International Journal of u- and e-Service, Science and Technology recommender systems sometimes generates inappropriate recommendation because of difference between user's current purpose and information of past time. Indeed, the inappropriate recommendation makes a user angry because the recommendation was generated based on information about the gift to someone else before [3]. Furthermore the existing recommender systems are not considering the intent of the current user So the generated recommendation is not appropriate for the current situation [3] e In this paper, we propose genetic recommendation generating method offers real- he recommend in order overcoming drawbacks of the existing systems. The proposed method generates lists of recommendation using genetic algorithm, and using a fitness function of genetic algorithm that estimate the suitability of recommended lists(that is the best recommendation for the current user ) In addition. our method collects users real-time click-stream to analyze the user's intent continually, and then adapts the fitness function of genetic algorithm to fit the user's current intention In this paper, we compared proposed method with existing recommender systems files from the Internet jewelry store. We used precision and coverage to evaluate a to evaluate the proposal. The experiments have been conducted using the server log proposed method. As a result, we prove that the accuracy of the proposed method is better than existing methods In addition, we measure the change of the accuracy as the amount of real-time user ehavior increasing to evaluate whether the proposed technique can reflect the intent of the user in real-time. As a result, we verified the accuracy of recommendation is increasing as real-time user behavior information is increasing 2 User model In this paper, we use two user models: one is based on the previously collected information as the model of the existing recommender system, and the other one is the eal-time users model. The first user model represents the users preference for a particular product. But it cannot determine the user intention at the time of purchase, nd does not fit with the user's current intent. So, in this paper, the model is only used assistant information for the fitness function of genetic algorithm In this paper, to collect information of the user, we gathered the user click stream implicitly. The benefits of implicit method is to collect much information than the explicit way, and do not bother users The click stream is represented as log format, and the users behavior in a specific product page can be analyzed based on this information. In addition, users item preference can be inferred by using user's behavior score. For example, if the user purchases the products, the preference score is 10 points, 2 points to see the large image of product, and interest in the detailed item information is adding 5 points. This score is stored in the DB and is used for analyzing the users preference in the past The real-time user information is also analyzed based on real-time click stream The visited categories and its frequency, the visited products, search keywords, and search options are included in the information. Based on this information, we can determine what the user wants. Here. we divide the intent of user as three states: therecommender systems sometimes generates inappropriate recommendation because of difference between user`s current purpose and information of past time. Indeed, the inappropriate recommendation makes a user angry because the recommendation was generated based on information about the gift to someone else before [3]. Furthermore, the existing recommender systems are not considering the intent of the current user. So the generated recommendation is not appropriate for the current situation [3]. In this paper, we propose genetic recommendation generating method offers real￾time recommend in order overcoming drawbacks of the existing systems. The proposed method generates lists of recommendation using genetic algorithm, and using a fitness function of genetic algorithm that estimate the suitability of recommended lists (that is the best recommendation for the current user). In addition, our method collects user`s real-time click-stream to analyze the user's intent continually, and then adapts the fitness function of genetic algorithm to fit the user's current intention. In this paper, we compared proposed method with existing recommender systems to evaluate the proposal. The experiments have been conducted using the server log files from the Internet jewelry store. We used precision and coverage to evaluate the proposed method. As a result, we prove that the accuracy of the proposed method is better than existing methods. In addition, we measure the change of the accuracy as the amount of real-time user behavior increasing to evaluate whether the proposed technique can reflect the intent of the user in real-time. As a result, we verified the accuracy of recommendation is increasing as real-time user behavior information is increasing. 2 User Model In this paper, we use two user models: one is based on the previously collected information as the model of the existing recommender system, and the other one is the real-time user's model. The first user model represents the user's preference for a particular product. But it cannot determine the user intention at the time of purchase, and does not fit with the user's current intent. So, in this paper, the model is only used assistant information for the fitness function of genetic algorithm. In this paper, to collect information of the user, we gathered the user click stream implicitly. The benefits of implicit method is to collect much information than the explicit way, and do not bother users. The click stream is represented as log format, and the user's behavior in a specific product page can be analyzed based on this information. In addition, user`s item preference can be inferred by using user`s behavior score. For example, if the user purchases the products, the preference score is 10 points, 2 points to see the large image of product, and interest in the detailed item information is adding 5 points. This score is stored in the DB and is used for analyzing the user's preference in the past [4]. The real-time user information is also analyzed based on real-time click stream. The visited categories and its frequency, the visited products, search keywords, and search options are included in the information. Based on this information, we can determine what the user wants. Here, we divide the intent of user as three states: the 10 International Journal of u- and e- Service, Science and Technology