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題名 基於機器學習探討音樂多樣性推薦之研究
Exploring Diverse Music Recommendation based on Machine Learning Approaches作者 陳志明
Chen, Chih Ming貢獻者 蔡銘峰
Tsai, Ming Feng
陳志明
Chen, Chih Ming關鍵詞 推薦系統
機器學習
Recommendation System
Machine Learning日期 2013 上傳時間 1-Oct-2013 13:47:21 (UTC+8) 摘要 本論文提出了一種音樂推薦方法基於結合各種相似度資訊於分解機器（Factorization Machine）模型中。相似度的計算主要被廣泛使用於資訊檢索中，我們則是透過抽取內容與情境的相似度資訊方式，將此概念帶入至分解機器模型的架構裡，如此一來，不僅可以從大量的目標中擷取出具有相似特徵的群組，也能加速分解機器在學習中達到收斂結果的速度，這種透過結合不同數量的相似度特徵的方法還可以幫助使用者接觸到更多不同面向的物品。此外，加入大量的相似度資訊容易產生過多的計算量與雜訊，為了避免複雜度升高，我們採用了分群式的機器分解作為延伸的解決方法。在實驗裡，我們透過一個音樂資料的集合來展示我們提出的方法，此音樂資料集收集自一個線上的部落格網站，其中涵蓋了使用者聆聽音樂的記錄、使用者個人資料、社群資訊以及音樂資訊等相關內容。根據我們的實驗結果顯示，在結合各種相似度特徵的方法下，推薦的成效將會有顯著的提升，同時，調整不同數量的相似度資訊則可以單一化或者多樣化最後的推薦結果，最後，相對於傳統的協同過濾方法，在使用平均精確率平均(MeanAverage Precision)的標準之下，分群式的機器分解模型會也有顯著的成績提昇。
This paper proposes a music recommendation approach based on various similarity information via Factorization Machine (FM). We introduce the idea of similarity, which is widely studied in the filed of information retrieval, and incorporate multiple feature similarities into the FM framework, including content-based and context-based similarities. The similarity information not only captures the similar patterns from the referred objects, but enhances the convergence speed and accuracy of FM. By integrating different number of similarity features, the approach is even able to discover diverse objects that users never touched before. In addition, in order to avoid the high computational cost and noise within large similarity of features, we also adopt the grouping FM as the extended method to model the problem. In our experiments, a music-recommendation dataset is used to assess the performance of proposed approach. The dataset is collected from an online blogging website, which includes user listening history, user profiles, social information, and music information. Our experimental results show that, with the multiple feature similarities based on various types, the performance of music recommendation can be enhanced significantly. In the meantime the amount of similarity information can diversify the recommendations from a specific domain to a wide-ranging domain. Furthermore, via the grouping technique, the performance can be significant improved in terms of Mean Average Precision, compared to the traditional Collaborative Filtering approach.參考文獻 [1] Factorization machines with libfm. ACM Trans. Intell. Syst. Technol., 3(3):57:1– 57:22, May 2012.[2] R. Agrawal, S. Gollapudi, A. Halverson, and S. Ieong. Diversifying search results. In Proceedings of the Second ACM International Conference on Web Search and Data Mining, WSDM ’09, pages 5–14. ACM, 2009.[3] F.Aiolli. A preliminary study of a recommender system for the million songs dataset challenge. In Proceedings of the ECAI Workshop on Preference Learning: Problems and Application in AI, 2012.[4] M. Bradley and P. J. Lang. Affective norms for english words ANEW: Instruction manual and affective ratings. Technical report, The Center for Research in Psychophysiology, Univ. Florida, 1999.[5] R. Cai, C. Zhang, C. Wang, L. Zhang, and W.-Y. Ma. Musicsense: contextual music recommendation using emotional allocation modeling. In Proceedings of the 15th international conference on Multimedia, MULTIMEDIA ’07, pages 553–556. ACM, 2007.[6] K. Chen, T. Chen, G. Zheng, O. Jin, E. Yao, and Y. Yu. Collaborative personalized tweet recommendation. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, SIGIR ’12, pages 661–670. ACM, 2012.[7] T. Chen, L. Tang, Q. Liu, D. Yang, S. Xie, X. Cao, C. Wu, E. Yao, Z. Liu, Z. Jiang, et al. Combining factorization model and additive forest for collaborative followee recommendation.[8] J. Davidson, B. Liebald, J. Liu, P. Nandy, T. Van Vleet, U. Gargi, S. Gupta, Y. He, M. Lambert, B. Livingston, and D. Sampath. The youtube video recommendation system. In Proceedings of the fourth ACM conference on Recommender systems, RecSys ’10, pages 293–296. ACM, 2010.[9] D. T. Derek Tingle, Youngmoo E. Kim. Exploring automatic music annotation with acoustically-objective tags. pages 55–61, 2010.[10] N. Hariri, B. Mobasher, and R. Burke. Context-aware music recommendation based on latenttopic sequential patterns. In Proceedings of the sixth ACM conference on Recommender systems, RecSys ’12, pages 131–138. ACM, 2012.