International Journal of u-and e- Service, Science and Technology user does not have a purpose (just wandering between items), the user is interested in a particular category, and the user is interested in a particular product. Depending on the user's actions the importance of each state gradually changes. It is reflected in the fitness function of the genetic algorithm to generate the appropriate recommend list for current situation 3 Genetic Recommendation Generating Method 3.1 About the genetic algorithm a genetic algorithm is a search technique used in computing to find exact or approximate solutions to optimization and search problems [1]. It is after the model of the nature ability to adapt environment. The process of genetic algorithm is shown in Figure I enerate initial population 2 Evaluate each individual using fitness function Step. 3 Iterate until stopping criteria Step 3.2 Mutation Step. 3.3 Evaluate each individual using fitness function. Step 3. 4 Make next generation Step. 3. 5 Evaluate stopping criteria Fig. 1. The process of genetic algorithm 3.2 Chromosome encoding 12|5862778310411521255301 Fig. 2. An example of individual In genetic algorithm, the individual in a group is represented by a set of chromosomes 1]. In this paper, we represent each individual as a list of recommendation provided to the user. Each individual is made up of 10 chromosomes and each chromosome is represented a product in the recommendation list. Recommended products are represented in the product ID on the product DB. Figure 2 shows an individual useduser does not have a purpose (just wandering between items), the user is interested in a particular category, and the user is interested in a particular product. Depending on the user's actions the importance of each state gradually changes. It is reflected in the fitness function of the genetic algorithm to generate the appropriate recommend list for current situation. 3 Genetic Recommendation Generating Method 3.1 About the Genetic Algorithm A genetic algorithm is a search technique used in computing to find exact or approximate solutions to optimization and search problems [1]. It is after the model of the nature ability to adapt environment. The process of genetic algorithm is shown in Figure 1. Fig. 1. The process of genetic algorithm 3.2 Chromosome Encoding Step. 1 Generate initial population. Step. 2 Evaluate each individual using fitness function. Step. 3 Iterate until stopping criteria Step. 3.1 Selection and Crossover Step. 3.2 Mutation Step. 3.3 Evaluate each individual using fitness function. Step. 3.4 Make next generation Step. 3.5 Evaluate stopping criteria Fig. 2. An example of individual In genetic algorithm, the individual in a group is represented by a set of chromosomes [1]. In this paper, we represent each individual as a list of recommendation provided to the user. Each individual is made up of 10 chromosomes and each chromosome is represented a product in the recommendation list. Recommended products are represented in the product ID on the product DB. Figure 2 shows an individual used in this paper. International Journal of u- and e- Service, Science and Technology 11