A comprehensive reference model for personalized recommender systems

Existing reference models for recommender systems are on an abstract level of detail or do not point out the processes and transitions of recommendation systems. However, this information is relevant for developers to design or improve recommendation systems. Even so, users need some background information of the calculation process to understand the process and accept or configure these systems proper. In this paper we present a comprehensive reference model for recommender systems which conjuncts the recommendation processes on an adequate level of detail. To achieve this, the processes of content-based and collaboration-based systems are merged and extended by the transitions and phases of hybrid systems. Furthermore, the algorithms which can be applied in the phases of the model are examined to identify the data flow between these phases. With our model those information of the recommendation calculation process can be identified, which encourages the traceability and thus the acceptance of recommendations.