學術產出-學位論文
文章檢視/開啟
書目匯出
-
題名 以使用者音樂聆聽記錄於音樂歌單推薦之研究
Learning user music listening logs for music playlist recommendation作者 楊淳堯
Yang, Chun Yao貢獻者 蔡銘峰
Tsai, Ming Feng
楊淳堯
Yang, Chun Yao關鍵詞 音樂歌單推薦
圖形嵌入式表達式
Music playlist recommendation
Graph embedding日期 2018 上傳時間 2-三月-2018 11:49:29 (UTC+8) 摘要 音樂歌單是由一組多首不同元素、風格的音樂所組成的，它包含了編輯者的個人品味以及因應主題、目的性產生而成。我們可以透過樂曲的律動、節奏、歌曲的主題精神，進而編輯一個相應契合的系列歌曲。當今的音樂收聽市場主要是在網路串流平台上進行隨時、隨地的聆聽，主要的平台有Spotify、Apple Music 以及KKBOX。各家業者不單只是提供使用者歌曲的搜索、單曲的聆聽，更提供訂閱專業歌單編輯者的歌單訂閱服務，甚至是讓一般的使用者參與歌單自訂編輯的過程。然而如何在有限的時間內針對使用者的聆聽習慣去介紹平台上豐富的音樂資源是個很大的挑戰。上述的過程我們稱之為推薦，而當前的音樂推薦研究大多是在對使用者進行相關歌曲的推薦，鮮少能進一步在更抽象層次上的歌單上進行推薦。這邊我們就此一推薦應用提供嵌入式向量表示法學習模型，在有著使用者、歌曲、歌單的異質性社交網路上，對使用者進行歌單的推薦。為了能有效的學習出歌單推薦的模型，我們更將使用者、歌單和歌曲的異質性圖形重組成二分圖（bipartite graph）， 並在此圖形的邊上賦予不等的權重，此一權重是基於使用者隱式反饋獲得的。接著再透過隨機漫步（random walk），根據邊上的權值進行路徑的抽樣選取，最後再將路徑上經過的節點進行嵌入式向量表示法的學習。我們使用歐幾里德距離計算各節點表示法的鄰近關係，再將與使用者較為相關的歌單推薦給使用者。實驗驗證的部分，我們蒐集KKBOX 兩年份的資料進行模型訓練並進行推薦，並將推薦的結果與使用者所喜愛的歌單進行準確度（Precision）評估， 結果證實所得到的推薦效果較一般熱門歌單的推薦來的好，且為更具個人化的歌單推薦。
Music playlist is crafted with a series of songs, in which the playlist creator has controlled over the vibe, tempo, theme, and all the ebbs and flows that come within the playlist. To provide a personalization service to users and discover suitable playlists among lots of data, we need an effective way to achieve this goal. In this paper, we modify a representation learning method for learning the representation of a playlist of songs, and then use the representation for recommending playlists to users. While there have been some well-known methods that can model the preference between users and songs, little has been done in the literature to recommend music playlists. In light of this, we apply DeepWalk, LINE and HPE to a user-song-playlist network. To better encode the network structure, we separate user, song, and playlist nodes into two different sets, which are grouped by the user and playlist set and song as the other one. In the bipartite graph, the user and playlist node are connected to their joint songs. By adopting random walks on the constructed graph, we can embed users and playlists via the common information between each other. Therefore, users can discover their favorite playlists through the learned representations. After the embedding process, we then use the learned representations to perform playlist recommendation task. Experiments conducted on a real-world dataset showed that these embedding methods have a better performance than the popularity baseline. In addition, the embedding method learns the informative representations and brings out the personal recommendation results.參考文獻 G. Adomavicius and A. Tuzhilin. Context-Aware Recommender Systems. Recommender Systems Handbook, Springer US, 2011, pages 217–253.J. A. Bullinaria and J. P. Levy. Extracting semantic representations from word cooccurrence statistics: a computational study. Behavior Research Methods, 39 3:510–26, 2007.C.-M. Chen, M.-F. Tsai, Y.-C. Lin, and Y.-H. Yang. Query-based music recommendations via preference embedding. In Proceedings of the 10th ACM Conference on Recommender Systems, RecSys ’16, pages 79–82. ACM, 2016.K. Choi, G. Fazekas, and M. B. Sandler. Understanding music playlists. CoRR, abs/1511.07004, 2015.J. R. Firth. A synopsis of linguistic theory 1930-55. Studies in Linguistic Analysis (special volume of the Philological Society), 1952-59:1–32, 1957.J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22(1):5–53, Jan 2004.Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In In IEEE International Conference on Data Mining (ICDM 2008, pages 263–272, 2008.A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts. Learning word vectors for sentiment analysis. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1, HLT ’11, pages 142–150. Association for Computational Linguistics, 2011.T. Mikolov, K. Chen, G. Corrado, and J. Dean. Efficient estimation of word representations in vector space. CoRR, abs/1301.3781, 2013.T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. CoRR, abs/1310.4546, 2013.T. Mikolov, W.