[11] L. Hong, A. S. Doumith, and B. D. Davison. Co-factorization machines: modeling user interests and predicting individual decisions in twitter. In Proceedings of the sixth ACM international conference on Web search and data mining, WSDM ’13, pages 557–566. ACM, 2013.[12] M. Jiang, P. Cui, R. Liu, Q. Yang, F. Wang, W. Zhu, and S. Yang. Social contextual recommendation. In Proceedings of the 21st ACM international conference on Information and knowledge management, CIKM ’12, pages 45–54. ACM, 2012.[13] M. Jiang, P. Cui, F. Wang, Q. Yang, W. Zhu, and S. Yang. Social recommendation across multiple relational domains. In Proceedings of the 21st ACM international conference on Information and knowledge management, CIKM ’12, pages 1422– 1431. ACM, 2012.[14] N. Koenigstein, G. Dror, and Y. Koren. Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy. In Proceedings of the fifth ACM conference on Recommender systems, RecSys ’11, pages 165–172. ACM, 2011.[15] Y.Koren. Collaborative filtering with temporal dynamics. In Proceedings of the15th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD ’09, pages 447–456. ACM, 2009.[16] G. Linden, B. Smith, and J. York. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7(1):76–80, Jan. 2003.[17] H. Ma, C. Liu, I. King, and M. R. Lyu. Probabilistic factor models for web site recommendation. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, SIGIR ’11, pages 265–274. ACM, 2011.[18] I. Pila ́szy and D. Tikk. Recommending new movies: even a few ratings are more valuable than metadata. In Proceedings of the third ACM conference on Recommender systems, RecSys ’09, pages 93–100. ACM, 2009.[19] S. Rendle. Bayesian factorization machines.[20] S. Rendle. Factorization machines. In Proceedings of the 10th IEEE InternationalConference on Data Mining. IEEE Computer Society, 2010.[21] S. Rendle, Z. Gantner, C. Freudenthaler, and L. Schmidt-Thieme. Fast context- aware recommendations with factorization machines. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, SIGIR ’11, pages 635–644. ACM, 2011.[22] T. Sakai. Evaluation with informational and navigational intents. In Proceedings of the 21st international conference on World Wide Web, WWW ’12, pages 499–508. ACM, 2012.[23] T. Sakai and R. Song. Evaluating diversified search results using per-intent graded relevance. In Proceedings of the 34th international ACM SIGIR conference on Re- search and development in Information Retrieval, SIGIR ’11, pages 1043–1052. ACM, 2011.[24] Y. Shen and R. Jin. Learning personal + social latent factor model for social recommendation. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD ’12, pages 1303–1311. ACM, 2012.[25] Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson, A. Hanjalic, and N. Oliver. Tfmap: optimizing map for top-n context-aware recommendation. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, SIGIR ’12, pages 155–164. ACM, 2012.[26] J. Weston, C. Wang, R. Weiss, and A. Berenzweig. Latent collaborative retrieval. CoRR, abs/1206.4603, 2012. 描述 碩士
國立政治大學
資訊科學學系
100753018
102資料來源 http://thesis.lib.nccu.edu.tw/record/#G0100753018 資料類型 thesis dc.contributor.advisor 蔡銘峰 zh_TW dc.contributor.advisor Tsai, Ming Feng en_US dc.contributor.author (Authors) 陳志明 zh_TW dc.contributor.author (Authors) Chen, Chih Ming en_US dc.creator (作者) 陳志明 zh_TW dc.creator (作者) Chen, Chih Ming en_US dc.date (日期) 2013 en_US dc.date.accessioned 1-Oct-2013 13:47:21 (UTC+8) - dc.date.available 1-Oct-2013 13:47:21 (UTC+8) - dc.date.issued (上傳時間) 1-Oct-2013 13:47:21 (UTC+8) - dc.identifier (Other Identifiers) G0100753018 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/61201 - 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) 100753018 zh_TW dc.description (描述) 102 zh_TW dc.description.abstract (摘要) 本論文提出了一種音樂推薦方法基於結合各種相似度資訊於分解機器（Factorization Machine）模型中。相似度的計算主要被廣泛使用於資訊檢索中，我們則是透過抽取內容與情境的相似度資訊方式，將此概念帶入至分解機器模型的架構裡，如此一來，不僅可以從大量的目標中擷取出具有相似特徵的群組，也能加速分解機器在學習中達到收斂結果的速度，這種透過結合不同數量的相似度特徵的方法還可以幫助使用者接觸到更多不同面向的物品。此外，加入大量的相似度資訊容易產生過多的計算量與雜訊，為了避免複雜度升高，我們採用了分群式的機器分解作為延伸的解決方法。在實驗裡，我們透過一個音樂資料的集合來展示我們提出的方法，此音樂資料集收集自一個線上的部落格網站，其中涵蓋了使用者聆聽音樂的記錄、使用者個人資料、社群資訊以及音樂資訊等相關內容。根據我們的實驗結果顯示，在結合各種相似度特徵的方法下，推薦的成效將會有顯著的提升，同時，調整不同數量的相似度資訊則可以單一化或者多樣化最後的推薦結果，最後，相對於傳統的協同過濾方法，在使用平均精確率平均(MeanAverage Precision)的標準之下，分群式的機器分解模型會也有顯著的成績提昇。 