L. M. de Campos et al. 1 Introduction Recommender systems (RS) provide specific suggestions about items(or actions) within a given domain which may be considered of interest to the user(Resnick and Varian 1997). Depending on the information used when recommending, traditional RS are mainly classified into content and collaborative-based RS, although hybrid approaches do exist. The first type recommends a product by considering its content similarity with those products in which the user has previously expressed an interest. The second alternative attempts to identify groups of people with similar tastes to the user and to recommend items that they have liked Most RS are designed for individual use, i.e. there is an active user who receives recommendations about certain products once they have logged on to the system In this paper, we will focus on the related problem of group recommending(Gr) here the objective is to obtain recommendations for groups of people(Jameson and Smyth 2007). This kind of Rs is appropriate for domains where a group of people participate in a single activity such as watching a movie or going on vacation and also in situations where a single person must make a decision on behalf of the group. In one way or another, GR involves merging different individual preferences. In these situations, it is natural that one of the most important issues is the search for an aggregation mechanism to obtain recommendations for the group. According to Pennock and Wellman(2005)". there is nothing close to a single well-accepted normative basis for group beliefs, group preferences or group decision making. and many aggregation strategies can therefore be found in literature for group decisions (Masthoff 2004; Masthoff and Gatt 2006; Yu et al. 2006; Jameson and Smyth 2007) It is typically assumed that member preferences are given using a rating domain (let us say from 5*, really like, to 1*, really hate). An aggregation strategy is then used to determine the group rating. For example, let us consider a group with three individuals, John, Ann and Mary, where John rates a product 5, Ann rates it 2, and Mary rates it 5". Following an average aggregation criterion, we could then say that the group duct is 4 As in the previous example, the methods proposed in GR literature(see Jameson and Smyth 2007 for a review) do not deal with uncertainty. They assume that the inputs of the aggregation functions (i.e. user preferences)are precise and use a merging strat egy to compute precise outputs. This assumption is not necessarily true, especially if we consider that the user's preferences are normally determined by means of auto matic mechanisms In these cases, a probability distribution over the candidate ratings might be used to express user likelihoods. For example, Table I shows the probability distributions representing the preferences of three users(A, B, and C). In this case Table 1 User ratings for a given User208 L. M. de Campos et al. 1 Introduction Recommender systems (RS) provide specific suggestions about items (or actions) within a given domain which may be considered of interest to the user (Resnick and Varian 1997). Depending on the information used when recommending, traditional RS are mainly classified into content and collaborative-based RS, although hybrid approaches do exist. The first type recommends a product by considering its content similarity with those products in which the user has previously expressed an interest. The second alternative attempts to identify groups of people with similar tastes to the user and to recommend items that they have liked. Most RS are designed for individual use, i.e. there is an active user who receives recommendations about certain products once they have logged on to the system. In this paper, we will focus on the related problem of group recommending (GR), where the objective is to obtain recommendations for groups of people (Jameson and Smyth 2007). This kind of RS is appropriate for domains where a group of people participate in a single activity such as watching a movie or going on vacation and also in situations where a single person must make a decision on behalf of the group. In one way or another, GR involves merging different individual preferences. In these situations, it is natural that one of the most important issues is the search for an aggregation mechanism to obtain recommendations for the group. According to Pennock and Wellman (2005) “... there is nothing close to a single well-accepted normative basis for group beliefs, group preferences or group decision making.”, and many aggregation strategies can therefore be found in literature for group decisions (Masthoff 2004; Masthoff and Gatt 2006; Yu et al. 2006; Jameson and Smyth 2007). It is typically assumed that member preferences are given using a rating domain (let us say from 5∗, really like, to 1∗, really hate). An aggregation strategy is then used to determine the group rating. For example, let us consider a group with three individuals, John, Ann and Mary, where John rates a product 5∗, Ann rates it 2∗, and Mary rates it 5∗. Following an average aggregation criterion, we could then say that the group rating for this product is 4∗. As in the previous example, the methods proposed in GR literature (see Jameson and Smyth 2007 for a review) do not deal with uncertainty. They assume that the inputs of the aggregation functions (i.e. user preferences) are precise and use a merging strat￾egy to compute precise outputs. This assumption is not necessarily true, especially if we consider that the user’s preferences are normally determined by means of auto￾matic mechanisms. In these cases, a probability distribution over the candidate ratings might be used to express user likelihoods. For example, Table 1 shows the probability distributions representing the preferences of three users (A, B, and C). In this case, Table 1 User ratings for a given item User 1* 2* 3* 4* 5* A 0.2 0.2 0.2 0.19 0.21 B 0 000.1 0.9 C 0.49 0 0 0 0.51 123