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題名 基於超連結圖譜表示法學習之跨領域音樂推薦演算法
Cross-domain music recommendation based on superhighway graph embedding作者 楊昇芳
Yang, Sheng-Fang貢獻者 蔡銘峰
Tsai, Ming-Feng
楊昇芳
Yang, Sheng-Fang關鍵詞 網路表示法
推薦系統
特徵值學習
遷移學習
Network embedding
Recommendation systems
Feature learning
Transfer learning日期 2019 上傳時間 6-十一月-2019 15:27:40 (UTC+8) 摘要 近年來大數據以及機器學習技術的蓬勃發展，推薦系統被廣泛應用於各種實務上，而音樂串流系統中的音樂推薦也變成一項具有挑戰性的工作，尤其在各個不同市場中，群體的聆聽習慣也會有所不同。因此，我們使用了異質性網路表示法學習（ Heterogeneous Information Network Embedding ），可以將網路中不同類型之節點投影於低維度向量空間中，並基於此空間來完成後續相關之音樂推薦工作。又因對於新開發市場，用戶與歌曲聆聽紀錄等互動的資訊極為稀少且會因少數用戶而影響整體推薦的傾向，這便稱為資料的「稀疏性」問題，而資料的稀疏性通常是實務上一個很具有挑戰性的任務，其對於推薦系統整體的推薦效果影響是很巨大的。於是，本論文提出了一個基於異質性網路表示法學習的音樂推薦系統，透過加入網路資訊較為豐富的市場作為輔助來幫助改進新開發市場之推薦效果。
In recent years, big data and machine learning technology have been rapidly growing, and recommendation systems have been widely used in various real-world applications, such as music recommendation in music streaming services. However, for different domains, the recommneder systems will be different, because of the distinct user behavior data. Therefore, this thesis aims to use Heterogeneous Information Network Embedding to project the nodes in a network/domain into another network/domain on the basis of the low-dimension representations of the nodes. Therefore, this paper proposes a cross-domain music recommendation approach based on heterogeneous information network representation learning, the idea of which is to enrich the new domain/market data by using a well developed domain/market.參考文獻 [1] G. Adomavicius and A. Tuzhilin. Towards the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transac- tions on Knowledge and Data Engineering, 17(6):734–749, June 2005.[2] Y. Bengio, A. Courville, and P. Vincent. Representation learning: A review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell., 35(8):1798–1828, Aug. 2013.[3] B. Bocsi, L. Csato ́, and J. Peters. Alignment-based transfer learning for robot mod- els. pages 1–7, 08 2013.[4] R. Burke. Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4):331–370, Nov. 2002.[5] C.-M. Chen, M.-F. Tsai, Y.-C. Lin, and Y.-H. Yang. Query-based music recom- mendations via preference embedding. In Proceedings of the 10th ACM Conference on Recommender Systems, RecSys ’16, pages 79–82, New York, NY, USA, 2016. ACM.[6] C.-M. Chen, Y.-H. Yang, Y. Chen, and M.-F. Tsai. Vertex-context sampling for weighted network embedding. arXiv preprint arXiv:1711.00227, 2017.[7] P. Cremonesi, A. Tripodi, and R. Turrin. Cross-domain recommender systems. InProceedings of the 2011 IEEE 11th International Conference on Data Mining Work- shops, ICDMW ’11, pages 496–503, Washington, DC, USA, 2011. IEEE Computer Society.[8] W. Dai, Q. Yang, G.-R. Xue, and Y. Yu. Self-taught clustering. In Proceedings of the 25th International Conference on Machine Learning, ICML ’08, pages 200–207, New York, NY, USA, 2008. ACM.[9] Y. Ganin and V. Lempitsky. Unsupervised domain adaptation by backpropagation. In Proceedings of the 32Nd International Conference on International Conference on Machine Learning - Volume 37, ICML’15, pages 1180–1189. JMLR.org, 2015.[10] C. Gao, X. Chen, F. Feng, K. Zhao, X. He, Y. Li, and D. Jin. Cross-domain recom- mendation without sharing user-relevant data. In The World Wide Web Conference, WWW ’19, pages 491–502, New York, NY, USA, 2019. ACM.[11] A. Grover and J. Leskovec. Node2vec: Scalable feature learning for networks. InProceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, pages 855–864, New York, NY, USA, 2016. ACM.