跳到主要內容

臺灣博碩士論文加值系統

(44.200.169.3) 您好!臺灣時間:2022/12/01 02:19
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

我願授權國圖
: 
twitterline
研究生:沈思瑋
研究生(外文):Sih-Wei Shen
論文名稱:隱含式評價推薦系統
論文名稱(外文):An implicit rating based product recommendation system
指導教授:蔡介元蔡介元引用關係
指導教授(外文):Chieh-Yuan Tsai
口試委員:許嘉裕張啟昌
口試委員(外文):Chia-Yu HsuChi-Chang Chang
口試日期:2015-07-16
學位類別:碩士
校院名稱:元智大學
系所名稱:工業工程與管理學系
學門:工程學門
學類:工業工程學類
論文種類:學術論文
畢業學年度:103
語文別:英文
論文頁數:69
中文關鍵詞:推薦系統隱含式評價協同過濾推薦兩項關係
外文關鍵詞:Recommendation systemsImplicit feedbackCollaborative filteringItem association
相關次數:
  • 被引用被引用:0
  • 點閱點閱:198
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
  在資訊爆炸的時代,用戶面對太多的選擇可選,所以用戶會希望能直接找到他們真正想要的選擇。為了解決這個問題,推薦系統可以解決此問題,讓賣家可以提供買家可能購買的物品。協同過濾(Collaborative filtering) 依賴於用戶的偏好(User profile)來找尋相似的用戶,並利用相似用戶所喜歡的評價來進行推薦。然而,明確的用戶的偏好資訊在現實中並非每次都可以收集得到,因此如何利用隱含性評價資料(Implicit feedback)來轉換成明確的用戶的偏好資訊是很重要的問題。
  本研究的目的是開發一個不需要任何明確的用戶的偏好資訊也可以進行推薦的推薦系統。本研究利用用戶購買歷史記錄中的購買時間和購買次數來轉換成用戶的偏好資訊。如果遇到無任何購買紀錄的新用戶,本研究利用人口統計資料來與舊用戶來進行比較,利用相似的舊客戶資料來解決新客戶的空白評價(Cold start problem )。此外,為了更貼近用戶的需求,我們用最大最小模糊理論結合購買的時間,當前時間和兩項物品關係(Two item association)。透過實驗,我們可以發現單純使用購買次數來轉換使用這評價是不夠的,若利用模糊理論來整合物品兩項關係,用戶偏好和最後一次購買時間可以相對得到最好的推薦結果。

  In the information overloaded age, there are so many options for users to choose. Users usually hope to find what they want without wasting their time and also enable sellers to provide buyers with the items they are likely to purchase. To solve this burden, recommendation systems have emerged in response to this problem. Collaborative filtering, one of the most popular approach for recommendation systems, relies on users whose preferences are similar to those of the target user and recommends items that users have liked. However, explicit feedback is not always available in practice. To solve the above problem, this thesis develops a recommendation system that can derive user preference ratings from users’ purchase history without any explicit feedback provided by the user. If a user did not have any transaction data which call cold start problem, demographic data will be used to compare with old users and take their user preference as new user preference. In addition, this research also considers the time interval between purchased time and current time and item relationship and use max-min fuzzy theory to combine these factors. Through the experiment, the proposed user preference rating approach is better than the one only considering the purchase frequency of an item. The experiment also shows that the performance of considering the time interval between purchased time and current time as well as item relationship is also better than the one without considering them.
ABSTRACT i
摘 要 i
致 謝 ii
Table of Contents iii
List of Figures v
List of Tables vi
Chapter 1 Introduction 1
1.1 Research Background 1
1.2 Research Problem 2
1.3 Research Objectives 3
1.4 Thesis Organization 4
Chapter 2 Literature Review 5
2.1 Recommendation Systems 5
2.2 Applications using Collaborative filtering 8
Chapter 3 Research Method 13
3.1 System overview 13
3.2 Establishing user preference rating using transaction data 16
3.3 Cold start problem 19
3.4 Primary suggestion 22
3.4.1 The best-n-neighbor generation 22
3.4.2 Recency and cycle time consideration 25
3.4.3 Item association 27
3.4.4 Recommendation list generation 29
Chapter 4 Experimental Illustration 32
4.1 Experimental environment 32
4.2 Example Illustration 36
4.3 Experiment Results 48
4.3.1 Five datasets 49
4.3.2 Number of customers 51
4.3.3 Number of recommended items 53
4.3.4 Number of Neighbors 54
4.3.5 Computational Time 56
Chapter 5 Conclusions and Future Work 59
5.1 Conclusions 59
5.2 Future Work 60
References 62


