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.

翻译：用户期望了解推荐的可靠性；若无可靠性证据，他们不接受高预测值。推荐系统应提供与预测相关联的可靠性值。可靠性度量的研究需要存在简单、合理且通用的可靠性质量度量。推荐系统质量度量的研究一直集中于准确性。此外，新颖性、意外发现性和多样性已得到研究；然而，关于可靠性/置信度质量度量的研究存在显著不足。本文提出了一种可靠性质量预测度量（RPI）和一种可靠性质量推荐度量（RRI）。这两种质量度量基于以下假设：可靠性度量越合适，应用时其提供的准确性结果越好。当预测关联适当的可靠性值（即正确预测对应高可靠性值，或错误预测对应低可靠性值）时，这些可靠性质量度量显示了准确性提升。所提出的可靠性质量度量将引导全新推荐系统可靠性度量的设计。这些度量可应用于不同的矩阵分解技术，以及基于内容、情境感知和社交推荐方法。采用所提出的可靠性质量度量，设计的推荐系统可靠性度量可被测试、比较和优化。