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研究生:吳振銘
研究生(外文):Zhen-Ming Wu
論文名稱:應用改良式K-means分群法於個人化音樂推薦服務系統之實現
論文名稱(外文):Application of Refined K-means Clustering to the Implementation of Personalized Musical Recommendation System
指導教授:廖斌毅謝欽旭謝欽旭引用關係
指導教授(外文):Bin-Yih LiaoChin-Shiuh Shieh
學位類別:碩士
校院名稱:國立高雄應用科技大學
系所名稱:電子工程系
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:101
畢業學年度:100
語文別:中文
論文頁數:75
中文關鍵詞:推薦系統、協同式過濾、K-means分群、相關相似性、凝聚率、鑑別率、RMSE
外文關鍵詞:Recommendation system, Collaborative Filter, Content-Base Filter, K-means clustering, Correlation, Agglomerate rate, Discrimination rate, RMSE
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  • 被引用被引用:16
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近年來全球數位化內容與網際網路的發達,使用網路服務與獲得資訊的人數越來越多;在這資訊爆發時代,使用者接受的資訊範圍也越來越廣,而網路的終端節點不受限於傳統個人電腦,生活周遭的每樣消費電子產品都可經由網路彼此互相溝通、連結,而本篇論文將提出結合音樂推薦系統,與搭載Android作業系統的移動式手持裝置,建立起雲端推薦系統伺服器,使用者可以經由個人的移動式手持裝置連結雲端音樂推薦系統伺服器進行匹配運算,最後經由推薦模組產生出推薦結果回傳至移動式手持裝置。本篇論文提出運用推薦系統的分析技術:協同式過濾(Collaborative Filter)模型與內容式過濾模型(Content-Base Filter),實現基於以音樂為主的推薦系統。收集使用者的對音樂喜好的評分(Rating)建立使用者的使用資訊,並以協同式過濾的概念,找出聆聽興趣、喜好相同的使用者,結合本篇論文提出的改良式K-means分群技術,改良傳統K-means分群的缺點提高群內的相似度,分群的結果將找出使用者間具有聆聽相同音樂喜好的同群使用者,以喜好類似的同群使用者作為推薦的依據,分析各使用者間的聆聽記錄,計算各使用者聆聽項目間的相關相似性(Correlation),產生預測的評分結果,雲端推薦系統伺服器將評分高的音樂推薦給使用者。實驗結果將會比較改良式K-means分群法與其他分群演算法的結果,以分群的凝聚率(Agglomerate rate)與鑑別率(Discrimination rate)作為分群評估,最後比較使用不同的分群技術對於雲端音樂推薦系統的影響,以RMSE(Root Mean Square Error)平均誤差值評估不同的分群技術;評估分析對雲端音樂推薦的推薦結果,實驗結果表明:改良式K-means分群法對於雲端音樂推薦系統的準確度,有著良好的改善。
As digitized content and the Internet evolve in recent years, the number of users who obtain information via the Internet keeps growing. In the era of information explosion, the personal computer is no longer the only option as network terminals. A wide range of 3C products can communicate and link together with each other via the Internet. In this study, a music recommendation system based on Android operation system and mobile handhold devises is proposed. The objective is to establish a cloud computing service such that users can get the music matching information via music recommendation model.

Two techniques are involved in our recommendation system: Collaborative Filter Model and Content-Base Filter Model. The rating information is collected by the users’ actual rating and the users with similar interest or preference will be found by system via collaborative filter model. An improved K-Means Clustering algorithm is proposed in this thesis which improves the similarity among data in the same group. The clustering classifies users into groups with the same preference. Recommendations are made base on the music preference of users in the same group. The candidates with highest score will be recommended to the users by the cloud recommendation system.

The proposed improvement was experimented against previous approaches. The aggregation rate and discrimination rate were used as the performance indices. And then the Root Mean Square Error was used to evaluate the effectiveness of the recommendation system. Experiment results reveal that the proposed refinement outperforms previous schemes.
摘 要…………………………………………………………………………………. i
ABSTRACT………………………………………………………………………….. ii
誌 謝 …………………………………………………………………………………..iv
目錄………………………………………………………………………………….…v
圖目錄………………………………………………………………………………. vii
表目錄………………………………………………………………………………... ix

第一章 緒論 1
1.1. 前言 1
1.2. 研究動機與目的 2
1.3. 論文架構 3
第二章 相關研究及文獻探討 5
2.1 推薦系統 5
2.1.1 長尾效應理論(The Long Tail) 7
2.1.2 內容式過濾技術(Content-Base Filtering) 9
2.1.3 協同式過濾技術(Collaborative Filtering) 11
2.1.4 混合式過濾技術(Hybrid Filtering) 14
2.2 分群技術 16
2.2.1 分割式分群演算法(Partitioning Clustering Algorithms) 16
2.2.2 階層式分群演算法(Hierarchical Clustering Algorithms) 17
2.2.3 基於密度分群演算法(Density-base Clustering Algorithms) 20
2.2.4 基於網格分群演算法(Grid-base Clustering Algorithms) 20
2.2.5 基於模型分群演算法(Model-base Clustering Algorithms) 20
2.3 Android系統 21
2.3.1 JSON資料交換 23
第三章 Android雲端音樂服務推薦系統建置 24
3.1 音樂推薦服務系統流程與架構 24
3.2 建置Music Style 25
3.3 建置User Profile 26
3.3.1 User Profile的計算 27
3.4 User-base分群演算法 29
3.5 K-means分群演算法 32
3.6 改良式K-means分群演算法 33
3.6.1 改良初始化群中心 33
3.6.2 動態K值演算法 37
3.7 協同式過濾音樂推薦模組 42
3.8 Android移動式手持裝置的開發與連結 43
第四章 實驗結果與評估 44
4.1 環境建置 44
4.2 分群評估 45
4.4.1 凝聚率(Agglomerate rate) 45
4.4.2 鑑別率(Discrimination rate) 47
4.3 推薦系統評估 49
4.4 系統展示 50
4.4.1 個人化推薦 51
4.4.2 個性化推薦 53
第五章 結論與未來展望 56
5.1 研究結論 56
5.2 未來展望 57
參考文獻 58
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