1.Adomavicius, G., and Tuzhilin, A., “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(6), pp. 734–749, 2005.

2.Albadvi, A., and Shabbazi M., “A hybrid recommendation technique based on product category attributes,” Expert Systems with Application, 36(9), pp. 11480-11488, 2009.

3.Aggarwal, C. C., Procopiuc, C., and Yu, P. S., “Finding localized associations in market basket data,” IEEE Transactions on Knowledge and Data Engineering 14(1), pp. 51-62, 2002.

4.Balabanovic, M., and Shoham, Y., “Content-based, collaborative recommendation,” Communications of the ACM 40(3), pp. 66-72, 1998.

5.Belkin, N. J., and Croft, W. B., “Information filtering and information retrieval: two sides of the same coin,” Communications of the ACM 35(12), pp. 29–38, 1992.

6.Bennett, J., and Lanning, S., “The Netflix Prize,” In Proceedings of KDD cup and workshop, 2007.

7.Billsus, D., Brunk, C. A., Evans, C., Gladish, B., and Pazzani, M., “Adatpvie interfaces for ubiquitous web access,” Communications of the ACM 45(5), pp. 34-38, 2002.

8.Choi, K., Yoo, D., Kim, G., and Suh, Y., “A hybrid online-product recommendation system: Combining implicit rating-based collaborative filtering and sequential pattern analysis,” Electronic Commerce Research and Applications 11(4), pp. 309-317, 2012.

9.Das,M., Sihem, A. Y., Das, G., and Yu, C., “MRI: Meaningful Interpretations of Collaborative Ratings,” Proceedings of the VLDB Endowment 4(11), pp. 1063–1074, 2011.

10.Simon R. D, Tengke, X. and Wang, S., “Combining Collaborative Filtering and Clustering for Implicit Recommender System,” Advanced Information Networking and Applications IEEE 27, pp. 748-755, 2013.

11.Goldberg, D., Nichols, D., Oki, B. M., and Terry, D., “Using Collaborative Filtering to Weave an Information Tapestry,” Communications of the ACM 35(12), pp. 61–70, 1992.

12.Hill, W., Stead, L., Rosenstein, M., and Furnas, G., “Recommending and Evaluating Choices in a Virtual Community of Use,” In Proceedings of CHI '95, pp. 194-201, 1995.

13.Hu, Y., Koren, Y., and Volinsky, C., “Collaborative filtering for Implicit Feedback Datasets,” IEEE International Conference on Data Mining 08, pp. 263-272, 2008.

14.Huang, C. L., and Huang, W. L., “Handling sequential pattern decay: developing a two stage collaborative recommendation system,” Electronic Commerce Research and Applications 8(3), pp. 117-129, 2009.

15.Jin, R., Si, L., Zhai, C. X., and Callan, J., “Collaborative Filtering with Decoupled Models for Preferences and Ratings,” Communications of the ACM 1, pp. 309-316, 2003.

16.Konstan, J., Miller, B., Maltz, D., Herlocker, J.,Gordon, L., and Riedl, J., “GroupLens: Applying Collaborative Filtering to Usenet News,” Communications of the ACM 40(3), pp. 77-87, 1997.

17.Koren, Y., “Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model,” KDD’08, pp. 426-434, 2008.

18.Kim, Y. S., Yum, B. J., Song, J., and Kim, S. M., “Development of a recommender system based on navigational and behavioral patterns of customers in e-commerce sites,” Expert Systems with Applications 28, pp. 381-393, 2005.

19.Kim, H. N., Ji, A. T., Ha, I., and Jo, G. S., “Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation,” Electronic Commerce Research and Applications 9(1), pp. 73-83, 2010.

