  In the context of content-based recommender systems, the aim of this paper is
to determine how better profiles can be built and how these affect the
recommendation process based on the incorporation of temporality, i.e. the
inclusion of time in the recommendation process, and topicality, i.e. the
representation of texts associated with users and items using topics and their
combination. The main contribution of the paper is to present two different
ways of hybridising these two dimensions and to evaluate and compare them with
other alternatives.
