for music,” in ICASSP, May 2020, pp. 16–20 [17] P. B.Thorat, R. M. Goudar, and S. Barve, “Survey on collaborative filtering, content-based filtering and hybrid recommendation system,” IJCA, vol. 110, no. 4, Jan. 2015, pp. 31–36. [18] Koen Verstrepen, Kanishka Bhaduriy, Boris Cule, and Bart Goethals, “Collaborative filtering for binary, positive only data,” ACM SIGKDD Explorations Newsletter 19.1, 2017. [19] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Incremental singular value decomposition algorithms for highly scalable recommender systems,” in ICIS, 2002, pp. 27–28. [20] Charu C. Aggarwal, “An introduction to recommender systems,” in Recommender Systems, vol. 1, Cham: Springer International Publishing, 2016. [21] Bell, Robert M., Yehuda Koren, and Chris Volinsky. “The Bellkor solution to the Netflix prize,” Netflix, Los Gatos, California, USA, 2007. [22] H.-T. Cheng, L. Koc, J. Harmsen, T. Shaked, T. Chandra, H. Aradhye, G. Anderson, G. Corrado, W. Chai, M. Ispir et al., “Wide deep learning for recommender systems,” in Proceedings of the 1st workshop on deep learning for recommender systems, Jun. 2016, pp. 7–10. [23] A. Flexer, D. Schnitzer, M. Gasser, and G. Widmer, 'Playlist generation using start and end songs,' in ISMIR, vol. 8, 2008, pp. 173–178. [24] J. Lee and J. Nam, “Multi-level and multi-scale feature aggregation using pretrained convolutional neural networks for music auto-tagging,” IEEE Signal Process. Lett., vol. 24, no. 8, Aug. 2017, pp. 1208–1212. [25] D. Holtz, B. Carterette, P. Chandar, Z. Nazari, H. Cramer, and S. Aral, “The engagement-diversity connection: Evidence from a field experiment on Spotify,” in Proc. ACM Conference EC, Virtual Event Hungary, Jul. 2020, pp. 75–76. [26] H. Ma, I. King, and M. R. Lyu, “Effective missing data prediction for collaborative filtering,” in Proc. ACM SIGIR Conf. Res. Dev. Inf. Retr., 2007, pp. 39–46. [27] KOREN, Yehuda, “Factorization meets the neighborhood: a multifaceted collaborative filtering model,” in Proc. ACM SIGKDD, 2008, pp. 426–434. [28] Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” Computer, vol. 42, no. 8, Aug. 2009, pp. 30–37. [29] Charu C. Aggarwal, “On k-anonymity and the curse of dimensionality,” Proc. VLDB, 2005, pp. 901–909. [30] R. Burke, “Hybrid recommender systems: Survey and experiments,” UMUAI, vol. 12, no. 4, pp. 331–370, Nov. 2002. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80199 | - |
| dc.description.abstract | 播放清單延續對於音樂推薦而言是不可或缺的，其目標為利用歌曲資訊及歌曲與播放清單間的關係，將適合的歌曲加入播放清單內。然而，由於冷啟動歌曲的資訊稀缺性，當候選歌曲或播放清單內的既存歌曲為冷啟動歌曲時，既有的推薦系統多半表現不佳。在這篇論文中，我們利用了結合協同過濾（collaborative filtering）及內容過濾（content-based filtering）的混合模型，以回歸的方式來處理冷啟動問題。具體而言，我們將播放清單中各首歌曲的標籤視為一張二維圖片，並以周邊特徵向量利用潛藏於標籤之中的資訊，以進行總體推薦（ensemble recommendations）。為了確保順暢的聆聽體驗及增進系統表現，我們保留了原本歌曲在播放清單中的排序。我們使用了編輯者及使用者創作的播放清單來衡量此模型的性能，實驗結果顯示，該模型在含有冷啟動的資料集中以命中率作為衡量指標時，表現勝過了其他競爭模型。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-23T09:31:25Z (GMT). No. of bitstreams: 1 U0001-1906202110241900.pdf: 2462939 bytes, checksum: 8f71841962bad3982feccb40a8ce6730 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | "Chapter 1 Introduction……………………………………………………………………… 1 Chapter 2 Related Work……………………………………………………………………… 4 2.1 Collaborative Filtering………………………………………………………… 4 2.2 Content-Based Filtering………………………………………………………… 7 2.3 Hybrid Filtering…………………………………………………………………………… 7 2.4 Music Information Retrieval……………………………………………… 8 Chapter 3 Problem Formulation…………………………………………………… 9 Chapter 4 Proposed Hybrid Model…………………………………………… 12 4.1 MSM, ItemKNN, UserKNN, and SVD…………………………………… 12 4.2 Context K-Nearest-Neighborhood…………………………………… 17 4.3 Switching Mechanism………………………………………………………………… 18 Chapter 5 Experimental Setup…………………………………………………… 22 5.1 Dataset………………………………………………………………………………………………… 22 5.2 Data Preprocessing…………………………………………………………………… 24 Chapter 6 Results………………………………………………………………………………… 25 6.2 Performance Comparison………………………………………………………… 25 6.3 Discussion………………………………………………………………………………………… 31 Chapter 7 Conclusion………………………………………………………………………… 34 Reference……………………………………………………………………………………………………… 35" | |
| dc.language.iso | en | |
| dc.subject | 內容過濾 | zh_TW |
| dc.subject | 協同過濾 | zh_TW |
| dc.subject | 音樂資訊檢索 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 推薦系統 | zh_TW |
| dc.subject | 自動播放清單延續 | zh_TW |
| dc.subject | content-based filtering | en |
| dc.subject | collaborative filtering | en |
| dc.subject | automatic playlist continuation | en |
| dc.subject | recommender systems | en |
| dc.subject | machine learning | en |
| dc.subject | music information retrieval | en |
| dc.title | 利用標籤資訊之周邊資訊基準音樂播放清單延續 | zh_TW |
| dc.title | Context-Based Music Playlist Continuation Using Tag Information | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.author-orcid | 0000-0002-2865-3818 | |
| dc.contributor.oralexamcommittee | 楊奕軒(Hsin-Tsai Liu),陳怡安(Chih-Yang Tseng),蘇黎 | |
| dc.subject.keyword | 自動播放清單延續,協同過濾,內容過濾,音樂資訊檢索,機器學習,推薦系統, | zh_TW |
| dc.subject.keyword | automatic playlist continuation,collaborative filtering,content-based filtering,music information retrieval,machine learning,recommender systems, | en |
| dc.relation.page | 37 | |
| dc.identifier.doi | 10.6342/NTU202101052 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-07-29 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
| 顯示於系所單位： | 電信工程學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-1906202110241900.pdf | 2.41 MB | Adobe PDF | 檢視/開啟 |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。