政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/152130

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/152130

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | 全文筆數/總筆數 : 114401/145431 (79%)
造訪人次 : 53151669 線上人數 : 823

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

搜尋範圍

查詢小技巧：

您可在西文檢索詞彙前後加上"雙引號"，以獲取較精準的檢索結果

若欲以作者姓名搜尋，建議至進階搜尋限定作者欄位，可獲得較完整資料

進階搜尋

主頁 ‧ 登入 ‧ 上傳 ‧ 說明 ‧ 關於政大典藏 ‧ 管理

到手機版

政大機構典藏 > 商學院 > 統計學系 > 學位論文 > Item 140.119/152130

請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/152130

題名:	推薦系統的類別特徵工程基於熵驅動的優化 Entropy-driven Optimization of Recommendation Systems through Categorical Feature Engineering
作者:	鄭竣鴻 Zheng, Jun-Hong
貢獻者:	周珮婷張育瑋 Chou, Pei-Ting Chang, Yu-Wei 鄭竣鴻 Zheng, Jun-Hong
關鍵詞:	類別變數特徵篩選條件熵推薦系統機器學習 Categorical variable Feature selection Conditional entropy Recommendation system Machine learning
日期:	2024
上傳時間:	2024-07-01 13:27:41 (UTC+8)
摘要:	特徵篩選在機器學習中扮演著關鍵角色，它有助於提高模型的準確性和效率，而條件熵是信息理論中一個用於評估特徵相關性的指標，它考慮了特徵之間的條件關係，有助於發現與目標變量密切相關的特徵。本研究旨在探討條件熵作為特徵篩選方法在大量類別型變數資料集的應用。以KKbox音樂資料集為例，利用條件熵在類別變數特徵篩選後的結果，評估篩選後的特徵集對模型性能的影響。我們的實驗結果顯示，我們能夠獲得一個具有較少特徵但仍具有良好性能的模型。這表明條件熵可以作為一種有效的特徵篩選方法，幫助我們發現與用戶聽歌行為密切相關的特徵，從而簡化大量資料集並提升模型的運算效率。 Feature selection plays a crucial role in machine learning as it helps enhance the accuracy and efficiency of models. Conditional entropy is an index from information theory used to evaluate the relevance of features, considering the conditional relationships between them. This helps in identifying features that are closely related to the target variable. This study aims to explore the application of conditional entropy as a feature selection method in datasets with a large number of categorical variables. Taking the KKbox music dataset as an example, we evaluate the impact on model performance by assessing the feature set selected through conditional entropy in categorical variable. Our experimental results show that we were able to obtain a model with fewer features but still maintaining good performance. This demonstrates that conditional entropy can serve as an effective feature selection method, helping us to discover features closely related to user listening behavior, thereby simplifying large datasets and enhancing the computational efficiency of the model.
參考文獻:	Addison Howard, Arden Chiu, M. M. m. W. K. Y. (2017). Wsdm - kkbox’s music recommendation challenge. Chang, Y.-F. (2024). Entropy: A join between science and mind-society. change, 15:29. Darcy, R. and Aigner, H. (1980). The uses of entropy in the multivariate analysis of categorical variables. American Journal of Political Science, 24(1):155–174. Hill, W., Stead, L., Rosenstein, M., and Furnas, G. (1995). Recommending and evaluating choices in a virtual community of use. In Proceedings of the SIGCHI conference on Human factors in computing systems, pages 194–201. KBVresearch (2022). Global recommendation engine market size, share industry trends analysis report by type, by application, by deployment type, by organization size, by end use, by regional outlook, strategy, challenges and forecast, 2021 - 2027. https://www.kbvresearch. com/recommendation-engine-market/. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc. Klema, V. and Laub, A. (1980). The singular value decomposition: Its computation and some applications. IEEE Transactions on Automatic Control, 25(2):164–176. Kraskov, A., Stögbauer, H., and Grassberger, P. (2004). Estimating mutual information. Physical review E, 69(6):066138. Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., and Liu, H. (2017). Feature selection: A data perspective. ACM Comput. Surv., 50(6). Li, Q., Kim, B. M., Guan, D. H., and Oh, D. w. (2004). A music recommender based on audio features. In Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, pages 532–533. PyPI (2021). python package index - pypi. https://pypi.org/. Rosenberg, A. and Hirschberg, J. (2007). V-measure: A conditional entropy-based external cluster evaluation measure. In Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP- CoNLL), pages 410–420. Song, Y., Dixon, S., and Pearce, M. (2012). A survey of music recommendation systems and future perspectives. In 9th international symposium on computer music modeling and retrieval, volume 4, pages 395–410. Citeseer. Statista (2021). Volume of data/information created, captured, copied, and consumed world- wide from 2010 to 2020, with forecasts from 2021 to 2025. https://www.statista.com/ statistics/871513/worldwide-data-created/. Wold, S., Esbensen, K., and Geladi, P. (1987). Principal component analysis. Chemometrics and Intelligent Laboratory Systems, 2(1):37–52. Proceedings of the Multivariate Statistical Workshop for Geologists and Geochemists. Zhang, J. and Fogelman-Soulié, F. (2018). Kkbox’s music recommendation challenge solution with feature engineering. In 11th ACM International Conference on Web Search and Data Mining WSDM, pages 1–8.
描述:	碩士國立政治大學統計學系 111354009
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0111354009
資料類型:	thesis
顯示於類別:	[統計學系] 學位論文

文件中的檔案:

檔案	描述	大小	格式	瀏覽次數
400901.pdf		1009Kb	Adobe PDF	0	檢視/開啟

在政大典藏中所有的資料項目都受到原著作權保護.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - 回饋