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題名 一個使用雙分群演算法進行智慧型手機應用程式推薦之框架
A Framework for Using Co-Clustering Algorithms to Recommend Smartphone Apps
作者 葉思妤
Yeh, Szu Yu
貢獻者 徐國偉
Hsu, Kuo Wei
葉思妤
Yeh, Szu Yu
關鍵詞 雙分群
智慧型手機應用程式
推薦系統
Co-clustering
Mobile Application
Recommender System
日期 2013
上傳時間 21-Jul-2014 15:42:28 (UTC+8)
摘要 近年來，智慧型手機(Smartphone)的銷量超過其他型式手機。智慧型手機具有更先進、更開放的行動作業系統，可允許使用者自行安裝應用程式軟體(Application)來擴充手機功能。目前市面上的應用程式數量非常龐大，在眾多的應用程式和有限的時間下，使用者不太可能將所有的應用程式下載試用，所以對使用者而言，找出自己所想要和需要的應用程式，是個困難的問題。推薦系統可依照使用者的喜好，或是準備推薦項目的相似程度來做推薦，讓使用者能較快得到想要的資訊，目前主要的方式有協同過濾(Collaborative Filtering, CF)、內容過濾(Content-Based Filtering, CBF)，還有結合前述兩種方式的混和式推薦(Hybrid Approach)。
本研究所使用的資料集是由政治大學資訊科學系所開發的實驗平台蒐集而來。資料以側錄的方式，將使用者實際操作手機應用程式的狀況記錄下來，其中包含了25位使用者和1125個應用程式。我們將原始資料集以三種方式整理成三個資料集：一、是否使用應用程式；二、使用應用程式的次數；三、使用應用程式的頻率，其值表示使用者在該應用程式的使用狀況。我們並將資料分成前段與後段時間兩部分，以前段時間的資料當作基準，推薦最多同群使用者使用的應用程式、同群使用者使用次數最多的應用程式，以及同群使用者最常使用的應用程式，然後以後段時間的資料做驗證，計算推薦結果的準確率與召回率加以比較。
我們使用知名的Information Theoretic Co-Clustering Algorithm和兩種基於Minimum Squared Residue Co-Clustering Algorithm的演算法將使用者與應用程式分群，利用分群結果做計算，推薦應用程式給使用者。實驗發現三種演算法在第一個資料集的準確率與召回率表現最好，此資料集以0和1的值，來紀錄使用者在各應用程式的使用狀況。實驗比較三個演算法的結果，在大部分的情況之下，一個基於Minimum Squared Residue Co-Clustering Algorithm的演算法，給出的結果較好。
此外，我們也發現應用程式開發者將應用程式上架提供下載時，以個人主觀想法對該應用程式定義其分類，與我們利用雙分群方法，以使用者實際操作的情況將應用程式分類的結果有些差異，或許在Google Play的分類上可做調整。
本研究提出推薦系統的框架具有彈性，未來可以使用不同的雙分群演算法做分群，也能套用其他的推薦方式。
With the rapid evolution of smartphone devices, tens of thousands applications have been supplied on online stores such as App Store (operated by Apple Inc.) and Google Play (operated by Google Inc.). Since there are many applications, recommending applications to users becomes an important topic. In this thesis, we present a framework for using a co-clustering algorithm to recommend applications to users. Recommendations are a part of everyday life. People usually rely on some external knowledge to make informed decisions about a particular artifact or action. Using recommender systems is one of general approaches that help people make decisions. There are three common types of recommender systems, namely collaborative filtering, content-based filtering, and hybrid recommender systems.
In this thesis, we use the dataset that was collected by a tool developed by the Department of Computer Science at the National Chengchi University. It recorded the users’ behavior when they were using their smartphones. We transform the original dataset into three types of datasets: 1) indicating whether a user used an application; 2) indicating the number of uses made by a user for an application; 3) indicating the frequency of uses made by a user for an application. Furthermore, we divide each dataset into two parts: The first part containing data for the early time period is used as the recommending base, and the second part containing data for the late time period is used for verifying the results. We utilize three famous co-clustering algorithms, which are the Information Theoretic Co-Clustering Algorithm and two algorithms based on the Minimum Squared Residue Co-Clustering Algorithm, in the proposed framework.
