6 International Journal of u- and e-Service, Science and Technology 7 Conclusions and Future work In this paper, we proposed real-time genetic recommendation method in order to overcome the existing recommendation techniques are not reflect the current users intend. The proposed method collects the users click stream, and analyzes the users intent. Then the method generates recommendation fit the user's intention by using the genetic algorithm. The fitness function of genetic algorithm adapts users real time intention continually, By comparing with existing method, the experimental results show the proposed method could deliver more diverse and more accurate recommendation than existing methods. In addition, we measured the change of the accuracy as the amount of real-time user behavior increases to evaluate whether the proposed technique can reflect the intent of the user in real-time. As a result, we verified that the accuracy of recommendation is increasing as real-time user behavior information increases Additional future research will be optimizing the elements of genetic algorithm for the recommendation research domain to improve the accuracy of recommendation. In addition, the context information can also be applied on real-time recommendation method to improve the performance of analyzing real-time intent of the user References 1. Mitsuo, G, Runwei, C: Genetic algorithm Engineering Optimization, John Wiley Sons 2. Gediminas, A, Alexander T: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transaction on Knowled nd Data Engineering 17(6), pp. 734--749(2005) 3. Gorgoglione, M, Palmisano, C, Tuzhilin, A: Personalization in Context: Does Context Matter When Building personalized Customer Models? In: 6th IEEE International Conference on Data Mining, pp. 222---231(2006) 4. Cho, J, Kwon, K, Park, Y: Collaborative Filtering Using Dual Information Sources. IEEE Intelligent Systems 22(3), pp 30-38(2007) 5. Zanker M, Jessenitschnig M, Jannach D, Gordea S mparing Recommendation Strategies in a Commercial Context. IEEE Intelligent Systems 22(3), pp 69-73(20 ering 6. Zan, H Recommendation Algorithms for E-commerce. IEEE Intelligent Systems 22(5), pp 68-78 7. Linden, G, Smith, B, York, J. Amazon com Recommendation Item-to-Item Collaborative Filtering. IEEE Internet Computing, pp. 76--80 (2003)7 Conclusions and Future Work In this paper, we proposed real-time genetic recommendation method in order to overcome the existing recommendation techniques are not reflect the current user`s intend. The proposed method collects the user’s click stream, and analyzes the user’s intent. Then the method generates recommendation fit the user's intention by using the genetic algorithm. The fitness function of genetic algorithm adapts user`s real￾time intention continually. By comparing with existing method, the experimental results show the proposed method could deliver more diverse and more accurate recommendation than existing methods. In addition, we measured the change of the accuracy as the amount of real-time user behavior increases to evaluate whether the proposed technique can reflect the intent of the user in real-time. As a result, we verified that the accuracy of recommendation is increasing as real-time user behavior information increases. Additional future research will be optimizing the elements of genetic algorithm for the recommendation research domain to improve the accuracy of recommendation. In addition, the context information can also be applied on real-time recommendation method to improve the performance of analyzing real-time intent of the user. References 1. Mitsuo, G., Runwei, C.: Genetic algorithm & Engineering Optimization, John Wiley & Sons (2000) 2. Gediminas, A., Alexander T.: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transaction on Knowledge and Data Engineering 17(6), pp.734—749 (2005) 3. Gorgoglione, M., Palmisano, C., Tuzhilin, A.: Personalization in Context: Does Context Matter When Building Personalized Customer Models? In: 6th IEEE International Conference on Data Mining, pp.222—231 (2006) 4. Cho, J., Kwon, K., Park, Y.: Collaborative Filtering Using Dual Information Sources. IEEE Intelligent Systems 22(3), pp.30—38 (2007) 5. Zanker M., Jessenitschnig M., Jannach D., Gordea S.: Comparing Recommendation Strategies in a Commercial Context. IEEE Intelligent Systems 22(3), pp.69—73 (2007) 6. Zan, H., Daniel, Z., Hsinchun, C.: A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce. IEEE Intelligent Systems 22(5), pp.68—78 (2007) 7. Linden, G., Smith, B., York, J.: Amazon.com Recommendation Item-to-Item Collaborative Filtering. IEEE Internet Computing, pp.76—80 (2003) 16 International Journal of u- and e- Service, Science and Technology