zh_TW dc.description.abstract (摘要) This paper proposes a music recommendation approach based on various similarity information via Factorization Machine (FM). We introduce the idea of similarity, which is widely studied in the filed of information retrieval, and incorporate multiple feature similarities into the FM framework, including content-based and context-based similarities. The similarity information not only captures the similar patterns from the referred objects, but enhances the convergence speed and accuracy of FM. By integrating different number of similarity features, the approach is even able to discover diverse objects that users never touched before. In addition, in order to avoid the high computational cost and noise within large similarity of features, we also adopt the grouping FM as the extended method to model the problem. In our experiments, a music-recommendation dataset is used to assess the performance of proposed approach. The dataset is collected from an online blogging website, which includes user listening history, user profiles, social information, and music information. Our experimental results show that, with the multiple feature similarities based on various types, the performance of music recommendation can be enhanced significantly. In the meantime the amount of similarity information can diversify the recommendations from a specific domain to a wide-ranging domain. Furthermore, via the grouping technique, the performance can be significant improved in terms of Mean Average Precision, compared to the traditional Collaborative Filtering approach. en_US dc.description.tableofcontents 1 Introduction 12 Related Work 5 2.1 ContextualRecommendationSystems ................... 5 2.2 MusicRecommendationSystems...................... 6 2.3 FactorizationMachines........................... 6 2.4 RecommendationDiversity......................... 73 Methodology 9 3.1 StandardFactorizationMachine ...................... 9 3.2 GroupingFactorizationMachine ...................... 9 3.3 SimilarityFramework............................ 10 3.4 ExtractedFeatures ............................. 12 3.4.1 Content-basedFeatures....................... 14 3.4.2 Context-basedFeatures....................... 154 Experimental Results 17 4.1 ExperimentalSettings............................ 17 4.1.1 Dataset ............................... 17 4.1.2 EvaluationMetrics ......................... 18 4.2 ContextualRecommendationSystem.................... 18 4.2.1 CF-basedRecommendations.................... 19 4.2.2 FMwithContent-basedFeatures.................. 20 4.2.3 FM with Content-based and Context-based Features . . . . . . . 20 4.3 SimilarityApproach............................. 21 4.3.1 UserSimilarityandItemSimilarity ................ 21 4.3.2 Content-basedfeaturesimilarity .................. 22 4.3.3 Context-basedfeaturesimilarity .................. 23 4.4 GroupingApproach............................. 24 9 4.4.1 TrainingLoss............................ 25 4.4.2 ModelComplexity ......................... 26 4.4.3 HybridRecommendations ..................... 27 4.5 RecommendationDiversity......................... 275 Conclusions 29Bibliography 31 zh_TW dc.format.extent 3940138 bytes - dc.format.mimetype application/pdf - dc.language.iso en_US - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0100753018 en_US dc.subject (關鍵詞) 推薦系統 zh_TW dc.subject (關鍵詞) 機器學習 zh_TW dc.subject (關鍵詞) Recommendation System en_US dc.subject (關鍵詞) Machine Learning en_US dc.title (題名) 基於機器學習探討音樂多樣性推薦之研究 zh_TW dc.title (題名) Exploring Diverse Music Recommendation based on Machine Learning Approaches en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) [1] Factorization machines with libfm. ACM Trans. Intell. Syst. Technol., 3(3):57:1– 57:22, May 2012.[2] R. Agrawal, S. Gollapudi, A. Halverson, and S. Ieong. Diversifying search results. In Proceedings of the Second ACM International Conference on Web Search and Data Mining, WSDM ’09, pages 5–14. ACM, 2009.[3] F.Aiolli. A preliminary study of a recommender system for the million songs dataset challenge. In Proceedings of the ECAI Workshop on Preference Learning: Problems and Application in AI, 2012.[4] M. Bradley and P. J. Lang. Affective norms for english words ANEW: Instruction manual and affective ratings. Technical report, The Center for Research in Psychophysiology, Univ. Florida, 1999.