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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 謝俊霖(Choon-Ling Sia) | |
| dc.contributor.author | YUN-ZHE XIE | en |
| dc.contributor.author | 謝昀哲 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:33:13Z | - |
| dc.date.available | 2021-08-13 | |
| dc.date.available | 2022-11-24T03:33:13Z | - |
| dc.date.copyright | 2021-08-13 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-08-06 | |
| dc.identifier.citation | Ashby. (2000). A Stochastic Version of General Recognition Theory. Journal of mathematical psychology, 44 2, 310-329. Bahdanau, D., Cho, K., Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. CoRR, abs/1409.0473. Bordes, A., Usunier, N., García-Durán, A., Weston, J., Yakhnenko, O. (2013). Translating Embeddings for Modeling Multi-relational Data. NIPS, Celma, Ò. (2010). Music Recommendation and Discovery - The Long Tail, Long Fail, and Long Play in the Digital Music Space. Celma, Ò., Cano, P. (2008). From hits to niches?: or how popular artists can bias music recommendation and discovery. NETFLIX '08, Cheng, H.-T., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., Anderson, G., Corrado, G., Chai, W., Ispir, M., Anil, R., Haque, Z., Hong, L., Jain, V., Liu, X., Shah, H. (2016). Wide Deep Learning for Recommender Systems. Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. Cheng, Z., Chang, X., Zhu, L., Catherine, R., Kankanhalli, M. (2019). MMALFM: Explainable Recommendation by Leveraging Reviews and Images. ACM Trans. Inf. Syst., 37, 16:11-16:28. Coats, W. S., Feeman, V. L., Given, J., Rafter, H. D. (2000). Streaming into the Future: Music and Video Online. Loyola of Los Angeles Entertainment Law Review, 20, 285. Covington, P., Adams, J. K., Sargin, E. (2016). Deep Neural Networks for YouTube Recommendations. Proceedings of the 10th ACM Conference on Recommender Systems. Ganu, G., Elhadad, N., Marian, A. (2009). Beyond the Stars: Improving Rating Predictions using Review Text Content. WebDB, Grbovic, M., Cheng, H. (2018). Real-time Personalization using Embeddings for Search Ranking at Airbnb. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery Data Mining. Hu, R., Pu, P. (2009). Acceptance issues of personality-based recommender systems. RecSys '09, IFPI. 2020. 'Global Music Report' https://www.ifpi.org/ Mikolov, T., Chen, K., Corrado, G., Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. ICLR, North, A., Hargreaves, D., Hargreaves, J. J. (2004). Uses of Music in Everyday Life. Music Perception, 22, 41-77. North, A., Hargreaves, D., O'Neill, S. (2000). The importance of music to adolescents. The British journal of educational psychology, 70 ( Pt 2), 255-272. Oord, A. v. d., Dieleman, S., Schrauwen, B. (2013). Deep content-based music recommendation. NIPS, Pappas, N., Popescu-Belis, A. (2013). Sentiment analysis of user comments for one-class collaborative filtering over ted talks. Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. Pu, P., Chen, L., Hu, R. (2011). A user-centric evaluation framework for recommender systems. RecSys '11, Ricci, F., Rokach, L., Shapira, B. (2015). Recommender Systems: Introduction and Challenges. Recommender Systems Handbook, Schafer, J., Konstan, J., Riedl, J. (2004). E-Commerce Recommendation Applications. Data Mining and Knowledge Discovery, 5, 115-153. Schedl, M. (2019). Deep Learning in Music Recommendation Systems. Frontiers in Applied Mathematics and Statistics, 5. Tay, Y., Luu, A. T., Hui, S. C. (2018). Multi-Pointer Co-Attention Networks for Recommendation. