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Recommender systems help to show possible items of interest, but these recommendations are closely linked to the amount of available user information. This information is generated by user interaction with the system, which is generally absent, especially in the case of a new user. In this contribution, we intend to check the methods of filtering applied in the context of traditional recommendation systems by adapting them to an inter-application context by using collaborative filtering techniques. To this end, we propose the architecture and rules for building an environment of a recommendation system for inter-application. The resulting database was developed for evaluations of inter-analysis applications and it will be made available to the scientific community for future research.