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研究生:張為義
研究生(外文):Wei-YiChang
論文名稱:整合社群與協同資訊探勘之音樂推薦技術
論文名稱(外文):Integrated Mining of Social and Collaborative Information for Music Recommendation
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:2012
畢業學年度:100
語文別:英文
論文頁數:69
中文關鍵詞:推薦系統社群標籤音樂推薦協同過濾隨機漫步
外文關鍵詞:recommender systemsocial tagsmusic recommendationcollaborative filteringrandom walk
相關次數:
  • 被引用被引用:1
  • 點閱點閱:218
  • 評分評分:
  • 下載下載:14
  • 收藏至我的研究室書目清單書目收藏:2
隨著使用者可以從網路上取得越來越多的音樂資料,如何從大量的音樂資料中挑選出合適的音樂給使用者顯得越來越重要。為了達到這個目標,推薦系統已經被提出用來發覺使用者的偏好,而目前最常見的推薦方法是協同式推薦技術。但是當使用者的評分太稀疏時,協同式推薦技術較無法產生合適的推薦結果。因此,考慮其它有用的資訊是必要的,於是在本研究中我們提出一個整合社群和協同資訊的音樂推荐技術。我們整合了使用者的聽歌記錄和音樂的標籤雲(tag cloud)去計算音樂之間的相似度。此外,我們也利用音樂的標籤雲和音樂的歌手資訊去計算歌手之間的相似度。最後我們整合這兩種相似度去做預測。藉由整合社群和協同資訊,使用者的偏好可以被精確的預測。經由實驗分析顯示,我們所提出的方法比其他推薦技術在準確率上具有更好的效果。
With the rapid growth of music data available on the web, it is necessary to have a tool to select the preferred music from a massive of music datasets for users. To achieve this goal, recommender systems have been proposed to discover the user’s preference, and collaborative filtering (CF) is the most popular approach. However, it is difficult for CF to make suitable recommendation when user’s ratings are too sparse. Thus, it is necessary to consider other useful information to aim at this problem. We propose a recommender system, namely FAT (Fusion of Artist-Tags and Tripartite Random Walk with Restarts Similarity) which integrates the social and collaborative information. We integrate the user’s listening log and the tag cloud of music to calculate the item similarity in music space. Besides, we utilize the tag cloud of music and the artist information of music to calculate the item similarity in artist space. Finally, we fuse these two kinds of item similarities to make prediction. By integrating the social and collaborative information, the user’s preference could be predicted accurately. The empirical evaluations reveal that our proposed FAT outperforms several existing recommendation algorithms in terms of accuracy.
中文摘要 I
ABSTRACT II
誌謝 IV
CONTENTS V
List of Tables VI
List of Figures VII
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 4
1.3 Preview of Our Proposed Method 6
1.4 Contribution 7
1.5 Thesis Organization 8
Chapter 2 Related Work 9
2.1 Collaborative Filtering 9
2.2 Tag-based Recommendation 16
2.3 Social Filtering Recommendation 20
Chapter 3 The Proposed Music Recommender 22
3.1 Overview of the Proposed Recommender 22
3.2 Offline Stage 24
3.3 Online Stage 37
Chapter 4 Empirical Study 39
4.1 Experimental Environment 39
4.2 Experimental Settings 44
4.3 Study of Experimental Evaluations 48
4.4 Experimental Discussion 57
Chapter 5 Conclusions and Future Work 59
5.1 Conclusions 59
5.2 Future Work 61
Reference 62
VITA 69

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