پایان نامه ارائه مدلی برای رتبه‌ بندی اسناد وب بر اساس تعاملات کاربران

فهرست:

چکیده......................................................................................................................................................1

فصل اول: کلیات پژوهش                                                                                                

1-1 مقدمه...............................................................................................................................................3

1-2 بازیابی اطلاعات...............................................................................................................................4

1-3 انگیزش............................................................................................................................................5

1-4 موتور جستجو..................................................................................................................................9

1-5 نمایه‌سازی و پردازش پرس‌و‌جو....................................................................................................11

1-6 تصمیم‌گیری چند شاخصه.............................................................................................................13

1-7 بیان مسأله اساسی تحقیق...............................................................................................................13

1-8 اهمیت و ضرورت انجام تحقیق....................................................................................................14

1-9 اهداف مشخص تحقیق..................................................................................................................16

1-10 فرضیه‏های تحقیق........................................................................................................................16

فصل دوم :مروری بر کارهای انجام شده

2-1 مقدمه.............................................................................................................................................18

2-2 رتبه‌بندی بر مبنای متن...................................................................................................................19

2-2-1 مدل فضای برداری....................................................................................................................19

2-2-2 مدل احتمالی.............................................................................................................................20

2-3 رتبه‌بندی مبتنی بر اتصال...............................................................................................................22

2-3-1 رتبه‌بندی مستقل از پرس وجو..................................................................................................23

2-3-2 رتبه‌بندی وابسته به پرس وجو..................................................................................................27

2-3-3 چالش‌های رتبه‌بندی بر اساس پیوند.........................................................................................31

2-4 رتبه‌بندی ترکیبی............................................................................................................................34

2-5 رتبه‌بندی مبتنی بر یادگیری............................................................................................................37

2-6 رتبه‌بندی مبتنی بر رفتار کاربر........................................................................................................39

2-6-1 گسترش سند.............................................................................................................................42

2-6-2 روش NM................................................................................................................................45

2-6-3 روش CVM ............................................................................................................................43

2-6-4 روش LA ................................................................................................................................45

2-6-5 روشی برای رتبه‌بندی وب با تعریف فاکتورهای محبوبیت.......................................................46

2-6-6 مدل مارکو از رفتار کاربر به عنوان یک پیش بینی‌گر در جهت یک جستجوی موفق..............51

فصل سوم: شرح روش پیشنهادی                                                                                         

3-1 تحلیل یک سیستم چند معیاره.......................................................................................................64

3-2 بررسی فرآیند تصمیم‌گیری چند شاخصه......................................................................................64

3-2-1 بی مقیاس کردن........................................................................................................................66

3-2-2 وزن دهی به شاخص‌ها.............................................................................................................67

3-3 توصیف روش TOPSIS...............................................................................................................69

3-4 روش پیشنهادی  .............................................................................................................................71

فصل چهارم: پیاده سازی و ارزیابی روش پیشنهادی

4-1 خصوصیات روش پیشنهادی  .........................................................................................................75

4-2 توصیف شبیه‌سازی مدل پیشنهادی  .76

4-3 نمونه شبیه‌سازی مدل پیشنهادی  79

فصل پنجم: نتیجه گیری 

5-1 بحث و نتیجه گیری  82

5-2 مزایای روش پیشنهادی  83

5-3  کارهای آینده 84

فهرست منابع  85

منبع:

[1]  Baeza-Yates R, and Ribeiro-Neto B. Modern Information Retrieval. ACM Press /    Addison-Wesley, 2005.

[2] Bharat K, Henzinger MR. Improved algorithms for topic distillation in a                 hyperlinked environment. In Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Melbourne, Australia, August 2006. pp:104-111.

[3] Kleinberg M. Authoritative sources in a hyperlinked environment. Journal of the   ACM, Vol. 46 No. 5, September 1999. pp:604–632.

[4]   Kent  P. Search Engine Optimization for dummies. John Wiley & Sons Inc. 2008.

[5]  Keyhanipour AH, Moshiri B, Piroozmand M, Lucas C. Aggregation of Multiple Search Engines Based on Users’ Preferences in WebFusion. Elsevier Journal of Knowledge-Based Systems, Vol. 20, No. 4, May 2007. pp:321–328.

