  Interpreting the performance results of models that attempt to realize user
behavior in platforms that employ recommenders is a big challenge that
researchers and practitioners continue to face. Although current evaluation
tools possess the capacity to provide solid general overview of a system's
performance, they still lack consistency and effectiveness in their use as
evident in most recent studies on the topic. Current traditional assessment
techniques tend to fail to detect variations that could occur on smaller
subsets of the data and lack the ability to explain how such variations affect
the overall performance. In this article, we focus on the concept of data
clustering for evaluation in recommenders and apply a neighborhood assessment
method for the datasets of recommender system applications. This new method,
named neighborhood-based evaluation, aids in better understanding critical
performance variations in more compact subsets of the system to help spot
weaknesses where such variations generally go unnoticed with conventional
metrics and are typically averaged out. This new modular evaluation layer
complements the existing assessment mechanisms and provides the possibility of
several applications to the recommender ecosystem such as model evolution
tests, fraud/attack detection and a possibility for hosting a hybrid model
setup.
