臺灣博碩士論文加值系統

現在網際網路科技的發達，網路資料量日益增多，使得人們逐漸無法處理過於龐大的資料，也因此出現了資訊過載(Information Overloading)的議題。當資料量變得龐大時,不只會帶來資訊過載的問題，在資料儲存上也常會出現因使用者數目龐大、資料項目過多而造成稀疏矩陣，例如：擁有成千上百產品或數以千計書籍的網路書城，其使用者交易的書目通常為個位數，因此”顧客”在數以千計的”產品項目”項下，只能針對少數幾項產品有交易資訊，如此就會形成資料儲存時的稀疏性。
過去有許多學者提到稀疏性問題，然而在協同過濾式推薦研究上，則較少有學者針對此一議題提具出有時效性之解決方法。有鑑於此，本研究提出一個藉由改善稀疏矩陣處理方式之協同過濾式推薦，提升處理稀疏矩陣在運算上的時效性。本研究所提之方法較傳統常用的皮爾森相關係數、歐幾里德距離、傑卡德指數，在時效上與準確度上都有較佳的結果。

As the development and usage of Internet keeps growing, the sheer volume of data on the Internet makes it difficult for user to handle. As a result, users must deal with the information overloading problem. Even when the amount of data keeps booming such as hundreds of products, thousands of books, however, the amount of transactions for each product item will still be similar. Therefore, the transaction data among thousands of items and thousands of uses may form the sparse matrix. For collaborative filtering recommendation, there are many researchers proposing great methodologies to handle user-item-matrix dataset, however, not many researchers deal with the performance of sparse matrix problem. This is the reason why this research proposes a novel solution for collaborative filtering recommendation. The proposed method has compared to the traditional Pearson Correlation Coefficient method, Euclidean Distance, Jaccard index and the results show that our proposed method is better in terms of time efficient and precision.

目 錄
中文摘要………………………………………………………………… …… iv
英文摘要………………………………………………………………………… v
誌謝頁…………………………………………………………………………… vi
第壹章、導論………………………………………………………………… 1
第貳章、文獻探討………………………………………………………… 4
2.1推薦系統……………………………………………………………… 4
2.2協同過濾式推薦……………………………………………………… 6
2.3皮爾森相關係數……………………………………………………… 8
2.4稀疏性與稀疏矩陣…………………………………………………… 10
2.5歐幾里德距離………………………………………………………… 11
2.6 互斥或(XOR)運算……………………………………………………… 12
2.7傑卡德指數(Jaccard index)………………………………………… 12
第參章、研究架構…………………………………………………………… 15
3.1 研究架構……………………………………………………………… 16
3.2 取得資料集…………………………………………………………… 21
3.3 篩選資料……………………………………………………………… 21
3.4 資料轉換……………………………………………………………… 22
3.5 矩陣轉換……………………………………………………………… 23
3.6 資料XOR比對………………………………………………………… 24
3.7 推薦應用……………………………………………………………… 25
第肆章、實驗結果………………………………………………………… 26
4.1 實驗資料……………………………………………………………… 26
4.2 實驗工具……………………………………………………………… 28
4.3 實驗流程……………………………………………………………… 28
4.3.1實驗設計……………………………………………………… 29
4.3.2 R語言程式…………………………………………………… 30
4.4 實驗結果呈現………………………………………………………… 31
第伍章、結論與未來研究………………………………………………… 42
5.1 結論…………………………………………………………………… 42
5.2 後續研究……………………………………………………………… 43
文獻參考………………………………………………………………………… 44

圖目錄
圖2.1 稀疏矩陣示意圖…………………………………………………… 11
圖3.1 本研究的架構圖…………………………………………………… 15
圖3.2 資料集CRF的示意圖……………………………………………… 17
圖3.3 分數轉換示意圖…………………………………………………… 18
圖3.4 User-Item矩陣示意圖…………………………………………… 19
圖3.5 (a) XOR比對示意圖……………………………………………… 20
(b) XOR比對後結果示意圖……………………………………… 20
圖3.6 User Rating Matrix資料集示意圖…………………………… 22
圖3.7 轉換後資料集……………………………………………………… 23
圖3.8 R語言程式矩陣轉換……………………………………………… 24
圖3.9 User-Item矩陣示意圖…………………………………………… 24
圖3.10 資料XOR比對…………………………………………………… 25
圖4.1 Epinions資料集示意圖………………………………………… 27
圖4.2 轉換後矩陣………………………………………………………… 27
圖4.3 隨機抽出資料示意圖……………………………………………… 30
圖4.4 R語言XOR程式…………………………………………………… 31
圖4.5 實驗輸出結果CSV檔……………………………………………… 32
圖4.6 轉換後排序資料…………………………………………………… 33
圖4.7 目標使用者與比對使用者的準確度比較………………………… 33
圖4.8 取出不同數量的三組資料時間之比較圖………………………… 35
圖4.9 取出不同數量的三組資料準確率之比較圖……………………… 36
圖4.10 L10-1時間比較…………………………………………………… 37
圖4.11 L10-1準確率比較………………………………………………… 38
圖4.12 L10-2時間比較…………………………………………………… 39
圖4.13 L10-2準確度比較………………………………………………… 40
 
