Uncertainty in group recommending lthough 5" might be considered the most probable rating, we will not have the same confidence about every situation. Surprisingly, little attention has been paid in GR literature to the problem of man aging uncertainty although it has been well established in the general group decision framework(see Clemen and winkler 1999: Genest and Zidek 1986 for a review ) In his paper, therefore, we will focus on this particular problem. We maintain that two different sources of uncertainty can be found in group recommending processes: the uncertainty shown when user preferences are set, i.e. the user's personal opinion about an item or feature; and the uncertainty which is inherent to the merging process The purpose of this paper is to investigate the value of using Bayesian networks (BN) to represent how different individuals in a group interact in order to make a final choice or recommendation. In our approach, the BN formalism is used to represent both the interactions between group members and the processes leading to the final choice or recommendation We will show how common decision rules in literature could be managed by adequately designing canonical models with the BN language, thereby shedding new light on the combination processes. Discussion about subjects such as how the groups are formed, how long they have existed, relationships between group members, how the group might interact to reach a consensus, etc are beyond the scope of this paper. We shall assume that all the individuals use the same set of labels to express their preferences for an item, and that these preferences are represented by means of a probability distribution(probably estimated from a data set) We consider BNs appropriate because they combine a qualitative representation of the problem through an explicit representation of the dependence relationships between items, users and groups, with a quantitative representation by means of a set of probability distributions to measure the strength of these relationships. Throughout the process, we must consider the computational aspects of the RS, where the sparse ness of the data and the fact that the ranking should be computed in real time represent two challenges. The second section of this paper briefly examines group recommender systems nd related work. Section 3 presents the proposed BN-based model which enables the interaction between individuals to be represented. Section 4 examines how to represent the strength of the individuals'interactions (i.e. conditional probability distributions) and Sect. 5 discusses how inference is performed in order to make recommendations to the group. Section 6 examines the experimental framework. Section 7 discusses e experimental results obtained when considering uncertainty in individual ratings and in Sect. 8 we study those situations where the process behind the group rating is also uncertain. Finally, our conclusions and comments regarding further research are discussed in Sect. 9 2 Classification of group recommender systems and related work Although GR is quite a new research topic, many papers on this problem have already been published. The specific objectives of recommender systems in the research pub- lished so far are determined by the characteristics of the domain for which the system has been developed. These characteristics significantly affect the choice of design andUncertainty in group recommending 209 although 5∗ might be considered the most probable rating, we will not have the same confidence about every situation. Surprisingly, little attention has been paid in GR literature to the problem of man￾aging uncertainty although it has been well established in the general group decision framework (see Clemen and Winkler 1999; Genest and Zidek 1986 for a review). In this paper, therefore, we will focus on this particular problem. We maintain that two different sources of uncertainty can be found in group recommending processes: the uncertainty shown when user preferences are set, i.e. the user’s personal opinion about an item or feature; and the uncertainty which is inherent to the merging process. The purpose of this paper is to investigate the value of using Bayesian networks (BN) to represent how different individuals in a group interact in order to make a final choice or recommendation. In our approach, the BN formalism is used to represent both the interactions between group members and the processes leading to the final choice or recommendation. We will show how common decision rules in literature could be managed by adequately designing canonical models with the BN language, thereby shedding new light on the combination processes. Discussion about subjects such as how the groups are formed, how long they have existed, relationships between group members, how the group might interact to reach a consensus, etc. are beyond the scope of this paper. We shall assume that all the individuals use the same set of labels to express their preferences for an item, and that these preferences are represented by means of a probability distribution (probably estimated from a data set). We consider BNs appropriate because they combine a qualitative representation of the problem through an explicit representation of the dependence relationships between items, users and groups, with a quantitative representation by means of a set of probability distributions to measure the strength of these relationships. Throughout the process, we must consider the computational aspects of the RS, where the sparse￾ness of the data and the fact that the ranking should be computed in real time represent two challenges. The second section of this paper briefly examines group recommender systems and related work. Section 3 presents the proposed BN-based model which enables the interaction between individuals to be represented. Section 4 examines how to represent the strength of the individuals’ interactions (i.e. conditional probability distributions) and Sect. 5 discusses how inference is performed in order to make recommendations to the group. Section 6 examines the experimental framework. Section 7 discusses the experimental results obtained when considering uncertainty in individual ratings and in Sect. 8 we study those situations where the process behind the group rating is also uncertain. Finally, our conclusions and comments regarding further research are discussed in Sect. 9. 2 Classification of group recommender systems and related work Although GR is quite a new research topic, many papers on this problem have already been published. The specific objectives of recommender systems in the research pub￾lished so far are determined by the characteristics of the domain for which the system has been developed. These characteristics significantly affect the choice of design and 123