An immune programming-based ranking function discovery approach for effective information retrieval

Expert Systems with Applications

Volumn 37, Issue 8, 2010, Pages 5863-5871

  Wang, Shuaiqiang ^a   Ma, Jun ^a   He, Qiang ^a  

^a SHANDONG UNIVERSITY   (China)

Author keywords

Evolutionary computation; Immune programming; Information retrieval; Learning to rank; Machine learning

Indexed keywords

AFFINITY FUNCTIONS; EVOLUTIONARY COMPUTATIONS; IMMUNE PROGRAMMING; IMMUNE SYSTEMS; LEARNING TO RANK; MACHINE LEARNING ALGORITHMS; MACHINE-LEARNING; OPTIMIZATION PROBLEMS; RANKING FUNCTIONS; RANKING METHODS; SOLUTION REPRESENTATION;

ANTIBODIES; COMPUTER SOFTWARE SELECTION AND EVALUATION; CONVERGENCE OF NUMERICAL METHODS; EVOLUTIONARY ALGORITHMS; GENETIC PROGRAMMING; INFORMATION RETRIEVAL; LEARNING SYSTEMS;

LEARNING ALGORITHMS;

EID: 77951207917     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2010.02.019     Document Type: Article

References (30)

1. Baeza-Yates R., and Ribeiro-Neto B. Modern information retrieval (1999), Addison Wesley
2. Cao, Y., Xu, J., Liu, T.-Y., Li, H., Huang, Y., & Hon, H.-W. (2006). Adapting ranking SVM to document retrieval. In SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval, ACM, Seattle, Washington, USA.
3. Collins, R. J. (1992). Studies in artificial evolution. PhD thesis, Los Angeles, CA, USA.
4. Dasgupta, D., Ji, Z., & Gonzlez, F. (2003). Artificial immune system (AIS) research in the last five years. In Proceedings of the international conference on evolutionary computation.
5. de Almeida, H. M., Gonçalves, M. A., Cristo, M., & Calado, P. (2007). A combined component approach for finding collection-adapted ranking functions based on genetic programming. In SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval.
6. de Castro L.R., and Timmis J. Artificial immune systems as a novel soft computing paradigm. Soft Computing 7 8 (2003) 526-544
7. de Castro L.N., and Von Zuben F.J. Learning and optimization using the clonal selection principle. IEEE Transactions on Evolutionary Computation 6 3 (2002) 239-251
8. Fan, W., Gordon, M. D., & Pathak, P. (2000). Personalization of search engine services for effective retrieval and knowledge management. In ICIS '00: Proceedings of the 21st international conference on information systems, Atlanta, GA, USA.
9. Fan W., Gordon M.D., and Pathak P. A generic ranking function discovery framework by genetic programming for information retrieval. Information Processing and Management 40 4 (2004) 587-602
10. Fan W., Gordon M.D., and Pathak P. Discovery of context-specific ranking functions for effective information retrieval using genetic programming. IEEE Transactions on Knowledge and Data Engineering 16 4 (2004) 523-527
11. Fan W., Fox E.A., Pathak P., and Wu H. The effects of fitness functions on genetic programming-based ranking discovery for web search. Journal of the American Society for Information Science and Technology 55 7 (2004) 628-636
12. Fan W., Gordon M.D., and Pathak P. Genetic programming-based discovery of ranking functions for effective web search. Journal of Management Information Systems 21 4 (2005) 37-56
13. Freund Y., Iyer R., Schapire R.E., and Singer Y. An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research 4 (2003) 933-969
14. Järvelin K., and Kekäläinen J. Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems (TOIS) 20 4 (2002) 422-446
15. Joachims, T. (2002). Optimizing search engines using clickthrough data. In KDD '02: Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining, ACM.
16. Joachims T., Li H., Liu T.-Y., and Zhai C. Learning to rank for information retrieval (LR4IR 2007). SIGIR Forum 41 2 (2007) 58-62
17. Lacerda, A., Cristo, M., Gonçalves, M. A., Fan, W., Ziviani, N., & Neto, B. R. (2006). Learning to advertise. In SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval, ACM, Seattle, Washington, USA.
18. Liu, T.-Y., Xu, J., Qin, T., Xiong, W., & Li, H. (2007). LETOR: Benchmark dataset for research on learning to rank for information retrieval. In SIGIR workshop on learning to rank for IR (LR4IR).
19. Musilek P., Lau A., Reformat M., and Wyard-Scott L. Immune programming. Information Sciences 176 8 (2006) 972-1002
20. Nossal G. The molecular and cellular basis of affinity maturation in the antibody response. Cell 68 (1993) 1-2
21. Qin, T., Liu, T.-Y., Zhang, X.-D., Chen, Z., & Ma, W.-Y. (2005). A study of relevance propagation for web search. In: SIGIR.
22. Robertson S.E. Overview of the okapi projects. Journal of Documentation 53 1 (1997) 3-7
23. Robertson, S. E., Walker, S., Jones, S., Hancock-Beaulieu, M. M., & Gatford, M. (1995). Okapi at trec-3. In Proceedings of TREC-3, NIST special publication 500-226.
24. Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. In Information processing and management.
25. Shakery, A., & Zhai, C. (2003). Relevance propagation for topic distillation UIUC TREC 2003 web track experiments. In TREC.
26. Singhal, A., Buckley, C., & Mitra, A. (1996). Pivoted document length normalization. In Proceedings of the 19th ACM SIGIR, ACM Press.
27. Trotman A. Learning to rank. Information Retrieval 8 3 (2005) 359-381
28. Tsai, M.-F., Liu, T.-Y., Qin, T., Chen, H.-H., & Ma, W.-Y. (2007). FRank: A ranking method with fidelity loss. In SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval, ACM, Amsterdam, The Netherlands.
29. Xu, J., & Li, H. (2007). AdaRank: A boosting algorithm for information retrieval. In SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval, ACM, Amsterdam, The Netherlands.
30. Zhai, C., & Lafferty, J. (2001). A study of smoothing methods for language models applied to Ad Hoc information retrieval. In SIGIR '01: Proceedings of the 24th annual international ACM SIGIR conference on research and development in information retrieval, ACM, New Orleans, Louisiana, United States.