[5] R. Cai, C. Zhang, C. Wang, L. Zhang, and W.-Y. Ma. Musicsense: contextual music recommendation using emotional allocation modeling. In Proceedings of the 15th international conference on Multimedia, MULTIMEDIA ’07, pages 553–556. ACM, 2007.[6] K. Chen, T. Chen, G. Zheng, O. Jin, E. Yao, and Y. Yu. Collaborative personalized tweet recommendation. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, SIGIR ’12, pages 661–670. ACM, 2012.[7] T. Chen, L. Tang, Q. Liu, D. Yang, S. Xie, X. Cao, C. Wu, E. Yao, Z. Liu, Z. Jiang, et al. Combining factorization model and additive forest for collaborative followee recommendation.[8] J. Davidson, B. Liebald, J. Liu, P. Nandy, T. Van Vleet, U. Gargi, S. Gupta, Y. He, M. Lambert, B. Livingston, and D. Sampath. The youtube video recommendation system. In Proceedings of the fourth ACM conference on Recommender systems, RecSys ’10, pages 293–296. ACM, 2010.[9] D. T. Derek Tingle, Youngmoo E. Kim. Exploring automatic music annotation with acoustically-objective tags. pages 55–61, 2010.[10] N. Hariri, B. Mobasher, and R. Burke. Context-aware music recommendation based on latenttopic sequential patterns. In Proceedings of the sixth ACM conference on Recommender systems, RecSys ’12, pages 131–138. ACM, 2012.[11] L. Hong, A. S. Doumith, and B. D. Davison. Co-factorization machines: modeling user interests and predicting individual decisions in twitter. In Proceedings of the sixth ACM international conference on Web search and data mining, WSDM ’13, pages 557–566. ACM, 2013.[12] M. Jiang, P. Cui, R. Liu, Q. Yang, F. Wang, W. Zhu, and S. Yang. Social contextual recommendation. In Proceedings of the 21st ACM international conference on Information and knowledge management, CIKM ’12, pages 45–54. ACM, 2012.[13] M. Jiang, P. Cui, F. Wang, Q. Yang, W. Zhu, and S. Yang. Social recommendation across multiple relational domains. In Proceedings of the 21st ACM international conference on Information and knowledge management, CIKM ’12, pages 1422– 1431. ACM, 2012.[14] N. Koenigstein, G. Dror, and Y. Koren. Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy. In Proceedings of the fifth ACM conference on Recommender systems, RecSys ’11, pages 165–172. ACM, 2011.[15] Y.Koren. Collaborative filtering with temporal dynamics. In Proceedings of the15th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD ’09, pages 447–456. ACM, 2009.[16] G. Linden, B. Smith, and J. York. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7(1):76–80, Jan. 2003.[17] H. Ma, C. Liu, I. King, and M. R. Lyu. Probabilistic factor models for web site recommendation. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, SIGIR ’11, pages 265–274. ACM, 2011.[18] I. Pila ́szy and D. Tikk. Recommending new movies: even a few ratings are more valuable than metadata. In Proceedings of the third ACM conference on Recommender systems, RecSys ’09, pages 93–100. ACM, 2009.[19] S. Rendle. Bayesian factorization machines.[20] S. Rendle. Factorization machines. In Proceedings of the 10th IEEE InternationalConference on Data Mining. IEEE Computer Society, 2010.[21] S. Rendle, Z. Gantner, C. Freudenthaler, and L. Schmidt-Thieme. Fast context- aware recommendations with factorization machines. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, SIGIR ’11, pages 635–644. ACM, 2011.[22] T. Sakai. Evaluation with informational and navigational intents. In Proceedings of the 21st international conference on World Wide Web, WWW ’12, pages 499–508. ACM, 2012.[23] T. Sakai and R. Song. Evaluating diversified search results using per-intent graded relevance. In Proceedings of the 34th international ACM SIGIR conference on Re- search and development in Information Retrieval, SIGIR ’11, pages 1043–1052. ACM, 2011.[24] Y. Shen and R. Jin. Learning personal + social latent factor model for social recommendation. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD ’12, pages 1303–1311. ACM, 2012.[25] Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson, A. Hanjalic, and N. Oliver. Tfmap: optimizing map for top-n context-aware recommendation. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, SIGIR ’12, pages 155–164. ACM, 2012.[26] J. Weston, C. Wang, R. Weiss, and A. Berenzweig. Latent collaborative retrieval. CoRR, abs/1206.4603, 2012. zh_TW
