Opinion Dynamics-Based Group Recommender Systems
Tipo de publicación: International Journal
Año de publicación: 2018
Autores: Jorge Castro
Director: Jie Lu, Guangquan Zhang, Yucheng Dong, Luis Martínez
Tipo: IEEE Transactions on Systems, Man, and Cybernetics: Systems
Editorial: IEEE
Incidencia: 12
Volumen: 48
Paginación: 2394-2406
Numero ISSN: 2168-2232
Resumen: 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 users 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.
URL: http://ieeexplore.ieee.org/document/7919222/
DOI: 10.1109/TSMC.2017.2695158
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