[12] 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, ICDM ’08, pages 263–272, Washington, DC, USA, 2008. IEEE Computer Society.[13] Y.Koren,R.Bell,andC.Volinsky.Matrixfactorizationtechniquesforrecommender systems. Computer, 42(8):30–37, Aug. 2009.[14] K. Lai, T. Wang, H. Chi, Y. Chen, M. Tsai, and C. Wang. Superhighway: Bypass data sparsity in cross-domain CF. CoRR, abs/1808.09784, 2018.[15] B. Li, Q. Yang, and X. Xue. Can movies and books collaborate?: Cross-domain collaborative filtering for sparsity reduction. In Proceedings of the 21st Interna- tional Jont Conference on Artifical Intelligence, IJCAI’09, pages 2052–2057, San Francisco, CA, USA, 2009. Morgan Kaufmann Publishers Inc.[16] J. McAuley, C. Targett, Q. Shi, and A. van den Hengel. Image-based recommen- dations on styles and substitutes. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’15, pages 43–52, New York, NY, USA, 2015. ACM.[17] T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean. Distributed represen- tations of words and phrases and their compositionality. In Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2, NIPS’13, pages 3111–3119, USA, 2013. Curran Associates Inc.[18] S. J. Pan and Q. Yang. A survey on transfer learning. IEEE Trans. on Knowl. and Data Eng., 22(10):1345–1359, Oct. 2010.[19] M. J. Pazzani and D. Billsus. The adaptive web. chapter Content-based Recommen- dation Systems, pages 325–341. Springer-Verlag, Berlin, Heidelberg, 2007.[20] B. Perozzi, R. Al-Rfou, and S. Skiena. Deepwalk: Online learning of social repre- sentations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’14, pages 701–710, New York, NY, USA, 2014. ACM.[21] R. Raina, A. Battle, H. Lee, B. Packer, and A. Y. Ng. Self-taught learning: Trans- fer learning from unlabeled data. In Proceedings of the 24th International Confer- ence on Machine Learning, ICML ’07, pages 759–766, New York, NY, USA, 2007. ACM.[22] S. Rendle. Factorization machines. In Proceedings of the 2010 IEEE International Conference on Data Mining, ICDM ’10, pages 995–1000, Washington, DC, USA, 2010. IEEE Computer Society.[23] S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI ’09, pages 452–461, Ar- lington, Virginia, United States, 2009. AUAI Press.[24] P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. Grouplens: An open architecture for collaborative filtering of netnews. pages 175–186. ACM Press, 1994.[25] J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei. Line: Large-scale infor- mation 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. International World Wide Web Conferences Steering Committee.[26] Y. Zhang, Q. Ai, X. Chen, and W. B. Croft. Joint representation learning for top- n recommendation with heterogeneous information sources. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM ’17, pages 1449–1458, New York, NY, USA, 2017. ACM. 描述 碩士
國立政治大學
資訊科學系
106753011資料來源 http://thesis.lib.nccu.edu.tw/record/#G0106753011 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) 楊昇芳 zh_TW dc.contributor.author (作者) Yang, Sheng-Fang en_US dc.creator (作者) 楊昇芳 zh_TW dc.creator (作者) Yang, Sheng-Fang en_US dc.date (日期) 2019 en_US dc.date.accessioned 6-十一月-2019 15:27:40 (UTC+8) - dc.date.available 6-十一月-2019 15:27:40 (UTC+8) - dc.date.issued (上傳時間) 6-十一月-2019 15:27:40 (UTC+8) - dc.identifier (其他 識別碼) G0106753011 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/127217 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學系 zh_TW dc.description (描述) 106753011 zh_TW dc.description.abstract (摘要) 近年來大數據以及機器學習技術的蓬勃發展，推薦系統被廣泛應用於各種實務上，而音樂串流系統中的音樂推薦也變成一項具有挑戰性的工作，尤其在各個不同市場中，群體的聆聽習慣也會有所不同。