Recommender systems are among the most popular tools used by online community these days. Traditionally, recommender techniques were evaluated using accuracy-based metrics such as precision; however, gradually the need for other qualities including more novel and diverse items emerged. Consequently, researchers started to evaluate their finding with different and often inconsistent metrics and made it nearly impossible to compare the existing approaches properly. It is clear that we need a more unified approach to assess the results of new techniques, and to the best of our knowledge, this problem has not been answered yet in previous studies. In this paper, we proposed a novel and extensible framework for evaluation of recommender systems using maximum bounds of possible measures in different datasets. Finally we provided the results of applying this framework on a set of different recommender algorithms.
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