Journal of Convergence Information Technology Volume 5. Number 8 October 2010 [4] M. Pazzani, "A framework for collaborative, content-based and demographic filtering, Artificial Intelligence Review, pp. 393-408, 1999 5]K. Chapphannarungsri and S. Maneeroj, Combining multiple criteria and multidimension for movie recommender system", In Proceeding of the International Multiconference of Engineers and Computer Scientist, (IMECS 2009), pp. 698-703, 2009 [6]F. L. Roux, E. Ranjeet, V. Ghai, Y. Gao and J. lu, A Course Recommender System Using Multiple Criteria Decision Making Method, In ISKE-2007 Proceedings, Series 'Advances in Intelligent Systems Research, 2007 [7 W.-G. Teng and H -H Lee, Collaborative Recommendation with Multi-Criteria Ratings", Journal of Computers(Special Issue on Data Mining), vol. 17, no. 4, pp. 69-78, January 2007 [8]K. Lakiotaki, S. Tsafarakis and N. Matsatsinis, "UTA-Rec: A recommender system based on ultiple criteria analysis", In Proceedings of the ACM conference on Recommender sys 219-226.2008 [9]Y. Siskos and D. Yanacopoulos, "UTA STAR-an ordinal regression method for building additive value functions", Investigacao Operational, vol 5, pp. 539-53, 1985 [10]M. Srinivas and L. M. Patnaik,"Genetic algorithms: A survey", IEEE Computer, vol 27, no. 6, pp.17-26,June1994 [11L. Davis, editor. Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York, 1991 [12]P. Resnick, N. lacovou, M. Suchak, P. Bergstrom, and J. Riedl,"GroupLens: An Open Architecture for Collaborative Filtering of Netnews", In Proceedings of ACM Conference on Computer Supported Cooperative World, pp 175-186, 1994 [13]. B. Statnikov and J. B Matusov, Multicrieria Optimization and Engineering, Springer Verlag, March 1995 [14]A Czarn, C MacNish, K Vijayan, B Turlach, and R Gupta, "Statistical exploratory analysis of genetic algorithms", IEEE Transactions on Evolutionary Computation, vol. 8, no. 4, pp. 405-421 August 2004 [15]D E. Goldberg, ""Sizing Populations for Serial and Parallel Genetic Algorithms", In Proceedings of the Third International Conference on Genetic Algorithms. San Mateo, CA: Morgan Kaufman, pp 70-79,1989 Appendix I Table 1. Precision-metric for different values of P. P and GN Precision 102030405060 100 0.5046810.4730481049001005220.5280.535.05380.540 06|046404740483049305081053260534054505530563 0.01070489003051005190330585057505205890591 080.4920.50405130.52805430:5740.5850.5930.5950.596 09049705030.5130523055305620582059006040609 1.0047804870.50305100.512053205530.5630.5640.567 0504700.4720.4780489049705140.5270.53705430.545 0604690473048304920.50605250533055705630.564 0.030704910504051305220538051057705850590593 0.04930506051605056505750584060506080601 1.00480048305050512052505440565057005720573 0.504730475047904910497051905320.5500.590.562 0.604700474048404870.5060.5180.5320.5610.56810.571 0.05 0,704950506051305380.549055110580059105980.59 0.81049640.5080519054205680.5750581060006030604 0104990502051405671057205940601060906100613 1.004830485049705060532056105750.58005840588Journal of Convergence Information Technology Volume 5, Number 8, October 2010 [4] M. Pazzani, “A framework for collaborative, content-based and demographic filtering”, Artificial Intelligence Review, pp. 393-408, 1999. [5] K. Chapphannarungsri and S. Maneeroj, ”Combining multiple criteria and multidimension for movie recommender system”, In Proceeding of the International Multiconference of Engineers and Computer Scientist, (IMECS 2009), pp. 698-703, 2009. [6] F. L. Roux, E. Ranjeet, V. Ghai, Y. Gao and J. Lu, “A Course Recommender System Using Multiple Criteria Decision Making Method”, In ISKE-2007 Proceedings, Series ‘Advances in Intelligent Systems Research’, 2007. [7] W.