L. M. de Campos et al. RECOMM STRATEGIES A. Prof INFORMATION SOURCE Fig. 1 Classification of Group Recommending Systems each publication therefore focuses on a specific issue( from how to acquire information about group preferences or how the system generates and explains the recommenda- tions to studying the mechanism used to reach a consensus (Jameson and Smyth 2007)). As a result, relating the different approaches is a difficult task. In this section, we will present a new classification taxonomy for group recom ending systems. This classification is based on three independent components of primary importance in the design of a group recommending system and not on the particular techniques used to solve each problem: the information source, the aggre gation criterion used to make the recommendations, and the user's interaction with the system Figure I shows a graphical representation of the proposed classification Source of information: This classification criterion, which has been borrowed from classical RS literature(Adomavicius and Tuzhilin 2005), distinguishes betweer content-based(CB)and collaborative filtering(CF). In the first case, the recom- mended items are those which are similar to the ones that individuals have found interesting in the past. As a result, it is necessary to analyze the content's features recommending. The second alternative considers that the recommendations for a target product have been obtained by considering how people with similar tastes rated a product in the past. These systems are based on the idea that people will agree in future evalua- tions if they have also agreed in their past evaluations. The information sources are therefore the preference ratings given by similar users A new category can obviously be obtained if we consider hybrid approaches that combine both(collaborative and content-based) methods Recommendation strategies: Once we have the information to hand, the strategy used for aggregating this infor mation is a central point in group recommending, and generally in any group deci sion process. In this case, two different approaches can be distinguished. The first I Without loss of generality, we have decided not toinclude thi ory in our taxonomy since, to the best of our knowledge, no study has tried to combine both techniques210 L. M. de Campos et al. USER active passive INFORMATION SOURCE content collaborative A. Rec. A. Prof. RECOMM. STRATEGIES Fig. 1 Classification of Group Recommending Systems each publication therefore focuses on a specific issue (from how to acquire information about group preferences or how the system generates and explains the recommenda￾tions to studying the mechanism used to reach a consensus (Jameson and Smyth 2007)). As a result, relating the different approaches is a difficult task. In this section, we will present a new classification taxonomy for group recom￾mending systems. This classification is based on three independent components of primary importance in the design of a group recommending system and not on the particular techniques used to solve each problem: the information source, the aggre￾gation criterion used to make the recommendations, and the user’s interaction with the system. Figure 1 shows a graphical representation of the proposed classification. – Source of information: This classification criterion, which has been borrowed from classical RS literature (Adomavicius and Tuzhilin 2005), distinguishes between content-based (CB) and collaborative filtering (CF). In the first case, the recom￾mended items are those which are similar to the ones that individuals have found interesting in the past. As a result, it is necessary to analyze the content’s features for recommending. The second alternative considers that the recommendations for a target product have been obtained by considering how people with similar tastes rated a product in the past. These systems are based on the idea that people will agree in future evalua￾tions if they have also agreed in their past evaluations. The information sources are therefore the preference ratings given by similar users. A new category can obviously be obtained if we consider hybrid approaches that combine both (collaborative and content-based) methods.1 – Recommendation strategies: Once we have the information to hand, the strategy used for aggregating this infor￾mation is a central point in group recommending, and generally in any group deci￾sion process. In this case, two different approaches can be distinguished. The first 1 Without loss of generality, we have decided not to include this category in our taxonomy since, to the best of our knowledge, no study has tried to combine both techniques in the group recommending framework. 123