Contents & References of Presenting a model for ranking web documents based on user interactions
List:
Abstract..1
Chapter One: Research Overview ..
1-1 Introduction..3
1-2 Information Retrieval.4
1-3 Motivation..5
1-4 Search Engine..9
1-5 Indexing and Processing Inquiry. 11
1-6 Multi-indicator decision-making. 13
1-7 Statement of the basic research problem. 13
1-8 Importance and necessity of conducting research. 14
1-9 Specific objectives of the research. 16
1-10 Research hypotheses. 16
Chapter Two: Overview of the work done
1-2 Introduction.. 18
2-2 text-based ranking.19
2-2-1 vector space model.19
2-2-2 probabilistic model.20
2-3 connection-based ranking.22
2-3-1 query-independent ranking.23
2-3-2 query-dependent ranking 27
2-3-3 link-based ranking challenges.31
2-4 hybrid ranking.34
2-5 learning-based ranking.37
2-6 ranking based on user behavior.39
2-6-1 document expansion.42
2-6-2 NM method.45
2-6-3 CVM method.43
2-6-4 LA method.45
2-6-5 A method for web ranking by defining popularity factors.46
2-6-6 Marko model of user behavior as a predictor for a successful search.51
Chapter three: Description of the proposed method
3-1 Analysis of a multi-criteria system.64
3-2 Review of multi-criteria decision making process.64
3-2-1 De-scaling.66
3-2-2 Weighting of indicators.67
3-3 Description of TOPSIS method.69
3-4 Proposed method.71
Chapter four: Implementation and evaluation The proposed method 4-1 Characteristics of the proposed method 75 4-2 Description of the proposed model simulation 76 4-3 Example of the proposed model simulation 79 Chapter Five: Conclusion 5-1 Discussion and conclusion 82 5-2 Advantages of the proposed method 83 5-3 Future works 84
List of references 85
Source:
[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, Nov 2004.
[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 information 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 journal of operational research. Volume 147,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 User Preferences. 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, August 16-19, Salvador, Brazil, 2005. pp:186-193. [30] Signorini A. A Survey of Ranking Algorithms. Department of Computer Science University of Iowa, September 11, 2005