According to the clusters given by a co-clustering algorithm, we recommend top five applications to each user by referring to the maximum number of users, the maximum number of uses, and the most frequently used applications that are in the same cluster. We calculate the precision and recall values to compare the results. From the experimental results, we find that the best result corresponds to the first type of dataset and also that one of the algorithms based on the Minimum Squared Residue Co-Clustering Algorithm is better than the other two algorithms in terms of the precision and recall values.
From the clusters of applications, we obtain some interesting insights into the categories of applications. The categories of applications are set by their developers, but the users may not totally agree with the settings. There might be space for improvement for the categories of applications on the online store.
In the future, we can utilize different co-clustering algorithms and other recommended methods in the proposed framework.
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[20] 王振遠，一個採用混合式推薦系統解決多樣性問題的方法，國立東華大學碩士論文，2002。
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[24] P. Melville, R. J. Mooney, R. Nagarajan. “Content-Boosted Collaborative Filtering for Improved Recommendations,” Eighteenth national conference on Artificial intelligence Pages 187-192 American Association for Artificial Intelligence Menlo Park, CA, USA, 2002.
[25] C. Basu, H. Hirsh, W. Cohen. “Recommendation as classification: Using social and content-based information in recommendation,” Venue: In Proceedings of the Fifteenth National Conference on Artificial Intelligence, 1998.
[26] 金柏均，花蓮旅遊景點查詢及推薦系統，國立東華大學碩士論文，2002。
[27] 范姜雅藍，建構於Facebook上之餐飲商店推薦系統，國立新竹教育大學碩士論文，2001。
[28] 鄧永聖，一個利用標籤為基礎之混合式推薦系統–以文化資產與歷史古蹟為例，逢甲大學碩士論文， 2000。
[29] R. Burke. “Hybrid Recommender Systems: Survey and Experiments,” Journal of User Modeling and User-Adapted Interaction November 2002, Volume 12, Issue 4, pp 331-370.
[30] 蔡子敬，以模糊語意法協助音樂CD資訊推薦系統之設計，國立屏東商業技術學院碩士論文，2000。
[31] U. Shardanand, P. Maes. “Social information filtering: algorithms for automating “word of mouth”,” CHI `95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems Pages 210-217 ACM Press/Addison-Wesley Publishing Co. New York, NY, USA, 1995.
[32] 羅健銘，協同過濾於網站推薦之研究，國立台北科技大學碩士論文，2000。
[33] 林文新，應用協同過濾法設計信用卡點數兌換之推薦系統，大同大學碩士論文，2008。
[34] Greg Linden, Brent Smith, Jeremy York. “Amazon.com Recommendations: Item-to-Item Collaborative Filtering,” IEEE Internet Computing, vol. 7, no. 1, pp. 76-80, Jan./Feb. 2003.
[35] 蔡松霖，電子商務推薦系統模型之初探，國立東華大學博士論文，2002。
[36] R. J. Mooney, L. Roy. “Content-based book recommending using learning for text categorization,” DL `00 Proceedings of the fifth ACM conference on Digital libraries Pages 195-204 ACM New York, NY, USA, 2000.
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[38] R. Forsati, H. M. Doustdar, M. Shamsfard, A. Keikha, M. R. Meybodi. “A fuzzy co-clustering Approach for hybrid recommender systems,” International Journal of Hybrid Intelligent Systems, Vol. 10, No. 2., 2013.
