Agent-based simulation model of customer behavior and personalized recommendation

This paper proposes an agent-based modeling and simulation method to overcome the limitations of traditional personalized recommendation method. Customer behavior and the effectiveness of personalized recommendation strategy under mobile electronic commerce were analyzed according to the emergence generated by the interactions of each agent entity. Taking catering recommendation system under mobile e-commerce as an example, this paper analyzed the interactions of customer and server in consuming process and the impact of context to customer consuming, and the recommendation and customer behavior rules were built, then on this basis the agent simulation model was realized under REPAST simulation environment. The simulation results show that this model can analyze and forecast the emergence of server recommendation and customer decision, and accordingly deduce the general consumer trends. Moreover, the effectiveness of recommendation model considering context is enhanced obviously compared with that of model based on only customer personalization information.