International Journal of u-and e-Service, Science and Technology 13 MaskL1o⊥o|1|1|0c 0001 Fig. 3. Uniform cross-over 3.6 Stopping Criteria Genetic algorithm has the condition to terminate it. If the condition is reached we believe that the genetic algorithm to achieve the purpose of the process and terminates the process. The terminate condition is usually certain amount of the generation or the fitness score[1]. In this paper, it is hard to set the goal fitness score because the users purpose is changing in real-time. Therefore, after the genetic process is passed 10 times the process is set to shut down conditions 4 Real-time Fitness Function Adaption Genetic algorithm used in this paper is applied to the evaluation criteria divided into 3: the diversity of recommendation, the suitability of recommendation, and the users product preference. The diversity of recommendation means how much the ecommended list includes the product that the user has not seen before. And the suitability of recommendation indicates how much the recommended list is s of the category. Finally, the user preference of product means how simlar g e priate for users real-time intention. It is measured based on the frequency of product in the recommendation list to the product favored previously The formula to calculate the diversity of the recommend is the same as expression (1): N, means the number of new products included in the individual i, and a is a constant value for normalization The formula to calculate the suitability of the recommendation is at expression(2) Cy means the visit frequency of the category j belong to the individual i, where a category of products that recently visited 10 categories, and visited ratio. B is a constant value for normalizationFig. 3. Uniform cross-over 3.6 Stopping Criteria Genetic algorithm has the condition to terminate it. If the condition is reached, we believe that the genetic algorithm to achieve the purpose of the process and terminates the process. The terminate condition is usually certain amount of the generation or the fitness score [1]. In this paper, it is hard to set the goal fitness score because the user`s purpose is changing in real-time. Therefore, after the genetic process is passed 10 times the process is set to shut down conditions. 4 Real-time Fitness Function Adaption Genetic algorithm used in this paper is applied to the evaluation criteria divided into 3: the diversity of recommendation, the suitability of recommendation, and the user`s product preference. The diversity of recommendation means how much the recommended list includes the product that the user has not seen before. And the suitability of recommendation indicates how much the recommended list is appropriate for user`s real-time intention. It is measured based on the frequency of visits of the category. Finally, the user preference of product means how similar the product in the recommendation list to the product favored previously. The formula to calculate the diversity of the recommend is the same as expression (1): Ni means the number of new products included in the individual i, and α is a constant value for normalization. = α × NiV i )( (1) The formula to calculate the suitability of the recommendation is at expression (2): Cij means the visit frequency of the category j belong to the individual i, where a category of products that recently visited 10 categories, and visited ratio. β is a constant value for normalization. ∑ ×= Cij iS )( β (2) International Journal of u- and e- Service, Science and Technology 13