20.Kelly, D., and Teevan, J., “Implicit feedback for inferring user preference: A bibliography,” Communications of the ACM 37(2), pp. 18–28, 2003.

21.Kabassi, K., “Personalizing recommendation for tourists,” Telemetric and Informatics, 27(1), pp. 51-66, 2010.

22.Lawrence, R. D., Almasi, G. S., Korlyar, V., Viveros, M. S., and Duri, S. S., “Personalization of supermarket product recommendations,” Data Mining and Knowledge Discovery 5(1), pp. 11–32, 2001.

23.Linden, G., Smith, B., and York, J., “Amazon.com Recommendations: Item-to-item Collaborative Filtering,” IEEE Internet Computing 7, pp. 76–80, 2003.

24.Liu, D. R., and Shih, Y. Y., “Hybrid approaches to product recommendation based on customer life time value and purchase preferences,” The Journal of Systems and Software 77, pp. 181-191, 2005.

25.Liu, D. R., and Shih, Y. Y., “Product recommendation approaches: Collaborative filtering via customer lifetime value and customer demands,” Expert Systems with Applications 35, pp. 350-360, 2008.

26.Liu, D. R., Lai, C. H., and Lee, W. J., “A hybrid of sequential rules and collaborative filtering for product recommendation,” Information Sciences 179(20), pp. 3505-3519, 2009.

27.Liu, D. R., and Shih, Y. Y., “Integrating AHP and data mining for product recommendation based on customer lifetime value,” Information &; Management 42(3), pp. 387-400, 2005.

28.Lee, T. Q., Park, Y., and Park, Y. T., “An empirical study on effectiveness of temporal information as implicit ratings,” Expert Systems with Applications 36(2), pp. 1315-1321, 2009.

29.Lee, S. K., Cho, Y. H., and Kim, S. H., “Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations,” Information Sciences 180(11), pp. 2142-2155, 2010.

30.Lee, T. Q., Park, Y., and Park, Y. T., “A time-based approach to effective recommender systems using implicit feedback,” Expert Systems with Applications 34(4), pp. 3055-3062, 2008.

31.Liu, Z., Qu, W., Li, H., and Xie, C., “A hybrid collaborative filtering recommendation mechanism for P2P networks,” Future Generation Computer Systems 26(8), pp. 1409-1417, 2010.

32.Lousame, F., and S´anchez, E., “View-based recommender systems,” RecSys '09 Proceedings of the third ACM conference on Recommender systems, pp. 389–392, 2009.

33.Mao, Q., Feng, B., and Pan, S., “A Study of Top-N Recommendation on User Behavior Data,” IEEE International Conference on Computer Science and Automation Engineering 2, pp. 582-586, 2012.

34.Mobasher, B., Chicago, D. P. U., and Cooley, I. R., “Automatic personalization based on Web usage mining,” Communications of the ACM 43(8), pp. 142-151, 2000.

35.Nakamura, A., and Abe, N., “Collaborative filtering using weighted majority prediction algorithm,” In 15th International Conference of Machine Learning, pp. 395-403, 1998.

36.Oard, D.W., and Kim, J., “Implicit Feedback for Recommender Systems”, Proc. 5th DELOS Workshop on Filtering and Collaborative Filtering, pp. 31–36, 1998.

37.Oard, D.W., and Kim, J., “Modeling information content using observable behavior,” ASIST Annual Meeting 38, pp. 481–488, 2001.

38.Pazzani, M., and Billsus, D., “Learning and revising user profile: the identification of interesting web sites,” Machine Learning 27(3), pp. 313-331, 1997.

39.Pradel, B., Sean, S., and Delporte, J., “A case study in a recommender system based on purchase data,” KDD '11 Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining discovery and data, pp. 377–385, 2011.

40.Park, Y. J., and Chang, K. N., “Individual and group behavior-based customer profile model for personalized product recommendation,” Expert Systems with Applications 36(2), pp. 1932-1939, 2009.

41.Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J., “GroupLens: An open architecture for collaborative filtering of netnews,” In Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, pp. 175-186, 1994.

