AToMRS: A Tool to Monitor Recommender Systems

Abstract

Recommender systems arose in response to the excess of available online information. These systems assign, to a given individual, suggestions of items that may be relevant. These system's monitoring and evaluation are fundamental to the proper functioning of many business related services. It is the goal of this paper to create a tool capable of collecting, aggregating and supervising the results obtained from the recommendation systems' evaluation. To achieve this goal, a multi-granularity approach is developed and implemented in order to organize the different levels of the problem. This tool also aims to tackle the lack of mechanisms to enable visually assessment of the performance of a recommender systems' algorithm. A functional prototype of the application is presented, with the purpose of validating the solution's concept.