[6]   Liu TY, Qin T, Xu J, Xiong W, Li H. LETOR: Benchmark dataset for research on learning to rank for information retrieval. In SIGIR Workshop on Learning to Rank for Information Retrieval, 2007.

[7]   Castillo C. Effective Web Crawling, Ph.D. Thesis, University of Chile, Nov2004.

[8]  Baeza-Yates R. Challenges in the interaction of information retrieval and natural language processing. In Proceedings of 5th international conference on Computational Linguistics and Intelligent Text Processing (CICLing), Lecture Notes in Computer Science Springer, Vol. 2945, February 2004. pp:445–456.

[9]   Tomasic A,  Garcia-Molina H. Performance of inverted indices in  sharednothing distributed text document  informatioon retrieval systems. In  Proceedings of the second international conference on Parallel and distributed  information systems,IEEE Computer Society Press,1993. pp: 8–17.

[10]  Moussea V. Figueria J. Gomes silv C. Resolving Inconsistencies Among Constraints on the parameters of MCDA model" , European jornal of operational research. Volume 147, 2006. pp: 72-93.

[11]  Zhang Y, Chen W, Wang D, Yang Q. User-click modeling for understanding and predicting search-behavior. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD'11). ACM, New York, NY, USA, August 2011. pp: 1388-1396.

[12]  Zhao D, Zhang M, Zhang D. A Search Ranking Algorithm Based on UserPreferences. Journal of Computational Information Systems, November 2012.  pp: 8969-8976.

[13]  Attenberg J, Pandey S, Suel T. Modeling and predicting user behavior in sponsored search. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '09). ACM, New York, NY, USA,2009. pp:1067-1076.

 [14]  Yu J, Lu Y, Sun S, Zhang F. Search Results Evaluation Based on User Behavior. Springer-Verlag Berlin Heidelberg, vol. 320,2013. pp: 397-403.

 [15]  Dupret G, Liao C. A model to estimate intrinsic document relevance from the clickthrough logs of a web search engine. In Proceedings of the third ACM international conference on Web search and data mining (WSDM '10). ACM, New York, NY, USA, February 2010. pp: 190-181.

 [16]  Liu C, White RW, Dumais S. Understanding web browsing behaviors through Weibull analysis of dwell time. In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval (SIGIR '10). ACM, New York, NY, USA,2010. pp: 386-379.

 [17]     Yang C, Liu C, Shao-chieh H, A Hybrid Item-Based Recommendation Ranking Algorithm Based on User Access Patterns. Springer-Verlag Berlin Heidelberg, vol. 163,2013. pp: 233-225.

[18]  Guo F, Liu C, Wang Y. Efficient Multiple-Click Models in Web Search. The definitive version will appear in WSDM ’09: Proceedings of the second ACM international conference on web search and data mining. 2008 ACM.

[19]  Salton G, Buckley C. Term-weighting approaches in automatic text retrieval. Information Processing and Management: an International Journal, Vol. 24 No.5,1988. pp:513–523.

[20]  Salton G. The SMART retrieval system - experiments in automatic document  processing. Prentice-Hall, 1971.

 [21]  Zhai C. A brief review of information retrieval models. Technical report, Department of Computer Science, University of Illinois at Urbana-Champaign, 2010.

[22]  Sparck Jones K, Walker S, Robertson SE. A probabilistic model of information retrieval: development and comparative experiments - part 1 and part 2. Information Processing and Management, Vol. 36 No. 6,2012. pp:779-808 and 804-809.

[23]  Robertson SE, Walker S, Jones S, Gatford M. Okapi at TREC-3.In Harman, D. K., editor, The Third Text REtrieval Conference (TREC-3), pp: 109-126.

[24]  Neelam Duhan, A. K. Sharma, Komal Kumar Bhatia, page ranking Algorithms: A Survey, IEEE International Advance Computing Conference IACC 2009 Patiala, India, March 2009. pp: 6-7.

[25]  Jain R, Dr Purohit GN, page ranking Algorithms for Web Mining, International Journal of Computer Applications (0975 – 8887)Volume 13– No.5, January 2011.

