  Users want to know the reliability of the recommendations; they do not accept
high predictions if there is no reliability evidence. Recommender systems
should provide reliability values associated with the predictions. Research
into reliability measures requires the existence of simple, plausible and
universal reliability quality measures. Research into recommender system
quality measures has focused on accuracy. Moreover, novelty, serendipity and
diversity have been studied; nevertheless there is an important lack of
research into reliability/confidence quality measures.
  This paper proposes a reliability quality prediction measure (RPI) and a
reliability quality recommendation measure (RRI). Both quality measures are
based on the hypothesis that the more suitable a reliability measure is, the
better accuracy results it will provide when applied. These reliability quality
measures show accuracy improvements when appropriated reliability values are
associated with their predictions (i.e. high reliability values associated with
correct predictions or low reliability values associated with incorrect
predictions).
  The proposed reliability quality metrics will lead to the design of brand new
recommender system reliability measures. These measures could be applied to
different matrix factorization techniques and to content-based, context-aware
and social recommendation approaches. The recommender system reliability
measures designed could be tested, compared and improved using the proposed
reliability quality metrics.