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery Data Mining. Thomes, T. P. (2013). An economic analysis of online streaming music services. Inf. Econ. Policy, 25, 81-91. Vaswani, A., Shazeer, N. M., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., Polosukhin, I. (2017). Attention is All you Need. ArXiv, abs/1706.03762. Wang, Z., Zhang, J., Feng, J., Chen, Z. (2014). Knowledge Graph Embedding by Translating on Hyperplanes. AAAI, Yang, C.-T. (2017). Attention and Perceptual Decision Making. Zhang, Y., Ai, Q., Chen, X., Wang, P. (2018). Learning over Knowledge-Base Embeddings for Recommendation. ArXiv, abs/1803.06540. Zhang, Y., Lai, G., Zhang, M., Zhang, Y., Liu, Y., Ma, S. (2014). Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. Proceedings of the 37th international ACM SIGIR conference on Research development in information retrieval. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81152 | - |
| dc.description.abstract | 隨著線上音樂串流以及訂閱制成了人們聽音樂的主流方式之後，人們有了比以往更多的選擇，這也同時使音樂推薦系統比以往更為重要。音樂推薦系統不僅能幫助使用者在眾多選擇之中快速找到自己可能有興趣的音樂，也可以找出使用者的潛在興趣。 在我們的研究之中，我們建立了一個Embedding-based的J-pop音樂推薦系統，為了能更精確地捕捉使用者的偏好。我們以歌曲的其他資訊，例如歌手或作曲當作使用者可能會喜歡一首歌曲的原因，並且以專業領域知識考慮了這些資訊之間的關聯。除此之外，我們另外蒐集了社群媒體上的評論來代表每一首歌的客觀感受。我們發現人們經常在社群媒體上面描述一首歌曲對於他們的感受，而這些感受可以帶來與其他使用者偏好不同的資訊。在推薦的時候，我們分別考慮了使用者的長期偏好以及短期偏好。長期偏好代表了使用者喜歡的音樂風格，而短期偏好則是指出了使用者最近生活周遭發生的事會影響到近期使用者所聽的歌曲類型或主題。此外，我們也認為使用者會因為不同的原因和偏好而喜歡不同的歌曲。也就是說使用者偏好是會隨著被推薦的歌曲而有所不同的，我們將這個概念稱為選擇性偏好。 我們透過深度學習模型建立一個Embedding-based的推薦系統。該模型包含了知識圖譜及注意力機制。接著我們透過實驗切除法(ablation experiment)來評估我們實作的每個概念對模型成效的影響。實驗成功之後，我們進一步去分析模型的結果及發現來證實這些機制運作的合理性。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:33:13Z (GMT). No. of bitstreams: 1 U0001-0608202116315600.pdf: 1597374 bytes, checksum: 903a86477e34d1e071d686397f49d647 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | ACKNOWLEDGEMENT i 摘要 ii Abstract iii List of Figures vi List of Tables vii 1. Introduction 1 2. Literature review 4 2.1 Recommendation system method 5 2.1.1 Content-based recommendation 5 2.1.2 Collaborative filtering 6 2.2 Review-based 6 2.3 Embedding-based 7 2.4 Music recommendation 9 2.5 Attention 10 2.5.1 Attention in Psychology 10 2.5.2 Attention in Machine Learning 10 2.6 Knowledge graph 11 3. Methodology 13 3.1 Overview 14 3.2 Music knowledge graph and Candidate generation 15 3.3 Text information 16 3.4 Attention mechanism and ranking model 17 3.5 Evaluation metric 19 4. Data and experiments 20 4.1 Dataset 20 4.2 Knowledge graph experiment and candidate generation 21 4.3 Ranking model 22 5. Result and discussion 23 5.1 Experiment result 24 5.2 Discussion 25 5.3 Illustrative example 26 6. Conclusion 27 7. Future work 28 Reference 29 | |
| dc.language.iso | en | |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 音樂推薦系統 | zh_TW |
| dc.subject | 使用者偏好 | zh_TW |
| dc.subject | 注意力機制 | zh_TW |
| dc.subject | 社群媒體分析 | zh_TW |
| dc.subject | 知識圖譜 | zh_TW |
| dc.subject | User Preference | en |
| dc.subject | Attention Mechanism | en |
| dc.subject | Music Recommendation System | en |
| dc.subject | Machine Learning | en |
| dc.subject | Knowledge Graph | en |
| dc.subject | Social Media Analysis | en |
| dc.title | 透過社群媒體評論及選擇性使用者偏好建立 Embedding-based 音樂推薦系統 | zh_TW |
| dc.title | Embedding-based Music Recommendation System by Leveraging the Social Media Review and the Selective User Preference | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 魏志平(Hsin-Tsai Liu),彭志宏(Chih-Yang Tseng) | |
| dc.subject.keyword | 音樂推薦系統,使用者偏好,注意力機制,社群媒體分析,知識圖譜,機器學習, | zh_TW |
| dc.subject.keyword | Music Recommendation System,User Preference,Attention Mechanism,Social Media Analysis,Knowledge Graph,Machine Learning, | en |
| dc.relation.page | 32 | |
| dc.identifier.doi | 10.6342/NTU202102159 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-08-09 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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