[26]  Saxena PC, Gupta JP, Gupta N. Web page ranking Based on Text Content of Linked Pages, International Journal of Computer Theory and Engineering, Vol. 2, No. 1 February, 2010.  pp:1793-1800.

[27]  Haveliwala T. Topic-sensitive pagerank. In Proceedings of the Eleventh Int’l World Wide Web Conf. 2002.

 [28]  Gyongyi Z, Garcia-Molina H, Pedersen J. Combating web spam with TrustRank. In Proceedings of the International Conference on Very Large Databases (VLDB), 2004. pp: 576-587.

 [29]  Xue GR, Yang Q, Zeng HJ, Chen Z. Exploiting the hierarchical structure for link analysis. In SIGIR, August16- 19, Salvador, Brazil, 2005. pp:186-193.

 [30]  Signorini A. A Survey of Ranking Algorithms. Department of Computer Science University of Iowa, September 11, 2005.

[31]  Cho J, Roy S. Impact of search engines on page popularity. In Proceedings of the 13th international conference on World Wide Web (WWW '04). ACM, New York, NY, USA, 2008. pp: 20-29.

[32]  Pandey S, Roy S, Olston C. Shuffling a Stacked Deck: The Case for Partially Randomized Ranking of Search Engine Results. Proceedings of the 31st VLDB Conference ,2007 .pp: 781-792.

[33]  Shakery A, Zhai CX. Relevance propagation for topic distillation uiuc trec 2003 web track experiments. In Proceedings of the TREC Conference, 2003.

 [34] Shakery A, Zhai CX. A probabilistic relevance propagation model for hypertext retrieval, In Proceedings of the 15th ACM International Conference on Information and Knowledge Management (CIKM), 2006. pp: 550-558.

[35]  Qin T, Liu TY, Zhang XD, Chen Z. A study of relevance propagation for web search, In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, ACM Press, 2005. pp: 408–415.

[36]  Yeh JY, Lin JY, Yang WP. Learning to Rank for Information Retrieval Using Genetic Programming, Presented in SIGIR 2007 Workshop, Amsterdam, Netherlands, July 23 – 27. 2007.

 [37]  Joachims T. Optimizing Search Engine using Clickthrough Data. In Proceedings of the ACM Conference on Knowledge Discovery and Data Mining,2004.  pp:132-142.

[38]    Joachims T. Evaluating retrieval performance using clickthrough data. In J. Franke, G. Nakhaeizadeh, and I. Renz, editors, Text Mining, Physica/Springer Verlag, pp.79-96, 2003.

[39]  Gui-Rong X, Hua-Jun Z, Zheng C, Yong Y, WeiGuo F. Optimizing web search using web click-through data. In Proceedings of the thirteenth ACM international conference on Information and knowledge management (CIKM '04). ACM, New York, NY, USA, 2004, pp:118-126.

 [40]  Keyhanipour AH, Moshiri B, Piroozmand M. Lucas C. A Multi- Layer/Multi-Agent Architecture for Meta-Search Engines, International Journal on Artificial Intelligence and machine learning (AIML), Special issue, Vol. 6, March 2006, pp: 1-6.

[41]  Qiu F, Cho J. Automatic Identification of User Interest for Personalized Search. . In Proceedings of the International World-Wide Web Conference,\ Edinburgh, UK, May 22–26, 2009.

[42] Kemp C, Ramamohanarao K. Long-Term Learning for Web Search Engines, Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery, August 19-23, 2008. pp:263-274.

[43] Singh D. Improving Web Search Ranking Through User Behavior Information. International Journal of Information Technology and Knowledge Management July-December, Volume 4, No. 2, 2011. pp: 635-638.

[44]  Hassan A, Jones R ,Klinkner KL. Beyond DCG: user behavior as a predictor of a successful search. In Proceedings of the third ACM international conference on Web search and data mining (WSDM '10). ACM, New York, NY, USA, 2010. pp: 221-230.

 [45] Yurdakul MY. Devolopment of a performance measurement model for manufactuering companies using the AHP and TOPSIS approaches, International jornal of production research, 2008, pp: 21-43.