4 research outputs found
A personality-aware group recommendation system based on pairwise preferences
Human personality plays a crucial role in decision-making and it has paramount importance
when individuals negotiate with each other to reach a common group decision.
Such situations are conceivable, for instance, when a group of individuals want to watch
a movie together. It is well known that people influence each other’s decisions, the more
assertive a person is, the more influence they will have on the final decision. In order to
obtain a more realistic group recommendation system (GRS), we need to accommodate
the assertiveness of the different group members’ personalities. Although pairwise preferences
are long-established in group decision-making (GDM), they have received very little
attention in the recommendation systems community. Driven by the advantages of pairwise
preferences on ratings in the recommendation systems domain, we have further pursued
this approach in this paper, however we have done so for GRS. We have devised a
three-stage approach to GRS in which we 1) resort to three binary matrix factorization
methods, 2) develop an influence graph that includes assertiveness and cooperativeness
as personality traits, and 3) apply an opinion dynamics model in order to reach consensus.
We have shown that the final opinion is related to the stationary distribution of a Markov
chain associated with the influence graph. Our experimental results demonstrate that our
approach results in high precision and fairness.Spanish Government PID2019-10380RBI00/AEI/10. 13039/501100011033Andalusian Government P20_0067