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研究生:葉信和
研究生(外文):Hsin-Ho Yeh
論文名稱:基於情境感知與音樂內涵探勘之智慧型音樂推薦技術
論文名稱(外文):Intelligent Music Recommendation Techniques by Mining Context Information and Musical Contents
指導教授:曾新穆曾新穆引用關係
指導教授(外文):Vincent Shin-Mu Tseng
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:英文
論文頁數:98
中文關鍵詞:喜好探勘內涵式推薦無所不在推薦系統協同過濾推薦
外文關鍵詞:Preference miningCollaborative Filteringcontext-aware RecommendationContent-based Recommendation
相關次數:
  • 被引用被引用:1
  • 點閱點閱:373
  • 評分評分:
  • 下載下載:76
  • 收藏至我的研究室書目清單書目收藏:1
隨著網路及通訊技術的進步,始得行動資訊檢索技術得以迅速發展。如何讓使用者從大量的音樂資料中與多變的情境條件下,找出使用者自己喜好的音樂是一件困難的事情。近幾十年來,大多數的研究方向著重於協同式音樂推薦技術。由於只考量使用者的評分行為並未考量情境感知與音樂內涵,始得目前的音樂推薦技術再也無法滿足現代使用者的需求。為了解決上述的問題,在本研究中提出兼具情境感知與音樂內涵探勘之智慧型音樂推薦技術。為了有效利用複雜的音樂內涵,我們提出二階段叢集法(two-stage clustering),藉由同時考量聽覺與時間的特性,將原先複雜的音樂內涵有效率的轉換成知覺樣式。透過情境感知探勘,使用者在相似情境底下過去評分行為會被收集並挖掘出喜好的音樂樣式(preference patterns)。藉由考量情境感知與音樂內涵,我們提出的方法可以準確的預測使用者在不同情境條件下喜好的音樂。經由實驗分析顯示,我們所提出的方法比其他推薦技術在推薦準確率上具有更高之準確度。
Advanced networking and telecommunication technologies make the ease of mobile information retrieval. However, it is hard for the users to find what she/he prefers under large music databases and variant context conditions. To solve this problem, some previous music recommenders have been proposed for providing preferable music automatically. Unfortunately, current music recommenders only based on user’s rating log cannot earn the user’s satisfaction in finding the preferred music due to the lacked consideration of context information and sufficient rating data. To solve above problems, in this thesis, we propose a novel Music Recommendation technique by integrating Context information and musical Contents (called MRCC). For context information, the users in similar context conditions are grouped to mine the similar preference patterns. For musical contents, we propose two-stage clustering to convert musical contents into perceptual patterns with considering acoustical and temporal features simultaneously. Finally, the user’s preference can be accurately predicted by integrated mining of context information and musical contents. Experimental results show that our proposed MRCC can capture the user’s preference effectively and outperform existing collaborative filtering approaches in terms of accuracy on context-aware recommendation.
中文摘要…................................................................................................. I
ABSTRACT............................................................................................. II
誌謝……….............................................................................................. IV
List of Tables..........................................................................................VII
List of Figures......................................................................................VIII
Chapter 1 Introduction ..........................................................................1
1.1 Background................................................................................................1
1.2 Motivation..................................................................................................3
1.3 Preview of Our Proposed Method..............................................................6
1.4 Contributions..............................................................................................8
1.5 Thesis Organization ...................................................................................9
Chapter 2 Related Work......................................................................10
2.1 Collaborative Filtering.............................................................................10
2.2 Content-based Filtering............................................................................16
2.3 Hybrid Collaborative Filtering and Content-based Filtering...................17
2.4 Context-Aware-based Recommendation..................................................18
Chapter 3 The Proposed Context-Aware Music Recommender......23
3.1 Overview of The Proposed Recommender ..............................................23
3.2 Preliminaries ............................................................................................26
3.3 Offline Preprocessing – Music Feature Extraction..................................29
3.4 Online Prediction .....................................................................................40
3.5 Example for Online Prediction ................................................................56
Chapter 4 Experimental Evaluation...................................................60
4.1 Experimental Environment ......................................................................60
4.2 Experimental Settings ..............................................................................67
4.3 Study of Experimental Evaluations .........................................................77
4.4 Experimental Discussions........................................................................86
Chapter 5 Conclusions and Future Work ..........................................87
5.1 Conclusion ...............................................................................................87
5.2 Future Work .............................................................................................88
5.3 Applications .............................................................................................89
Reference…..............................................................................................90
VITA……….............................................................................................97
Publications .............................................................................................98
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