跳到主要內容

臺灣博碩士論文加值系統

(3.231.230.177) 您好!臺灣時間:2021/08/02 11:30
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:李建宏
研究生(外文):Chien-Hung, Li
論文名稱:可改善推薦系統評價預測之使用者分群方法研究
論文名稱(外文):Improving the Prediction Accuracy of Recommender Systems by Clustering Users Based on User and Group Similarity
指導教授:劉立頌
指導教授(外文):Alan, Liu
口試委員:周忠信鄭有進
口試委員(外文):Jung Shin, JowYu Chin, Cheng
口試日期:2012-07-19
學位類別:碩士
校院名稱:國立中正大學
系所名稱:電機工程研究所
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:中文
論文頁數:65
中文關鍵詞:使用者分群動態門檻值協同式過濾評價預測推薦系統
外文關鍵詞:User clusterDynamic thresholdCollaborative filteringRating predictionRecommendation system
相關次數:
  • 被引用被引用:3
  • 點閱點閱:464
  • 評分評分:
  • 下載下載:59
  • 收藏至我的研究室書目清單書目收藏:1
推薦系統已經成功運用在許多地方,而過去學者們也不斷提出新方法或架構,希望能得到更好的推薦準確度。而系統在進行推薦時,群組中若含有大量使用者,會導致系統在進行推薦時耗費大量時間成本。為了解決這個問題,本研究提出一套使用者分群演算法,利用目標使用者與群組的平均相似度做為判斷依據,當相似度大於門檻值,則可判斷使用者與群組為相似。其中門檻值的設計我們是採用動態門檻值,透過目標群組的人數與系統中最大群組的人數為基礎,產生動態門檻值,運用此門檻值可以解決固定門檻值所發生的問題,且可以使得系統更有彈性,不論系統中使用者如何增加,這套門檻值也能動態的更改。經由本研究所提出的分群演算法,可以將使用者分成多個群組,群組中的使用者可視為相似使用者,之後系統在進行推薦時,若需要找尋相似使用者,只需要與群組中的使用者做比對,如此一來可以減少系統在進行推薦時的時間,同時也可以增加預測的準確度。我們最後使用MovieLens電影資料庫做為實驗資料,證明此分群演算法比協同式過濾有更好的效率以及準確度。

Recommender systems have become a popular issue in the mid-1990s. Scholars have proposed a lot of method and framework to improve the prediction accuracy. This thesis presents a clustering algorithm according to user and group similarity. We calculate the similarity between users by Pearson correlation coefficient based on user’s ratings. By using this similarity, we can calculate the average similarity between the target user and existing groups. If the average similarity is higher than the threshold which we set, the target user will be clustered on the group that has the highest average similarity. The threshold we propose is a dynamic value based on the size of group. The larger the group, the lower the threshold becomes. The design of threshold could avoid some problem of static threshold and makes our method more flexible. According to the clustering algorithm that we present, it is not only able to improve the prediction accuracy but also decreases the execution time. We present experimental results with the MovieLens data set to show that the proposed method has better performance and prediction accuracy than a pure collaborative filtering.
目錄
摘要 i
Abstract ii
第一章 緒論 1
1.1研究動機與目的 1
1.2論文架構 2
第二章 背景知識探討 3
2.1推薦系統介紹 3
2.2推薦系統常用技術 3
2.2.1內容導向篩選技術 3
2.2.2協同式過濾 4
2.2.3混合式篩選技術 14
2.3推薦系統評測機制 15
2.4推薦系統的問題與解決方法 17
2.5 結論 18
第三章 研究方法 20
3.1 研究流程 20
3.2 使用者分群演算法 22
3.3 門檻值設計 31
3.4 時間複雜度比較 33
3.5 研究方法結論 34
第四章 系統實做與驗證 36
4.1 資料來源與使用工具 36
4.2 需求描述 36
4.3 系統分析與設計 37
4.3.1使用者評價矩陣分析與設計 38
4.3.2使用者相似度表格分析與設計 39
4.3.3使用者平均評價表格分析與設計 40
4.3.4使用者分群演算法分析與設計 41
4.3.5產生預測表格分析與設計 43
4.3.6計算平均誤差分析與設計 45
4.4 實驗劇本與結果 45
第五章 結論與未來展望 59
參考文獻 61

參考文獻
[1] G. Lekakos, and G. M. Giaglis, “Improving the Prediction Accuracy of Recommendation Algorithms: Approaches Anchored on Human Factors,” Interacting with Computers 18, pp. 410-431, 2006.
