政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/94859

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/94859

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政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 > Item 140.119/94859

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题名:	基於個人電腦使用者操作情境之音樂推薦 Context-based Music Recommendation for Desktop Users
作者:	謝棋安 Hsieh, Chi An
贡献者:	沈錳坤 Shan, Man Kwan 謝棋安 Hsieh, Chi An
关键词:	音樂推薦情境個人電腦 MAML 演算法 music recommendation context desktop MAML algorithm
日期:	2009
上传时间:	2016-05-09 12:02:32 (UTC+8)
摘要:	隨著電腦音樂技術的蓬勃發展，合乎情境需求的音樂若被能自動推薦給使用者，將是知識工作者所樂見的。我們提出了一個定義使用者操作情境的情境塑模，定義使用者操作情境，並利用累計專注視窗的轉變，找出使用者的操作情境。同時，我們也提出了音樂推薦塑模，依據使用者的操作情境與聆聽的音樂，分析探勘情境與音樂特徵間的關聯特性，利用探勘出的關聯推薦適合情境的音樂給使用者。在此音樂推薦塑模中，我們採用Content-based Recommendation的作法。我們分析音樂的特徵值，並發展MAML(Multi-attribute Multi-label)的分類演算法以及Probability Measure二種方法來探勘情境屬性與音樂特徵間的關聯特性。根據探勘出的關聯特性，找出適合情境的音樂特徵，再從音樂資料庫中推薦符合音樂特徵的音樂給使用者。本論文的符合使用者操作情境的音樂推薦系統是利用Windows Hook API實作。經實驗證明，本論文方法在符合情境的音樂推薦上，擁有近七成準確率。 With the development of digital music technology, knowledge workers will be delighted if the music recommendation system is able to automatically recommend music based on the operating context in the desktop. The context model and context identification algorithm are proposed to define the operating context of users and to detect the transition of context based on the changes of focused windows. Two association discovery mechanisms, MMAL (Multi-attribute Multi-label) algorithm and PM (Probability Measure), are proposed to discover the relationships between context features and music features. Based on the discovered rules, the proposed music recommendation mechanism recommends music to the user from the music database according to the operating context of users. The context-based recommendation system is implemented using Windows Hook API. Experimental results show that near 70% accuracy can be achieved.
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Webb, “Proportional k-Interval Discretization for Naive-Bayes Classifiers,” Proceeding of European Conference on Machine Learning, 2001. [41] K. Yoshii, M. Goto, K. Komatani, T. Ogata, and H. Okuno, “Hybrid Collaborative and Content-based Music Recommendation Using Probabilistic Model with Latent User Preferences,” Proceeding of International Conference on Music Information Retrieval, 2006. [42] Feeling Words: http://eqi.org/fw.htm [43] Last.fm. http://www.last.fm/.
描述:	碩士國立政治大學資訊科學學系 95753007
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0095753007
数据类型:	thesis
显示于类别:	[資訊科學系] 學位論文

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