Opinion Dynamics-Based Group Recommender Systems

TítuloOpinion Dynamics-Based Group Recommender Systems
Tipo de publicaciónRevista Internacional
Año de publicaciónIn progress
AutoresJ. Castro, J. Lu, G. Zhang, Y. Dong and L. Martínez
RevistaIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volumenin press
Palabras claveopinion dynamics, Recommender systems, social influence, weights matrix

With the accessibility to information, users often face the problem of selecting one item (a product or a service) from a huge search space. This problem is known as information overload. Recommender systems (RSs) personalize content to a user's interests to help them select the right item in information overload scenarios. Group RSs (GRSs) recommend items to a group of users. In GRSs, a recommendation is usually computed by a simple aggregation method for individual information. However, the aggregations are rigid and overlook certain group features, such as the relationships between the group members' preferences. In this paper, it is proposed a GRS based on opinion dynamics that considers these relationships using a smart weights matrix to drive the process. In some groups, opinions do not agree, hence the weights matrix is modified to reach a consensus value. The impact of ensuring agreed recommendations is evaluated through a set of experiments. Additionally, a sensitivity analysis studies its behavior. Compared to existing group recommendation models and frameworks, the proposal based on opinion dynamics would have the following advantages: 1) flexible aggregation method; 2) member relationships; and 3) agreed recommendations.

Índice de impacto 
Hot paper 
Altamente citado