[2] N. Belkin and B. Croft, “Information Filtering and Information Retrieval,” Comm. ACM, vol. 35, no. 12, pp. 29-37, 1992.
[3] R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval. Addison-Wesley, 1999.
[4] G. Salton, Automatic Text Processing. Addison-Wesley, 1989.
[5] X. Yang, Y. Guo, and Y. Liu, “Bayesian-inference based recommendation in online social networks,” in INFOCOM, 2011 Proceedings IEEE, 2011, pp. 551–555.
[6] M. Pazzani and D. Billsus, “Learning and Revising User Profiles: The Identification of Interesting Web Sites,” Machine Learning, vol. 27, pp. 313-331, 1997.
[7] R. R. Liu, C. X. Jia, T. Zhou, D. Sun, and B. H. Wang, “Personal recommendation via modified collaborative filtering,” Physica A: Statistical Mechanics and its Applications, vol. 388, no. 4, pp. 462–468, 2009.
[8] P. Cano, et al., “Content-based music audio recommendation,” Proc. ACM Multimedia, pp. 212-212, 2005.
[9] B. N. Miller, I. Albert, S. K. Lam, J. A. Konstan, et al., “MovieLens Unplugged: Experiences with an Occasionally Connected Recommender System,” Proceedings of the 8th International Conference on Intelligent User Interfaces, pp. 263-266, 2003.
[10] D. Billsus and M. Pazzani, “User Modeling for Adaptive News Access,” User Modeling and User-Adapted Interaction, vol. 10, nos. 2- 3, pp. 147-180, 2000.
[11] G. Adomavicius, and A. Tuzhilin, “Towards the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions,” IEEE Transactions on Knowledge and Data Engineering 17, pp. 734-749, 2005.
[12] J. Delgado and N. Ishii, “Memory-Based Weighted-Majority Prediction for Recommender Systems,” Proc. ACM SIGIR ’99 Workshop Recommender Systems: Algorithms and Evaluation, 1999.
[13] Goldberg, D., Nichols, D., Oki, B.M. and Terry, D. Using Collaborative Filtering to Weave an Information Tapestry. Communications of the ACM, 35, 12 (1992), pp. 61-70
[14] E. Rich, “User Modeling via Stereotypes,” Cognitive Science, vol. 3, no. 4, pp. 329-354, 1979.
[15] J.A. Konstan, B.N. Miller, D. Maltz, J.L. Herlocker, L.R. Gordon, and J. Riedl, “GroupLens: Applying Collaborative Filtering to Usenet News,” Comm. ACM, vol. 40, no. 3, pp. 77-87, 1997.
[16] W. Hill, L. Stead, M. Rosenstein, and G. Furnas, “Recommending and Evaluating Choices in a Virtual Community of Use,” Proc. Conf. Human Factors in Computing Systems, 1995.
[17] U. Shardanand and P. Maes, “Social Information Filtering: Algorithms for Automating ‘Word of Mouth’,” Proc. Conf. Human Factors in Computing Systems, 1995.
[18] P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, “GroupLens: An Open Architecture for Collaborative Filtering of Netnews,” Proceedings of the ACM Conference on Computer Supported Cooperative Work, pp. 175-186, 1994.
[19] 廖品妍(2010),以顯性評價為主之相似推薦,朝陽科技大學資訊管理系碩士論文。
[20] H. C. Chen, and A. Chen, “A Music Recommendation System Based on Music Data Grouping and User Interest,” Proceedings of the 10th International Conference on Information and knowledge management, pp. 231-238, 2001.
[21] A. L. Chen, Yi-Hung Wu, and Yong-Chuan Chen, “Enabling Personalized Recommendation on the Web based on User Interests and Behaviors,” In 11th International Workshop on research Issues in Data Engineering, pp.17-24, 2001.
[22] 張維辰(2011),加強式分類導向相似度評估法於使用者模型比較之研究,國立中正大學電機工程研究所碩士論文。
[23] J.S. Breese, D. Heckerman, and C. Kadie, “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” Proc. 14th Conf. Uncertainty in Artificial Intelligence, July 1998.