-t. Yih, and G. Zweig. Linguistic regularities in continuous space word representations. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 746–751. Association for Computational Linguistics, 2013.B. Perozzi, R. Al-Rfou, and S. Skiena. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’14, pages 701–710. ACM, 2014.S. Rendle. Factorization machines with libfm. ACM Transactions on Intelligent Systems and Technology, 3(3):57:1–57:22, May 2012.J. D. M. Rennie and N. Srebro. Fast maximum margin matrix factorization for collaborative prediction. In Proceedings of the 22Nd International Conference on Machine Learning, ICML ’05, pages 713–719. ACM, 2005.J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei. Line: Large-scale information network embedding. In Proceedings of the 24th International Conference on World Wide Web, WWW ’15, pages 1067–1077, Republic and Canton of Geneva, Switzerland, 2015. ACM.W. Y. Zou, R. Socher, D. M. Cer, and C. D. Manning. Bilingual word embeddings for phrase-based machine translation. In EMNLP, 2013. 描述 碩士
國立政治大學
資訊科學學系
104753024資料來源 http://thesis.lib.nccu.edu.tw/record/#G0104753024 資料類型 thesis dc.contributor.advisor 蔡銘峰 zh_TW dc.contributor.advisor Tsai, Ming Feng en_US dc.contributor.author (作者) 楊淳堯 zh_TW dc.contributor.author (作者) Yang, Chun Yao en_US dc.creator (作者) 楊淳堯 zh_TW dc.creator (作者) Yang, Chun Yao en_US dc.date (日期) 2018 en_US dc.date.accessioned 2-三月-2018 11:49:29 (UTC+8) - dc.date.available 2-三月-2018 11:49:29 (UTC+8) - dc.date.issued (上傳時間) 2-三月-2018 11:49:29 (UTC+8) - dc.identifier (其他 識別碼) G0104753024 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/116079 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學學系 zh_TW dc.description (描述) 104753024 zh_TW dc.description.abstract (摘要) 音樂歌單是由一組多首不同元素、風格的音樂所組成的，它包含了編輯者的個人品味以及因應主題、目的性產生而成。我們可以透過樂曲的律動、節奏、歌曲的主題精神，進而編輯一個相應契合的系列歌曲。當今的音樂收聽市場主要是在網路串流平台上進行隨時、隨地的聆聽，主要的平台有Spotify、Apple Music 以及KKBOX。各家業者不單只是提供使用者歌曲的搜索、單曲的聆聽，更提供訂閱專業歌單編輯者的歌單訂閱服務，甚至是讓一般的使用者參與歌單自訂編輯的過程。然而如何在有限的時間內針對使用者的聆聽習慣去介紹平台上豐富的音樂資源是個很大的挑戰。上述的過程我們稱之為推薦，而當前的音樂推薦研究大多是在對使用者進行相關歌曲的推薦，鮮少能進一步在更抽象層次上的歌單上進行推薦。這邊我們就此一推薦應用提供嵌入式向量表示法學習模型，在有著使用者、歌曲、歌單的異質性社交網路上，對使用者進行歌單的推薦。為了能有效的學習出歌單推薦的模型，我們更將使用者、歌單和歌曲的異質性圖形重組成二分圖（bipartite graph）， 並在此圖形的邊上賦予不等的權重，此一權重是基於使用者隱式反饋獲得的。接著再透過隨機漫步（random walk），根據邊上的權值進行路徑的抽樣選取，最後再將路徑上經過的節點進行嵌入式向量表示法的學習。我們使用歐幾里德距離計算各節點表示法的鄰近關係，再將與使用者較為相關的歌單推薦給使用者。實驗驗證的部分，我們蒐集KKBOX 兩年份的資料進行模型訓練並進行推薦，並將推薦的結果與使用者所喜愛的歌單進行準確度（Precision）評估， 結果證實所得到的推薦效果較一般熱門歌單的推薦來的好，且為更具個人化的歌單推薦。 zh_TW dc.description.abstract (摘要) Music playlist is crafted with a series of songs, in which the playlist creator has controlled over the vibe, tempo, theme, and all the ebbs and flows that come within the playlist. To provide a personalization service to users and discover suitable playlists among lots of data, we need an effective way to achieve this goal. In this paper, we modify a representation learning method for learning the representation of a playlist of songs, and then use the representation for recommending playlists to users. While there have been some well-known methods that can model the preference between users and songs, little has been done in the literature to recommend music playlists. In light of this, we apply DeepWalk, LINE and HPE to a user-song-playlist network. To better encode the network structure, we separate user, song, and playlist nodes into two different sets, which are grouped by the user and playlist set and song as the other one. In the bipartite graph, the user and playlist node are connected to their joint songs. By adopting random walks on the constructed graph, we can embed users and playlists via the common information between each other. Therefore, users can discover their favorite playlists through the learned representations. After