因此，我們使用了異質性網路表示法學習（ Heterogeneous Information Network Embedding ），可以將網路中不同類型之節點投影於低維度向量空間中，並基於此空間來完成後續相關之音樂推薦工作。又因對於新開發市場，用戶與歌曲聆聽紀錄等互動的資訊極為稀少且會因少數用戶而影響整體推薦的傾向，這便稱為資料的「稀疏性」問題，而資料的稀疏性通常是實務上一個很具有挑戰性的任務，其對於推薦系統整體的推薦效果影響是很巨大的。於是，本論文提出了一個基於異質性網路表示法學習的音樂推薦系統，透過加入網路資訊較為豐富的市場作為輔助來幫助改進新開發市場之推薦效果。 zh_TW dc.description.abstract (摘要) In recent years, big data and machine learning technology have been rapidly growing, and recommendation systems have been widely used in various real-world applications, such as music recommendation in music streaming services. However, for different domains, the recommneder systems will be different, because of the distinct user behavior data. Therefore, this thesis aims to use Heterogeneous Information Network Embedding to project the nodes in a network/domain into another network/domain on the basis of the low-dimension representations of the nodes. Therefore, this paper proposes a cross-domain music recommendation approach based on heterogeneous information network representation learning, the idea of which is to enrich the new domain/market data by using a well developed domain/market. en_US dc.description.tableofcontents 致謝 1中文摘要 2Abstract 3第一章 緒論 11.1 前言 11.2 研究目的 2第二章 相關文獻探討 42.1 網路表示法學習 42.2 推薦系統 52.3 遷移式學習 6第三章 研究方法 83.1 問題定義 83.2 異質性網路建圖 83.3 建立超連結圖譜 103.4 網路表示法學習 123.4.1 Deepwalk 123.4.2 Large-Scale Information Network Embedding 123.4.3 HeterogeneousPreferenceEmbedding 143.5 推薦系統 14第四章 實驗結果與討論 164.1 資料集 164.2 實驗設定 174.3 評估標準 194.4 實驗結果分析與討論 214.4.1 準確率表現 214.4.2 召回率表現 214.4.3 平均準確率均值表現 224.4.4 新穎度表現 234.5 實例分析 244.5.1 實例分析-以推薦系統為例 244.5.2 實例分析-以網路表示法學習為例 24第五章 結論 29參考文獻 30 zh_TW dc.format.extent 1864696 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0106753011 en_US dc.subject (關鍵詞) 網路表示法 zh_TW dc.subject (關鍵詞) 推薦系統 zh_TW dc.subject (關鍵詞) 特徵值學習 zh_TW dc.subject (關鍵詞) 遷移學習 zh_TW dc.subject (關鍵詞) Network embedding en_US dc.subject (關鍵詞) Recommendation systems en_US dc.subject (關鍵詞) Feature learning en_US dc.subject (關鍵詞) Transfer learning en_US dc.title (題名) 基於超連結圖譜表示法學習之跨領域音樂推薦演算法 zh_TW dc.title (題名) Cross-domain music recommendation based on superhighway graph embedding en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] G. Adomavicius and A. Tuzhilin. Towards the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transac- tions on Knowledge and Data Engineering, 17(6):734–749, June 2005.[2] Y. Bengio, A. Courville, and P. Vincent. Representation learning: A review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell., 35(8):1798–1828, Aug. 2013.[3] B. Bocsi, L. Csato ́, and J. Peters. Alignment-based transfer learning for robot mod- els. pages 1–7, 08 2013.[4] R. Burke. Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4):331–370, Nov. 2002.[5] C.-M. Chen, M.-F. Tsai, Y.-C. Lin, and Y.-H. Yang. Query-based music recom- mendations via preference embedding. In Proceedings of the 10th ACM Conference on Recommender Systems, RecSys ’16, pages 79–82, New York, NY, USA, 2016. ACM.[6] C.-M. Chen, Y.-H. Yang, Y. Chen, and M.-F. Tsai. Vertex-context sampling for weighted network embedding. arXiv preprint arXiv:1711.00227, 2017.[7] P. Cremonesi, A. Tripodi, and R. Turrin. Cross-domain recommender systems. InProceedings of the 2011 IEEE 11th International Conference on Data Mining Work- shops, ICDMW ’11, pages 496–503, Washington, DC, USA, 2011. IEEE Computer Society.[8] W. Dai, Q. Yang, G.-R. Xue, and Y. Yu. Self-taught clustering. In Proceedings of the 25th International Conference on Machine Learning, ICML ’08, pages 200–207, New York, NY, USA, 2008. ACM.