  In order to improve the accuracy of recommendations, many recommender systems
nowadays use side information beyond the user rating matrix, such as item
content. These systems build user profiles as estimates of users' interest on
content (e.g., movie genre, director or cast) and then evaluate the performance
of the recommender system as a whole e.g., by their ability to recommend
relevant and novel items to the target user. The user profile modelling stage,
which is a key stage in content-driven RS is barely properly evaluated due to
the lack of publicly available datasets that contain user preferences on
content features of items.
  To raise awareness of this fact, we investigate differences between explicit
user preferences and implicit user profiles. We create a dataset of explicit
preferences towards content features of movies, which we release publicly. We
then compare the collected explicit user feature preferences and implicit user
profiles built via state-of-the-art user profiling models. Our results show a
maximum average pairwise cosine similarity of 58.07\% between the explicit
feature preferences and the implicit user profiles modelled by the best
investigated profiling method and considering movies' genres only. For actors
and directors, this maximum similarity is only 9.13\% and 17.24\%,
respectively. This low similarity between explicit and implicit preference
models encourages a more in-depth study to investigate and improve this
important user profile modelling step, which will eventually translate into
better recommendations.