-G. Teng and H.-H. Lee, “Collaborative Recommendation with Multi-Criteria Ratings”, Journal of Computers (Special Issue on Data Mining), vol. 17, no. 4, pp. 69-78, January 2007. [8] K. Lakiotaki, S. Tsafarakis and N. Matsatsinis, “UTA-Rec: A recommender system based on multiple criteria analysis”, In Proceedings of the ACM conference on Recommender systems, pp. 219-226, 2008. [9] Y. Siskos and D. Yanacopoulos, “UTA STAR - an ordinal regression method for building additive value functions”, Investigacao Operational, vol. 5, pp. 539-53, 1985. [10]M. Srinivas and L. M. Patnaik, “Genetic algorithms: A survey”, IEEE Computer, vol. 27, no. 6, pp. 17–26, June 1994. [11]L. Davis, editor. Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York, 1991. [12]P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, “GroupLens: An Open Architecture for Collaborative Filtering of Netnews”, In Proceedings of ACM Conference on Computer Supported Cooperative World, pp. 175-186, 1994. [13]R.B. Statnikov and J. B. Matusov, Multicrieria Optimization and Engineering, Springer Verlag, March 1995. [14]A Czarn, C MacNish, K Vijayan, B Turlach, and R Gupta, “Statistical exploratory analysis of genetic algorithms”, IEEE Transactions on Evolutionary Computation, vol. 8, no. 4, pp. 405-421, August 2004. [15]D.E. Goldberg, “Sizing Populations for Serial and Parallel Genetic Algorithms”, In Proceedings of the Third International Conference on Genetic Algorithms. San Mateo, CA: Morgan Kaufman. pp. 70-79, 1989. Appendix I Table 1. Precision-metric for different values of Pm, Pc and GN Precision GN pm pc 10 20 30 40 50 60 70 80 90 100 0.01 0.5 0.468 0.473 0.481 0.490 0.510 0.522 0.528 0.535. 0.538 0.540 0.6 0.464 0.474 0.483 0.493 0.508 0.526 0.534 0.545 0.553 0.563 0.7 0.489 0.503 0.510 0.519 0.533 0.558 0.575 0.582 0.589 0.591 0.8 0.492 0.504 0.513 0.528 0.543 0.574 0.585 0.593 0.595 0.596 0.9 0.497 0.503 0.513 0.523 0.553 0.562 0.582 0.590 0.604 0.609 1.0 0.478 0.487 0.503 0.510 0.512 0.532 0.552 0.563 0.564 0.567 0.03 0.5 0.470 0.472 0.478 0.489 0.497 0.514 0.527 0.537 0.543 0.545 0.6 0.469 0.473 0.483 0.492 0.506 0.525 0.533 0.557 0.563 0.564 0.7 0.491 0.504 0.513 0.522 0.538 0.553 0.577 0.585 0.591 0.593 0.8 0.493 0.506 0.516 0.533 0.565 0.575 0.584 0.605 0.608 0.601 0.9 0.497 0.501 0.514 0.548 0.566 0.587 0.597 0.607 0.609 0.610 1.0 0.480 0.483 0.505 0.512 0.525 0.544 0.565 0.570 0.572 0.573 0.05 0.5 0.473 0.475 0.479 0.491 0.497 0.519 0.532 0.550 0.559 0.562 0.6 0.470 0.474 0.484 0.487 0.506 0.518 0.532 0.561 0.568 0.571 0.7 0.495 0.506 0.513 0.538 0.549 0.551 0.580 0.591 0.598 0.599 0.8 0.496 0.508 0.519 0.542 0.568 0.575 0.581 0.600 0.603 0.604 0.9 0.499 0.502 0.514 0.567 0.572 0.594 0.601 0.609 0.610 0.613 1.0 0.483 0.485 0.497 0.506 0.532 0.561 0.575 0.580 0.584 0.588 135