8TH International Conference on Advanced Technologies , Sarajevo, Bosna-Hersek, 26 - 30 Ağustos 2019
The main goal of group recommender systems is to provide appropriate products/services for members of a group. Various aggregation techniques have been proposed for combining individual preferences and estimating recommendations based on counts of ratings, rankings, deviation, and rating averages. Also, the size of the groups and the recommendation list are essential elements that directly influence the performance of the aggregation techniques; hence they should be considered when selecting the aggregation technique to be used in a group recommender system. In this study, the effectiveness of 11 baseline aggregation techniques regarding both different group sizes and recommendation lists is deeply analyzed. During the experimental processes, the groups are constructed using the k-means clustering algorithm, which helps clustering people with similar interests together. Experimental studies demonstrate that three aggregation techniques are successful, mostly when recommending a small number of items. However, they get reasonably hurt with a large number of recommendations. Also, the specificity of the groups is a crucial factor for aggregation techniques to better model group profiles, and they are more successful in small groups of people.