表目錄
表2.1 資料稀疏性示意圖……………………………………………… 9
表2.2 資料不足示意圖………………………………………………… 9
表2.3 顧客評分相同示意圖…………………………………………… 10
表2.4 顧客趨勢示意圖………………………………………………… 10
表2.5 運用XOR比對方式……………………………………………… 12
表2.6 Jaccard similarity與XOR方法的差異……………………… 14
表4.1 實驗工具及功能………………………………………………… 28
表4.2 時間比對(單位為秒)…………………………………………… 34
表4.3 準確率比對……………………………………………………… 35
表4.4 L10-1時間比較(單位為秒)……………………………………… 37
表4.5 L10-1準確率比較……………………………………………… 38
表4.6 L10-2時間比較(單位為秒)…………………………………… 39
表4.7 L10-2準確率比較……………………………………………… 40

中文文獻
[1] 蔡美慧，“以偏好導向為相似性量測的協同過濾式推薦”，朝陽科技大學資訊管理系碩士論文，2010。
[2] 曾小波，魏祖寬，金在弘，(2010)“協同過濾系統的矩陣稀疏性問題研究”，計算機應用(Journal of Computer Applications)，第30卷第4期，頁1079-1082。
[3] 顧立志，(2014)“協同過濾數據稀疏性問題研究”，《計算機光盤軟件與應用》，2014年第8期，頁101-102。
[4] 曹一鸣，(2012)，“協同過濾推薦瓶頸問題綜述”，中國期刊全文數據庫，軟件2012，第33卷第12期，頁315-321。
英文文獻
[5] H. J. Ahn, (2008), “A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem,” Information Sciences, Vol. 178, No. 1, pp. 37-51.
[6] M. Y. H. Al-Shamri, K. K. Bharadwaj, (2008), “Fuzzy-genetic approach to recommender systems based on a novel hybrid user model,” Expert Systems with Applications, Vol. 35, No. 3, pp. 1386-1399.
[7] M. Y. H. Al-Shamri and K. K. Bharadwaj (2008), “Fuzzy-genetic approach to recommender systems based on a novel hybrid user model,” Expert Systems with Applications, Vol. 35, No. 3, pp. 1386-1399.
[8] Wei Chen, Simon Fong, (2010), “Social network collaborative filtering framework and online trust factors: a case study on facebook,” The Fifth International Conference on Digital Information Management (ICDIM), pp. 266-273, Alexandria, VA, USA .
[9] Y. L. Chen, L. C. Cheng, (2008), “A novel collaborative filtering approach for recommending ranked items,” Expert Systems with Applications, Vol. 34, No. 4, pp. 2396-2405.
[10] J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl, (1999), “An algorithmic framework for performing collaborative filtering,” The 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 230-237, New York, NY, USA.
[11] J. Herlocker, J. A. Konstan, and J. Riedl (2002), “An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms,”Information Retrieval, Vol. 5, No. 4, pp. 287-310.
[12] J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl (2004),“Evaluating collaborative filtering recommender systems,” ACM Transactions on Information Systems, Vol. 22, No. 1, pp. 5-53.
[13] B. Jeong, J. Lee, and H. Cho, (2009), “An iterative semi-explicit rating method for building collaborative recommender systems, ” Expert Systems with Applications, Vol. 36, No. 3, Part 2, pp. 6181-6186.
[14] Hao Ji, Jinfeng Li, Changrui Ren, Miao He,(2013), “Hybrid collaborative filtering model for improved recommendation,” Proceedings of 2013 IEEE International Conference on Service Operations and Logistics, and Informatics, pp. 142-145, Dongguan, China.
[15] V. Kavinkumar, Rahul Reddy Rachamalla, Balasubramanian Rohit, M. Sridhar, Sridharan.K,Dr. D.Venkataraman, (2015), “A hybrid approach for recommendation system with added feedback component,” 2015IEEE, International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 745-752, Kerala Kochi, India .
[16] Venkatanareshbabu Kuppili, Gayatri Pradip Kudchadker, Ankush Arora, Deepak Kumar, (2015), “Variance based product recommendation using clustering and sentiment analysis,” 2015 IEEE Workshop on Computational Intelligence: Theories, Applications and Future Directions (WCI), pp. 1-5, Kanpur, India.
[17] K. Kwon, J. Cho, and Y. Park, (2009), “Multidimensional credibility model for neighbor selection in collaborative recommendation,” Expert Systems with Applications, Vol. 36, No. 3, Part 2, pp. 7114-7122.
[18] Chun-Cheng Lin, Tzong-Shyan Lin, Wan-Yu Liu, (2012), “A trust and distrust mechanism for a social network-based recommendation system,” The 15th International Symposium on Wireless Personal Multimedia Communications, pp. 172-176, Taipei, Taiwan .
[19] Jian-ping Liu, Yong Wang, Feng-Hua Yan, (2010), “An improved collaborative filtering recommendation algorithm, ” The 1st International Conference on Networking and Distributed Computing, pp. 194-198, Madrid, Spain.
