In requirements engineering for recommender systems, software engineers must identify
the data that drives the recommendations. This is a labor-intensive task, which is
error-prone and expensive. One possible solution to this problem is the adoption of
automatic recommender system development approach based on a general recommender framework.
One step towards the creation of such a framework is to determine the type of data
used in recommender systems. In this paper, a systematic review has been conducted
to identify the type of user and recommendation data items needed by a general recommender
system. A user and item model is proposed, and some considerations about algorithm
specific parameters are explained. A further goal is to study the impact of the fields
of big data and Internet of things on the development of recommender systems.