approach, aggregating recommendations(Ar), is a two-step strategy, where an indi- vidual recommendation is first obtained for each group member, and then a common recommendation is obtained by merging these individual recommendations. In the second approach, aggregating profiles(AP), the objective is to obtain a common profile by representing group preferences. This can be done explicitly, where the ndividuals use a common group account to give their preferences, or implicitly by means of an aggregation mechanism for the different individuals' profiles or Individual interactions Finally, a group recommending system can also be categorized by considering the way in which the users interact with the system. The individuals can be dichoto- mized into passive members(PM)and active members(AM). Focusing on the active embers, the final purpose is to reach a consensus between the group members and like many decision support system approaches, it is necessary for the users to eval- uate the system recommendations. In contrast, when the members are passive, the final purpose is only to provide a recommendation to the group, as might be the case when using an RS in a marketing campaign. In this situation, the individuals do not interact with the system in order to evaluate the proposed recommendations Since we use three non-overlapping criteria for classification purposes, a given GrS can be classified using three labels, one for each category. For instance, a GRS can be classified as CB-+AP+PM if the group profile is obtained by combining the infor mation about the content of the items which have been previously evaluated by each user. This profile will be used to send the final recommendations to the group 2.1 Related work Once the taxonomy has been presented, we will then go on to classify previously published GR systems CB+AP+PM: most published GRSs might be included in this category. For exam ple, let us consider MusicFX (McCarthy and Anagnost 2000). Given a database of member preferences for musical genres(each user rates each of the 91 genres on a five-point scale), the group profile is computed by summing the squared individual preferences. Using a weighted random selection operator, the next music station to be played is then selected. No interaction with the system is possible except by changing user preferences The inputs in the case of group modeling(Masthoff 2004)are user preferences(rat- ings)for a series of programs, and in this paper we study the performance of several aggregation strategies. The article(Yu et al. 2006) presents various TV program recommendations for multiple viewers by merging individual user preferences on eatures(e.g. genre, actor, etc. )to construct a group profile. The aim of the aggrega tion strategy is to minimize the total distance in such a way that the merged profile s close to most user preferences, thereby satisfying most of the group. CB+AP+AM: The Travel Decision Forum (Jameson 2004)was developed to help a group of users agree on the desired attributes of a vacation. This system allowsUncertainty in group recommending 211 approach, aggregating recommendations(AR), is a two-step strategy, where an indi￾vidual recommendation is first obtained for each group member, and then a common recommendation is obtained by merging these individual recommendations. In the second approach, aggregating profiles (AP), the objective is to obtain a common profile by representing group preferences. This can be done explicitly, where the individuals use a common group account to give their preferences, or implicitly, by means of an aggregation mechanism for the different individuals’ profiles or preferences. – Individual interactions Finally, a group recommending system can also be categorized by considering the way in which the users interact with the system. The individuals can be dichoto￾mized into passive members(PM) and active members(AM). Focusing on the active members, the final purpose is to reach a consensus between the group members and, like many decision support system approaches, it is necessary for the users to eval￾uate the system recommendations. In contrast, when the members are passive, the final purpose is only to provide a recommendation to the group, as might be the case when using an RS in a marketing campaign. In this situation, the individuals do not interact with the system in order to evaluate the proposed recommendations. Since we use three non-overlapping criteria for classification purposes, a given GRS can be classified using three labels, one for each category. For instance, a GRS can be classified as CB+AP+PM if the group profile is obtained by combining the infor￾mation about the content of the items which have been previously evaluated by each user. This profile will be used to send the final recommendations to the group. 2.1 Related work Once the taxonomy has been presented, we will then go on to classify previously published GR systems. – CB+AP+PM: most published GRSs might be included in this category. For exam￾ple, let us consider MusicFX (McCarthy and Anagnost 2000). Given a database of member preferences for musical genres (each user rates each of the 91 genres on a five-point scale), the group profile is computed by summing the squared individual preferences. Using a weighted random selection operator, the next music station to be played is then selected. No interaction with the system is possible except by changing user preferences. The inputs in the case of group modeling (Masthoff 2004) are user preferences (rat￾ings) for a series of programs, and in this paper we study the performance of several aggregation strategies. The article (Yu et al. 2006) presents various TV program recommendations for multiple viewers by merging individual user preferences on features (e.g. genre, actor, etc.) to construct a group profile. The aim of the aggrega￾tion strategy is to minimize the total distance in such a way that the merged profile is close to most user preferences, thereby satisfying most of the group. – CB+AP+AM: The Travel Decision Forum (Jameson 2004) was developed to help a group of users agree on the desired attributes of a vacation. This system allows 123