42.Roy, S. B., Sihem, A. Y., Chawla, A., Das, G., and Yu, C., “Space efficiency in group recommendation,” The VLDB Journal 19(6), pp. 877–900, 2010.

43.Suyun, W., Ye, N., and Zhang, Q., “Time-Aware Collaborative Filtering for Recommender Systems,” Communications in Computer and Information Science 321, pp. 663-670, 2012.

44.Shardanand, U., and Maes, P., “Social Information Filtering: Algorithms for Automating 'Word of Mouth',” Conference on Human Factors in Computing Systems - Proceedings 1, pp. 210-217, 1995.

45.Schafer, J.B., Konstan, J.A., and Riedl, J., “E-Commerce Recommendation Applications,” Data Mining and Knowledge Discovery 5(1), pp. 115-153, 2001.

46.Schafer, J.B., Konstan, J., and Riedl, J., “Recommender Systems in E-Commerce,” EC '99 Proceedings of the 1st ACM conference on Electronic commerce, pp. 158–166, 1999.

47.Sarwar, B., Karypis, G., Konstan, J., and Riedl, J., “Item-based Collaborative Filtering Recommendation Algorithms,” In: 10th ACM Conference on World Wide Web, pp. 285–295, 2001.

48.Salter, J., and Antonopoulos, N., “Cinema screen recommender agent: combining collaborative and content-based filtering,” IEEE Intelligent Systems 21(1), pp. 35-41, 2006.

49.Sarwar, B., karypis, G., konstan, J., and Riedl, J., “Analysis of recommendation algotihms for E-Commerce,” Proceedings of the 2nd ACM Conference on Electronic Commerce, pp. 158-167, 2000.

50.Shih, Y. Y., and Liu, D. R., “Product recommendation approaches: Collaborative filtering via customer lifetime value and customer demands,” Expert Systems with Applications 35(1), pp. 350-360, 2008.

51.Tan, J. F., Lu, E., and Tseng, V., “Preference-oriented mining techniques for location-based store search,” Knowledge &; Information Systems 34(1), pp. 147-169, 2013.

52.Wang, X., and Zhou C., “A Collaborative Filtering Recommendation Algorithm using User Implicit Demographic Information,” Computer Science &; Education 7, pp. 935-939, 2012.

53.Wang, Y., Dai, W., and Yuan, Y., “Website browsing aid: a navigation graph-based recommendation system,” Decision Support Systems 45(3), pp. 387-400, 2008.

54.Wei, C. P., Yang, C. S., and Hsiao, H. W., “A collaborative filtering-based approach to personalized document clustering,” Decision Support Systems 45(3), pp. 413-428, 2008.

55.Wei, S., Ye, N., and Zhang, Q., “Time-Aware Collaborative Filtering for Recommender Systems,” Communications in Computer and Information Science 321, pp. 663-670, 2012.

56.Yin, H., Chang, G., and Wang X., “A Cold-start Recommendation Algorithm Based on New User's Implicit Information and Multi-Attribute Rating Matrix,” International Conference on Hybrid Intelligent Systems 09, pp. 353-358, 2009.

57.Yuan, S., and Chang, W., “Mixed-initiative synthesized learning approach for web-based CRM,” Expert Systems with Applications, 20(2), pp.187–200, 2001.

58.Yu, K., Schwaighofer, A., Tresp, V., and Xiaowei, X., “Probabilistic memory-based collaborative filtering,” Knowledge and Data Engineering 16(1), pp. 56-69, 2004.

59.Yin, H., Chang, G., and Wang, X., “A Cold-Start Recommendation Algorithm Based on New User's Implicit Information and Multi-attribute Rating Matrix,” Hybrid Intelligent Systems 09, pp. 353-358,2009.

60.Zhou, S., Zhou, X., Yu, Z., Wang, K., Wang, H., and Ni, H., “A Recommendation Framework towards Personalized Services in Intelligent Museum,” International Conference on Computational Science and Engineering 09, pp. 229-234, 2009

電子全文 電子全文(本篇電子全文限研究生所屬學校校內系統及IP範圍內開放)
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關期刊