  In the context of stereotypes creation for recommender systems, we found that
certain types of categorical variables pose particular challenges if simple
clustering procedures were employed with the objective to create stereotypes. A
categorical variable is defined to be complex when it cannot be easily
translated into a numerical variable, when the semantic of the categories
potentially plays an important role in the optimal determination of
stereotypes, and when it is also multi-choice (e.g., each item can be labelled
with one or more categories that may be applicable, in a non pre-defined
number).
  The main objective of this paper is to analyse the possibility of obtaining a
viable recommendation system that operates on stereotypes generated directly
via the feature's metadata similarities, without using ratings information at
the time the generation of the classes. The encouraging results using
integrated MovieLens and Imdb data set show that the proposed algorithm
performs better than other categorical clustering algorithms like k-modes when
clustering complex categorical features. Notably, the representation of complex
categorical features can help to alleviate cold-start issues in recommender
systems.