the embedding process, we then use the learned representations to perform playlist recommendation task. Experiments conducted on a real-world dataset showed that these embedding methods have a better performance than the popularity baseline. In addition, the embedding method learns the informative representations and brings out the personal recommendation results. en_US dc.description.tableofcontents 致謝 3中文摘要 4Abstract 51 Introduction 12 Related Work 52.1 Word Embedding 52.2 Social Network Representation 62.3 Preserving Network Structure 63 Methodology 93.1 Music Dataset and Creating the Bipartite Graph 93.2 DeepWalk 113.3 Large-Scale Information Network Embedding 123.4 Heterogeneous Preference Embedding 134 Experimental Results 174.1 Experimental Settings 174.1.1 Dataset and Ground Truth 174.1.2 Similarity Calculation 184.1.3 Evaluation Metrics 194.2 Experimental Results 204.2.1 DeepWalk 204.2.2 LINE 214.2.3 HPE 224.3 Case Study 235 Conclusions 29Bibliography 31 zh_TW dc.format.extent 1024591 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0104753024 en_US dc.subject (關鍵詞) 音樂歌單推薦 zh_TW dc.subject (關鍵詞) 圖形嵌入式表達式 zh_TW dc.subject (關鍵詞) Music playlist recommendation en_US dc.subject (關鍵詞) Graph embedding en_US dc.title (題名) 以使用者音樂聆聽記錄於音樂歌單推薦之研究 zh_TW dc.title (題名) Learning user music listening logs for music playlist recommendation en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) G. Adomavicius and A. Tuzhilin. Context-Aware Recommender Systems. Recommender Systems Handbook, Springer US, 2011, pages 217–253.J. A. Bullinaria and J. P. Levy. Extracting semantic representations from word cooccurrence statistics: a computational study. Behavior Research Methods, 39 3:510–26, 2007.C.-M. Chen, M.-F. Tsai, Y.-C. Lin, and Y.-H. Yang. Query-based music recommendations via preference embedding. In Proceedings of the 10th ACM Conference on Recommender Systems, RecSys ’16, pages 79–82. ACM, 2016.K. Choi, G. Fazekas, and M. B. Sandler. Understanding music playlists. CoRR, abs/1511.07004, 2015.J. R. Firth. A synopsis of linguistic theory 1930-55. Studies in Linguistic Analysis (special volume of the Philological Society), 1952-59:1–32, 1957.J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22(1):5–53, Jan 2004.Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In In IEEE International Conference on Data Mining (ICDM 2008, pages 263–272, 2008.A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts. Learning word vectors for sentiment analysis. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1, HLT ’11, pages 142–150. Association for Computational Linguistics, 2011.T. Mikolov, K. Chen, G. Corrado, and J. Dean. Efficient estimation of word representations in vector space. CoRR, abs/1301.3781, 2013.T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. CoRR, abs/1310.4546, 2013.T. Mikolov, W.-t. Yih, and G. Zweig. Linguistic regularities in continuous space word representations. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 746–751. Association for Computational Linguistics, 2013.B. Perozzi, R. Al-Rfou, and S. Skiena. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’14, pages 701–710. ACM, 2014.S. Rendle. Factorization machines with libfm. ACM Transactions on Intelligent Systems and Technology, 3(3):57:1–57:22, May 2012.J. D. M. Rennie and N. Srebro. Fast maximum margin matrix factorization for collaborative prediction. In Proceedings of the 22Nd International Conference on Machine Learning, ICML ’05, pages 713–719. ACM, 2005.J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei. Line: Large-scale information network embedding. In Proceedings of the 24th International Conference on World Wide Web, WWW ’15, pages 1067–1077, Republic and Canton of Geneva, Switzerland, 2015. ACM.W. Y. Zou, R. Socher, D. M. Cer, and C. D. Manning. Bilingual word embeddings for phrase-based machine translation. In EMNLP, 2013. zh_TW