[20] Peng-yu Lu, Xiao-xiao WU, De-ning Teng, (2015), “Hybrid recommendation algorithm for e-commerce website,” 8th International Symposium on Computational Intelligence and Design, pp. 197-200, Hangzhou, China.
[21] Ziqi Lin, Wancheng Ni, Haidong Zhang, Meijing Zhao, Yiping Yang, (2015), “A k-medoids algorithm based method to alleviate the data sparsity in collaborative filtering,” Proceedings of the 34th Chinese Control Conference, pp. 4974-4979, Hangzhou, China.
[22] Zhang Mu, Chen Yi, Zhang Xiaohong, Lai Junyong, (2009), “Study on the recommendation technology for tourism information Service,” 2009 IEEE, Second International Symposium on Computational Intelligence and Design, pp.410-415.
[23] M. P. O'Mahony, N. J. Hurley, and G. C. M. Silvestre, (2004), “An evaluation of neighbourhood formation on the performance of collaborative filtering,” Artificial Intelligence Review, Vol. 21, No. 3, pp. 215-228.
[24] M. Papagelis, D. Plexousakis, (2005), “Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents,” Engineering Applications of Artificial Intelligence, Vol. 18, No. 7, pp. 781-789.
[25] C.H. Piao, J. Zhao, and L. J. Zheng, (2009), “Research on entropy-based collaborative filtering algorithm and personalized recommendation in e-commerce,” Service Oriented Computing and Applications, Vol. 3, No. 2,pp. 147-157.
[26] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, (2000), “Analysis of recommendation algorithms for e-commerce,” The 2nd ACM Conference on Electronic Commerce, pp. 158-167, Minneapolis, Minnesota.
[27] J. B. Schafer, J. Konstan, and J. Riedi (1999), “Recommender systems in e-commerce,” Proceedings of the 1st ACM Conference on Electronic Commerce, pp. 158-166, Denver, Colorado, USA.
[28] Sabine Schlick, Isabella Eigner and Alex Fechner, (2015), “Using collective intelligence to generate trend-based travel recommendations, ” The 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K), pp. 177-185, Lisbon, Portugal.
[29] Ye Tao, (2015) “Design of large scale mobile advertising recommendation system, ” The 4th International Conference on Computer Science and Network Technology (ICCSNT), pp. 763-767, Harbin, China.
[30] Quan Wen, Jianmin He, (2006), “Personalized recommendation services based on service-oriented architecture,” IEEE, Asia-Pacific Conference on Services Computing (APSCC'06), pp. 356-361, Guangzhou, Guangzhou, China.
[31] Li-Tung Weng, Yue Xu, Yuefeng Li, (2005), “A framework for e-commerce oriented recommendation systems,” Proceedings of the 2005 International Conference on Active Media Technology, 2005. (AMT 2005), pp. 309-314, Kagawa, Japan.
[32] Yueping Wu, Jianguo Zheng, (2010), “A collaborative filtering recommendation algorithm based on improved similarity measure method, ” IEEE International Conference on Progress in Informatics and Computing, pp. 246-249, Shanghai, China.
[33] Junrui Yang, Cai Yang, Xiaowei Hu, (2016), “A study of hybrid recommendation algorithm based on user,” 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), pp. 261-264, Hangzhou China.
[34] Peng Yi, Chen Yang, Xiaoming Zhou, Chen Li, (2016), ” A movie cold-start recommendation method optimized similarity measure,” 2016 16th International Symposium on Communications and Information Technologies (ISCIT), pp. 231-234, China Qingdao.
[35] Jinbo Zhang, Zhiqing Lin, Bo Xiao, Chuang Zhang, (2009), “An optimized item-based collaborative filtering recommendation algorithm, ” IEEE International Conference on Network Infrastructure and Digital Content, pp. 414-418.
[36]http://dns-learning.twnic.net.tw/internet/intro1.html
[37]http://www.inside.com.tw/2010/05/14/internet-and-apple
[38]https://zh.wikipedia.org/wiki/%E9%80%BB%E8%BE%91%E5%BC%82%E6%88%96
[39]https://en.wikipedia.org/wiki/Jaccard_index
[40]http://arthur0615.pixnet.net/blog/post/85675350-jaccard-similarity%E7%9B%B8%E4%BC%BC%E5%BA%A6
[41]http://tiredapple.pixnet.net/blog/post/4757594-%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92%E4%B8%AD%E7%9A%84%E7%9B%B8%E4%BC%BC%E6%80%A7%E5%BA%A6%E9%87%8F