Evaluating the Quality of Recommender Systems

The various recommendation techniques and a certain number of systems have evolved over time, attempting to move ever closer to the expectations and requirements of users. This process requires us to evaluate recommender systems in order to verify whether or not they are relevant and offer the required levels of performance for users in relation to context, objectives, response time, consideration of certain criteria, etc. The use of a single data set to evaluate different recommender systems makes it easier to compare the performance of these different systems directly; however, due consideration must be given to the density of the data set. The quality of recommender systems may be measured in many ways. The most widespread notion is that of accuracy. Three types of accuracy may be considered: accuracy in predicting recommendations, accuracy in classifying recommendations and accuracy in ranking recommendations.