English  |  正體中文  |  简体中文  |  Post-Print筆數 : 11 |  Items with full text/Total items : 88613/118155 (75%)
Visitors : 23491591      Online Users : 626
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 理學院 > 資訊科學系 > 學位論文 >  Item 140.119/67626
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/67626


    Title: 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
    Authors: 葉思妤
    Yeh, Szu Yu
    Contributors: 徐國偉
    Hsu, Kuo Wei
    葉思妤
    Yeh, Szu Yu
    Keywords: 雙分群
    智慧型手機應用程式
    推薦系統
    Co-clustering
    Mobile Application
    Recommender System
    Date: 2013
    Issue Date: 2014-07-21 15:42:28 (UTC+8)
    Abstract: 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.
    Reference: [1] 陳昭宇,根基於自我組織特徵映射圖為基礎之最佳化演算法之推薦系統,國立中央大學碩士論文,2005。
    [2] 李惠雯、林怡伶、陳怡如、陳郁琳,推薦系統之研究,吳鳳技術學院專題研究,2009。
    [3] 蔡淑慧,模糊協同過濾於網路教材推薦之研究,中國文化大學碩士論文,2005。
    [4] J. A. HARTIGAN, “Direct Clustering of a Data Matrix,” Journal of the American Statistical Association Volume 67, Issue 337, 1972.
    [5] I. S. Dhillon. “Coclustering documents and words using Bipartite Spectral Graph Partitioning”KDD '01 Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining Pages 269-274 ACM New York, NY, USA, 2001.
    [6] Wei Peng, Tao Li. “Temporal relation co-clustering on directional social network and author-topic evolution, ” Journal of Knowledge and Information Systems, March 2011, Volume 26, Issue 3, pp 467-486.
    [7] I. S. Dhillon, S. Mallela, D. S. Modha. “Information Theoretic Coclustering, “KDD '03 Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining Pages 89-98 ACM New York, NY, USA, 2003.
    [8] Hyuk Cho y, Inderjit S. Dhillon y, Yuqiang Guan y, Suvrit Sra. “Minimum Sum-Squared Residue Co-clustering of Gene Expression Data, ” Proceedings of the 2004 SIAM International Conference on Data Mining.
    [9] T. George and S. Merugu. “A scalable collaborative filtering framework based on co-clustering.”Data Mining, Fifth IEEE International Conference, 2005.
    [10] KWT Leung, DL Lee, WC Lee. “A Collaborative Location Recommendation Framework based on Co-Clustering,” Proceeding SIGIR '11 Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval Pages 305-314 ACM New York, NY, USA, 2011.
    [11] 張雅芳,利用MapReduce實作分散式協同推薦系統,國立清華大學碩士論文,2001。
    [12] 林凡鈞,動態相似度協同過濾法的推薦系統,國立交通大學碩士論文,2002。
    [13] 莊清男,協同過濾式群體推薦,國立中央大學碩士論文,2004。
    [14] 曾靖茹,群集式協同過濾推薦方法之研究,國立中山大學碩士論文,2002。
    [15] 林邑信,植基於OSGi與特徵選取之個人化推薦系統,國立高雄應用科技大學碩士論文,2000。
    [16] 林順德,應用多維度關聯規則探勘在回饋式推薦系統,雲林科技大學碩士論文,1999。
    [17] B. Sarwar, G. Karypis, J. Konstan. “Item-based collaborative filtering recommendation algorithms,” Published by ACM 2001 Article WWW '01 Proceedings of the 10th international conference on World Wide Web Pages 285-295 ACM New York, NY, USA, 2001.
    [18] N. Good, J. B. Schafer, J. A. Konstan, A. Borchers, B. Sarwar, J. Herlocker, J. Riedl. “Combining Collaborative Filtering with Personal Agents for Better Recommendations,” AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative Applications of artificial intelligence conference innovative Applications of artificial intelligence Pages 439-446 American Association for Artificial Intelligence Menlo Park, CA, USA, 1999.
    [19] 戴英修,考慮商品異質性改善協同過濾推薦系統,國立清華大學碩士論文,1999。
    [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。
    [21] 林秀芬,基於協同過濾和雲模型的混合式推薦系統之研究,中國文化大學碩士論文,2001。
