Exploring the Automatic Selection of Aggregation Methods in Group Recommendation.

A recommender system is a software tool designed to support users to filter out useless options within a multitude of choices and provide them with the best possible ones. Group recommender systems have emerged as an important trend in recommendation since they recommend social items that are enjoyed by more than one individual, such as TV programs and travel packages, that are typically consumed in groups. Although algorithm selection in recommender systems is a research problem covered to some extent by the research community in which individuals’ information is aggregated, this contribution is focused on the automatic selection of the most appropriate aggregation function in group recommendation. Specifically, a general framework that identifies group characteristics to be matched with the most appropriate aggregation function is presented. This approach is implemented by using a fuzzy decision tree classifier, in a content-based group recommendation approach. The development of an experimental protocol illustrates the advantage of the new proposal in relation to its corresponding baselines.