International Journal of u-and e- Service. Science and Technolo Genetic Recommend Generating Method with Real-time Fitness Function Adaption Minchul Jung, Jehwan Oh, and Eunseok Lee School of Electrical and Computer Engineering, Sunkyunkwan University 300 Cheoncheon-dong, Jangan-gu, Suwon, Gyeonggi-do 440-746, South Korea fmcjung, hide 7674, esle@ece skku ac kr Abstract. Existing recommender systems ecommendation using users information previously collect e information refle user's tastes. but it doesnt include users recommender systems sometimes generate not suitable recommendation because of difference between user's current purpose and the information of past time. In this paper, we propose genetic recommend generating method for overcome this problem. Our method analyzes user's real-time click-stream for grabbing s current intention, then uses genetic algorithm for generating appropriate recommendation. To reflect user's real-time intention, the proposed the proposed approach, we compare the proposed method with existing CF methods using the web-server log data collected from Internet jewelry shop And we confirm that the proposed approach can generate more accurate recommendation then compared methods Keywords: Genetic algorithm, Recommender system, User preference, User profile, Real-time recommendation 1 Introduction Due to recent advances in information and communication technology and growth of The Internet users, the amount of information on the Intemet has growing exponentially. It makes difficult to find information on the Internet for user. To resolve this problem, the recommender system that offers the appropriate information for the user has been actively studying General recommender systems create a recommendation based on information about the user. Information is collected using explicit and implicit method [2]. The existing recommender systems generate recommendation based on the above- mentioned information-gathering techniques to determine the user's preference However this recommendation does not consider the intent of the user. So existing This work was supported by the MKe 2lst Century Frontier R&D Program in Korea and a result of subproject UCN 08B3-B1-10M, ITRC IITA-2008-(C1090-080-0046), Grant No Ro1-2006-000-10954-0, Basic Research Program of the Korea Science engineeringGenetic Recommend Generating Method with Real-time Fitness Function Adaption* Minchul Jung, Jehwan Oh, and Eunseok Lee School of Electrical and Computer Engineering, Sunkyunkwan University, 300 Cheoncheon-dong, Jangan-gu, Suwon, Gyeonggi-do 440-746, South Korea {mcjung, hide7674, eslee}@ece.skku.ac.kr Abstract. Existing recommender systems generate recommendation usually using user's information previously collected. The information reflects the user`s tastes, but it doesn`t include user`s intend at that time. So existing recommender systems sometimes generate not suitable recommendation because of difference between user`s current purpose and the information of past time. In this paper, we propose genetic recommend generating method for overcome this problem. Our method analyzes user`s real-time click-stream for grabbing user`s current intention, then uses genetic algorithm for generating appropriate recommendation. To reflect user`s real-time intention, the proposed method adapts fitness function of genetic algorithm continuously. To evaluate the proposed approach, we compare the proposed method with existing CF methods using the web-server log data collected from Internet jewelry shop. And we confirm that the proposed approach can generate more accurate recommendation then compared methods. Keywords: Genetic algorithm, Recommender system, User preference, User profile, Real-time recommendation 1 Introduction Due to recent advances in information and communication technology and growth of The Internet users, the amount of information on the Internet has growing exponentially. It makes difficult to find information on the Internet for user. To resolve this problem, the recommender system that offers the appropriate information for the user has been actively studying. General recommender systems create a recommendation based on information about the user. Information is collected using explicit and implicit method [2]. The existing recommender systems generate recommendation based on the above￾mentioned information-gathering techniques to determine the user's preference. However, this recommendation does not consider the intent of the user. So existing * This work was supported by the MKE 21st Century Frontier R&D Program in Korea and a result of subproject UCN 08B3-B1-10M, ITRC IITA-2008-(C1090-080-0046), Grant No. R01-2006-000-10954-0, Basic Research Program of the Korea Science & Engineering Foundation. International Journal of u- and e- Service, Science and Technology 9