[9] Y. Ganin and V. Lempitsky. Unsupervised domain adaptation by backpropagation. In Proceedings of the 32Nd International Conference on International Conference on Machine Learning - Volume 37, ICML’15, pages 1180–1189. JMLR.org, 2015.[10] C. Gao, X. Chen, F. Feng, K. Zhao, X. He, Y. Li, and D. Jin. Cross-domain recom- mendation without sharing user-relevant data. In The World Wide Web Conference, WWW ’19, pages 491–502, New York, NY, USA, 2019. ACM.[11] A. Grover and J. Leskovec. Node2vec: Scalable feature learning for networks. InProceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, pages 855–864, New York, NY, USA, 2016. ACM.[12] 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, ICDM ’08, pages 263–272, Washington, DC, USA, 2008. IEEE Computer Society.[13] Y.Koren,R.Bell,andC.Volinsky.Matrixfactorizationtechniquesforrecommender systems. Computer, 42(8):30–37, Aug. 2009.[14] K. Lai, T. Wang, H. Chi, Y. Chen, M. Tsai, and C. Wang. Superhighway: Bypass data sparsity in cross-domain CF. CoRR, abs/1808.09784, 2018.[15] B. Li, Q. Yang, and X. Xue. Can movies and books collaborate?: Cross-domain collaborative filtering for sparsity reduction. In Proceedings of the 21st Interna- tional Jont Conference on Artifical Intelligence, IJCAI’09, pages 2052–2057, San Francisco, CA, USA, 2009. Morgan Kaufmann Publishers Inc.[16] J. McAuley, C. Targett, Q. Shi, and A. van den Hengel. Image-based recommen- dations on styles and substitutes. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’15, pages 43–52, New York, NY, USA, 2015. ACM.[17] T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean. Distributed represen- tations of words and phrases and their compositionality. In Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2, NIPS’13, pages 3111–3119, USA, 2013. Curran Associates Inc.[18] S. J. Pan and Q. Yang. A survey on transfer learning. IEEE Trans. on Knowl. and Data Eng., 22(10):1345–1359, Oct. 2010.[19] M. J. Pazzani and D. Billsus. The adaptive web. chapter Content-based Recommen- dation Systems, pages 325–341. Springer-Verlag, Berlin, Heidelberg, 2007.[20] B. Perozzi, R. Al-Rfou, and S. Skiena. Deepwalk: Online learning of social repre- sentations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’14, pages 701–710, New York, NY, USA, 2014. ACM.[21] R. Raina, A. Battle, H. Lee, B. Packer, and A. Y. Ng. Self-taught learning: Trans- fer learning from unlabeled data. In Proceedings of the 24th International Confer- ence on Machine Learning, ICML ’07, pages 759–766, New York, NY, USA, 2007. ACM.[22] S. Rendle. Factorization machines. In Proceedings of the 2010 IEEE International Conference on Data Mining, ICDM ’10, pages 995–1000, Washington, DC, USA, 2010. IEEE Computer Society.[23] S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI ’09, pages 452–461, Ar- lington, Virginia, United States, 2009. AUAI Press.[24] P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. Grouplens: An open architecture for collaborative filtering of netnews. pages 175–186. ACM Press, 1994.[25] J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei. Line: Large-scale infor- mation 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. International World Wide Web Conferences Steering Committee.[26] Y. Zhang, Q. Ai, X. Chen, and W. B. Croft. Joint representation learning for top- n recommendation with heterogeneous information sources. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM ’17, pages 1449–1458, New York, NY, USA, 2017. ACM. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU201901201 en_US