    [23] M. V. Setten, M. Veenstra, A. Nijholt, B. V. Dijk. “Case-Based Reasoning as a Prediction Strategy for Hybrid Recommender Systems,” Journal of Advances in Web Intelligence, Lecture Notes in Computer Science Volume 3034, 2004.
    [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.
    [37] T. Tran, R. Cohen. “Hybrid Recommender Systems for Electronic Commerce,” AAAI Technical Report WS-00-04, 2000.
    [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。
    [42] M. Balabanović, Y. Shoham. “content-based, collaborative recommendation,” Journal of Communications of the ACM CACM Homepage archive Volume 40 Issue 3, March 1997 Pages 66-72 ACM New York, NY, USA.
    [43] 林恩羽,一個輔助營養師的諮詢與推薦系統,逢甲大學碩士論文,2001。
    [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。
    [45] 林斐清,用使用者之Facebook社會網絡關係建立協同過濾推薦系統,屏東科技大學碩士論文,2001。
    [46] 劉睿哲,使用擴充資料進行共分群的協同式推薦系統,國立中央大學碩士論文,2001。
    [47] 曾億才,社群網路上運用群組關係於照片標註推薦系統,國立交通大學碩士論文,2001。
    [48] M. Böhmer, L. Ganev, A. Krüger. “AppFunnel A Framework for Usage-centric Evaluation of Recommender Systems that Suggest Mobile Applications,” IUI '13 Proceedings of the 2013 international conference on Intelligent User interfaces Pages 267-276 ACM New York, NY, USA, 2013.
    [49] B. Yan, G. Chen. “AppJoy: Personalized Mobile Application Discovery,” MobiSys '11 Proceedings of the 9th international conference on Mobile systems, Applications, and services Pages 113-126 ACM New York, NY, USA, 2011.
    [50] X. Xia, X. Wang, J. Li, X. Zhoua. “Multi-objective mobile App recommendation A system-level collaboration Approach,” Computers & Electrical Engineering Volume 40, Issue 1, January 2014, Pages 203–215, 2013.
    [51] K. Shi, K. Ali. “GetJar Mobile Application Recommendations with Very Sparse Datasets,” KDD '12 Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining Pages 204-212 ACM New York, NY, USA, 2012.
    [52] S. Rendle, L. Schmidt-Thieme. “Online-Updating Regularized Kernel Matrix Factorization Models for Large-Scale Recommender Systems,” Proceeding RecSys '08 Proceedings of the 2008 ACM conference on Recommender systems Pages 251-258 ACM New York, NY, USA, 2008.
    [53] V. Sindhwani, S.S. Bucak, J. Hu, A. Mojsilovic. “A Family of Non-negative Matrix Factorizations for One-Class Collaborative Filtering Problems,”2009
    [54] Wu H, Wang YJ, Wang Z, Wang XL, Du SZ. “Two-Phase collaborative filtering algorithm based on co-clustering.” Journal of Software, 2010
    [55] A. Banerjee, I. Dhillon, J. Ghosh, S. Merugu, D. S. Modha. “A Generalized Maximum Entropy Approach to Bregman Coclustering and Matrix Approximation,” Proceeding KDD '04 Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining Pages 509-514 ACM New York, NY, USA, 2004.
    [56] B. Li1, Q. Yang, X. Xue1. “Can Movies and Books Collaborate? Cross-Domain Collaborative Filtering for Sparsity Reduction,” Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence (IJCAI-09), 2009.
    [57] P. Chen, H. Wu, C. Hsu, W. Liao, and T. Li. “Logging and Analyzing Mobile User Behaviors,” International Symposium on Cyber Behavior, Taipei, Taiwan, February 2012.
    [58] Y. Cheng and G. Church. Biclustering of expression data. In Proceedings ISMB, pages 93103. AAAI Press, 2000.
    Description: 碩士
    國立政治大學
    資訊科學學系
    100971006
    102
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0100971006
    Data Type: thesis
    Appears in Collections:[資訊科學系] 學位論文

    Files in This Item:

    File SizeFormat
    100601.pdf1292KbAdobe PDF1272View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback