Evolutionary learning approach to multi-agent negotiation for group recommender systems

Recommender systems (RSs) have emerged as a solution to the information overload problem by filtering and presenting the users with information, services etc. according to their preferences. RSs research has focused on algorithms for recommending items for individual users. However, in certain domains, it may be desirable to be able to recommend items for a group of persons, e.g., movies, restaurants, etc. for which some remarkable group recommender systems (GRSs) have been developed. GRSs provide recommendations to groups, i.e., they take all individual group members' preferences into account and satisfy them optimally with a sequence of items. Taking into consideration the fact that each group member has different behaviour with respect to other members in the group, we propose a genetic algorithm (GA) based multi-agent negotiation scheme for GRS (GA-MANS-GRS) where each agent acts on behalf of one group member. The GA-MANS-GRS is modelled as many one-to-one bilateral negotiation schemes with two phases. In the negotiation phase, we have applied GA to obtain the maximum utility offer for each user and generated the most appropriate ranking for each individual in the group. For the recommendation generation phase, again GA is employed to produce the list of ratings with that minimizes the sum of distances among the preferences of the group members. Finally, the results of computational experiments are presented that establish the superiority of our proposed model over baseline GRSs techniques.