A Consensus-Driven Group Recommender System

Jorge Castro, Francisco Jose Quesada Real, Iván Palomares, Luis Martínez

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Recommender systems aim at filtering large amounts of information for users, providing them with those pieces of information which better meet their preferences or needs. Such systems have been traditionally used in diverse areas, such as e-commerce or tourism. Within this context, group recommender systems address the problem of generating recommendations for groups of users who might have different interests. Although different aggregation processes have been extensively utilized in real-life applications to generate group recommendations, such processes do not guarantee that the list of products recommended to the group reflect a high agreement level among its members' individual preferences. Given the need for considering the added value of obtaining group recommendations under a high agreement level, this paper presents a novel group recommender system methodology that attempts to reach a high level of consensus among individual recommendations of group members. To do this, and inspired by existing group decision-making approaches in the literature, a consensus reaching process is carried out to bring such individual recommendations closer to each other before delivering the group recommendations.
Original languageEnglish
Pages (from-to)887-906
Number of pages20
JournalInternational Journal of Intelligent Systems
Volume30
Issue number8
DOIs
Publication statusPublished - Aug 2015

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