An Altruistic-Based Utility Function for Group Recommendation

Preference aggregation strategies, that are inspired by economic models of decision makers, typically assume that the individual preferences of the group members depend only on their own individual evaluations of the considered items. In this direction, group recommendation algorithms rely on such standard aggregation techniques that do not consider the possibility of evaluating social interactions and influences among group’s members, as well as their personalities, which are, indeed, crucial factors in the group’s decision-making process, especially regarding small groups. On the contrary, the laboratory data have encouraged the development of models of other-regarding preferences since altruism, fairness, and reciprocity strongly motivate many people. In this paper, starting from a utility function from the literature, which combines the user personal evaluation of an item with the ones of the other group members, we propose a group recommendation method that takes into account altruism. Such function models the level of a user’s altruistic behavior starting from his/her agreeableness personality trait. Once such utility values are evaluated, the goal is to recommend items that maximize the social welfare. Performance is evaluated with a pilot study and compared with respect to Least Misery. Results showed that while for groups of two people Least Misery performs slightly better, in the other cases the two methods are comparable.