Conflict Resolution Profiles and Agent Negotiation for Group Recommendations

The pervasive use of web technologies and online cooperation tools is posing new challenges in the design of recommender systems, requiring now a rapid move from individual to group recommendations. In this paper, a multi-agent system to provide support to small groups of users in their decision-making process is presented. In detail, the addressed problem is to find a common solution for a group, represented by a set of activities in the touristic domain, among a huge set of possible alternatives, that meets the preferences of each member. The proposed system uses an automatic negotiation process that incrementally builds a candidate solution for the whole group according to the individual lists of each group member. Since the negotiation mechanism involves the real users to take part in the decision-making process, the proposed approach tries to limit the agreement search space during the negotiation process in order to minimize the user direct intervention. The proposed solution relies on negotiating agents that simulate the users' behavior while trading by using different conflict resolution styles, obtained by applying the Thomas Kilmann model. The results obtained with both simulated and real users' behavior show that the proposed system achieves a high probability of success, finding a shared solution, in most cases, in a relatively small number of rounds of negotiation. In addition, end users were satisfied with the received recommendations.