[24] T. K. Quan, I. Fuyuki, and H. Shinichi, “Improving accuracy of recommender system by clustering items based on stability of user similarity,” Computational Intelligence for Modeling, Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, 2006, p. 61.
[25] A. Nakamura and N. Abe, “Collaborative Filtering Using Weighted Majority Prediction Algorithms,” Proc. 15th Int’l Conf. Machine Learning, 1998.
[26] D. Billsus and M. Pazzani, “Learning Collaborative Information Filters,” Proc. Int’l Conf. Machine Learning, 1998.
[27] T. Hofmann, “Collaborative Filtering via Gaussian Probabilistic Latent Semantic Analysis,” Proc. 26th Ann. Int’l ACM SIGIR Conf., 2003.
[28] Y.-H. Chien and E.I. George, “A Bayesian Model for Collaborative Filtering,” Proc. Seventh Int’l Workshop Artificial Intelligence and Statistics, 1999.
[29] L. Getoor and M. Sahami, “Using Probabilistic Relational Models for Collaborative Filtering,” Proc. Workshop Web Usage Analysis and User Profiling (WEBKDD ’99), Aug. 1999.
[30] L.H. Ungar and D.P. Foster, “Clustering Methods for Collaborative Filtering,” Proc. Recommender Systems, Papers from 1998 Workshop, Technical Report WS-98-08 1998.
[31] P. Melville, R. J. Mooney, and R. Nagarajan, “Content-boosted collaborative filtering for improved recommendations,” in Proceedings of the National Conference on Artificial Intelligence, 2002, pp. 187–192.
[32] M. Claypool, A. Gokhale, T. Miranda, P. Murnikov, D. Netes, and M. Sartin, “Combining Content-Based and Collaborative Filters in an Online Newspaper,” Proc. ACM SIGIR ’99 Workshop Recommender Systems: Algorithms and Evaluation, Aug. 1999.
[33] B. Marlin, “Modeling User Rating Profiles for Collaborative Filtering,” Proc. 17th Ann. Conf. Neural Information Processing Systems (NIPS ’03), 2003.
[34] Marko Balabanović, Yoav Shoham, Fab: content-based, collaborative recommendation, Communications of the ACM, v.40 n.3, p.66-72, March 1997.
[35] A.I. Schein, A. Popescul, L.H. Ungar, and D.M. Pennock, “Methods and Metrics for Cold-Start Recommendations,” Proc. 25th Ann. Int’l ACM SIGIR Conf.,2002
[36] N. Good, J.B. Schafer, J.A. Konstan, A. Borchers, B. Sarwar, J.L. Herlocker, and J. Riedl, “Combining Collaborative Filtering with Personal Agents for Better Recommendations,” Proc. Conf. Am. Assoc. Artificial Intelligence (AAAI-99), pp. 439-446, July 1999.
[37] I. Soboroff and C. Nicholas, “Combining Content and Collaboration in Text Filtering,” Proc. Int’l Joint Conf. Artificial Intelligence Workshop: Machine Learning for Information Filtering, Aug. 1999.
[38] R. Burke, “Knowledge-Based Recommender Systems,” Encyclopedia of Library and Information Systems, A. Kent, ed., vol. 69, Supplement 32, Marcel Dekker, 2000.
[39] C. Basu, H. Hirsh, and W. Cohen, “Recommendation as Classification: Using Social and Content-Based Information in Recommendation,” Recommender Systems. Papers from 1998 Workshop, Technical Report WS-98-08, AAAI Press 1998.
[40] T. Fawcett, “An introduction to ROC analysis,” Pattern recognition letters, vol. 27, no. 8, pp. 861–874, 2006.
[41] Z. Huang, H. Chen, and D. Zeng, “Applying Associative Retrieval Techniques to Alleviate the Sparsity Problem in Collaborative Filtering,” ACM Trans. Information Systems, vol. 22, no. 1, pp. 116- 142, 2004.
[42] MovieLens Data Sets. [Online]. Available:http://www.grouplens.org/node/73 [July. 18, 2012].
[43] J. Bobadilla, F. Serradilla, and J. Bernal, “A new collaborative filtering metric that improves the behavior of recommender systems”. Knowledge Based Systems, vol. 23, no. 6, pp. 520–528, 2010.
[44] S. Gong and G. Cheng, “Mining User Interest Change for Improving Collaborative Filtering,” Second International Symposium in Intelligent Information Technology Application, pp. 24–27, 2008.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關期刊