[39] 陳正德，以項目為基礎的協同過濾應用於網路教材瀏覽推薦之研究，銘傳大學碩士論文，2003。
[40] 黃信傑，以協同過濾輔助內容分析之文件推薦系統，國立中山大學碩士論文，2005。
[41] 鍾岳豪，以學習者個人偏好與試題反應理論為基礎之個人化英文文章推薦系統，逢甲大學碩士論文，2000。
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[44] 邱俊耀，結合本體以多準則熵權重運算之推薦系統-以糖尿病藥物為例，朝陽科技大學碩士論文，2011。
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描述 碩士
國立政治大學
資訊科學學系
100971006
102
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0100971006
資料類型 thesis
dc.contributor.advisor 徐國偉zh_TW
dc.contributor.advisor Hsu, Kuo Weien_US
dc.contributor.author (Authors) 葉思妤zh_TW
dc.contributor.author (Authors) Yeh, Szu Yuen_US
dc.creator (作者) 葉思妤zh_TW
dc.creator (作者) Yeh, Szu Yuen_US
dc.date (日期) 2013en_US
dc.date.accessioned 21-Jul-2014 15:42:28 (UTC+8)-
dc.date.available 21-Jul-2014 15:42:28 (UTC+8)-
dc.date.issued (上傳時間) 21-Jul-2014 15:42:28 (UTC+8)-
dc.identifier (Other Identifiers) G0100971006en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/67626-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學學系zh_TW
dc.description (描述) 100971006zh_TW
dc.description (描述) 102zh_TW
dc.description.abstract (摘要) 近年來，智慧型手機(Smartphone)的銷量超過其他型式手機。智慧型手機具有更先進、更開放的行動作業系統，可允許使用者自行安裝應用程式軟體(Application)來擴充手機功能。目前市面上的應用程式數量非常龐大，在眾多的應用程式和有限的時間下，使用者不太可能將所有的應用程式下載試用，所以對使用者而言，找出自己所想要和需要的應用程式，是個困難的問題。推薦系統可依照使用者的喜好，或是準備推薦項目的相似程度來做推薦，讓使用者能較快得到想要的資訊，目前主要的方式有協同過濾(Collaborative Filtering, CF)、內容過濾(Content-Based Filtering, CBF)，還有結合前述兩種方式的混和式推薦(Hybrid Approach)。
本研究所使用的資料集是由政治大學資訊科學系所開發的實驗平台蒐集而來。資料以側錄的方式，將使用者實際操作手機應用程式的狀況記錄下來，其中包含了25位使用者和1125個應用程式。我們將原始資料集以三種方式整理成三個資料集：一、是否使用應用程式；二、使用應用程式的次數；三、使用應用程式的頻率，其值表示使用者在該應用程式的使用狀況。我們並將資料分成前段與後段時間兩部分，以前段時間的資料當作基準，推薦最多同群使用者使用的應用程式、同群使用者使用次數最多的應用程式，以及同群使用者最常使用的應用程式，然後以後段時間的資料做驗證，計算推薦結果的準確率與召回率加以比較。
我們使用知名的Information Theoretic Co-Clustering Algorithm和兩種基於Minimum Squared Residue Co-Clustering Algorithm的演算法將使用者與應用程式分群，利用分群結果做計算，推薦應用程式給使用者。實驗發現三種演算法在第一個資料集的準確率與召回率表現最好，此資料集以0和1的值，來紀錄使用者在各應用程式的使用狀況。實驗比較三個演算法的結果，在大部分的情況之下，一個基於Minimum Squared Residue Co-Clustering Algorithm的演算法，給出的結果較好。
此外，我們也發現應用程式開發者將應用程式上架提供下載時，以個人主觀想法對該應用程式定義其分類，與我們利用雙分群方法，以使用者實際操作的情況將應用程式分類的結果有些差異，或許在Google Play的分類上可做調整。
本研究提出推薦系統的框架具有彈性，未來可以使用不同的雙分群演算法做分群，也能套用其他的推薦方式。		zh_TW
dc.description.abstract (摘要) With the rapid evolution of smartphone devices, tens of thousands applications have been supplied on online stores such as App Store (operated by Apple Inc.) and Google Play (operated by Google Inc.). Since there are many applications, recommending applications to users becomes an important topic. In this thesis, we present a framework for using a co-clustering algorithm to recommend applications to users. Recommendations are a part of everyday life. People usually rely on some external knowledge to make informed decisions about a particular artifact or action. Using recommender systems is one of general approaches that help people make decisions. There are three common types of recommender systems, namely collaborative filtering, content-based filtering, and hybrid recommender systems.
