Influence of Rating Prediction on the Accuracy of a Group Recommender System that Detects Groups

Recommender systems suggest items that might be interesting for a user. In order to do so, rating prediction is the main form of information processing performed by them. In this paper, we tackle the problem of predicting ratings in a group recommender system, by analyzing how the accuracy of a system is influenced by the choice of a different prediction approach and by a solution that employs the predicted values to avoid data sparsity. The results of more than one hundred experiments show that, by predicting the ratings for the individual users instead of predicting them for the groups, and by using these predictions in the group detection task of a system, the accuracy increases and the problems caused by data sparsity are reduced.