In this thesis, we use the dataset that was collected by a tool developed by the Department of Computer Science at the National Chengchi University. It recorded the users’ behavior when they were using their smartphones. We transform the original dataset into three types of datasets: 1) indicating whether a user used an application; 2) indicating the number of uses made by a user for an application; 3) indicating the frequency of uses made by a user for an application. Furthermore, we divide each dataset into two parts: The first part containing data for the early time period is used as the recommending base, and the second part containing data for the late time period is used for verifying the results. We utilize three famous co-clustering algorithms, which are the Information Theoretic Co-Clustering Algorithm and two algorithms based on the Minimum Squared Residue Co-Clustering Algorithm, in the proposed framework.
According to the clusters given by a co-clustering algorithm, we recommend top five applications to each user by referring to the maximum number of users, the maximum number of uses, and the most frequently used applications that are in the same cluster. We calculate the precision and recall values to compare the results. From the experimental results, we find that the best result corresponds to the first type of dataset and also that one of the algorithms based on the Minimum Squared Residue Co-Clustering Algorithm is better than the other two algorithms in terms of the precision and recall values.
From the clusters of applications, we obtain some interesting insights into the categories of applications. The categories of applications are set by their developers, but the users may not totally agree with the settings. There might be space for improvement for the categories of applications on the online store.
In the future, we can utilize different co-clustering algorithms and other recommended methods in the proposed framework.		en_US
dc.description.tableofcontents 第一章 緒論 1
1.1前言 1
1.2研究動機 1
1.3研究目的 4
1.4論文架構 5
第二章 文獻探討 6
2.1推薦系統 6
2.1.1協同過濾 6
2.1.2內容過濾 7
2.1.3混合式推薦系統 7
2.2 CO-CLUSTERING (雙分群) 8
2.2.1 Information Theoretic Co-Clustering Algorithm 9
2.2.2 Minimum Squared Residue Co-Clustering Algorithm 9
2.3如何使用CO-CLUSTERING做推薦系統 10
2.4 總結 10
2.4.1 電影 11
2.4.2 景點/餐廳 13
2.4.3 電子商務 14
2.4.4 文本資料 15
2.4.5 健保 16
2.4.6 社群網路服務 16
2.4.7 手機應用程式 16
第三章 基於雙分群的推薦框架 18
3.1 總覽 18
3.2 實際運作範例 20
3.2.1 以使用者是否使用應用程式做推薦 20
3.2.2 以使用應用程式之次數做推薦 22
3.2.3 以使用者使用應用程式的頻率做推薦 24
第四章 實驗 27
4.1 環境與流程 27
4.1.1 實驗環境 27
4.1.2 實驗流程 27
4.2 資料集 34
4.3 實驗結果(一) 39
4.3.1 以使用者是否使用應用程式做推薦(YN) 41
4.3.2 以使用者使用應用程式的次數做推薦(Count) 42
4.3.3以使用者使用應用程式的頻率做推薦(Frequency) 45
4.3.4小結 48
4.4 實驗結果(二) 55
4.4.1 以使用者是否使用應用程式做應用程式分群(YN) 56
4.4.2 以使用者使用應用程式的次數做應用程式分群(Count) 58
4.4.3以使用者使用應用程式的頻率做應用程式分群(Frequency) 60
4.4.4小結 62
第五章 結論與未來可能研究方向 63
5.1結論 63
5.2未來可能研究方向 64
參考文獻 65		zh_TW
dc.format.extent 1323371 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0100971006en_US
dc.subject (關鍵詞) 雙分群zh_TW
dc.subject (關鍵詞) 智慧型手機應用程式zh_TW
dc.subject (關鍵詞) 推薦系統zh_TW
dc.subject (關鍵詞) Co-clusteringen_US
dc.subject (關鍵詞) Mobile Applicationen_US
dc.subject (關鍵詞) Recommender Systemen_US
dc.title (題名) 一個使用雙分群演算法進行智慧型手機應用程式推薦之框架zh_TW
dc.title (題名) A Framework for Using Co-Clustering Algorithms to Recommend Smartphone Appsen_US
dc.type (資料類型) thesisen
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