Group Recommender System Based on Rank Aggregation – An Evolutionary Approach

Meena, Ritu; Bharadwaj, Kamal Kant

doi:10.1007/978-3-319-03844-5_65

Cited by 8 publications

(1 citation statement)

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“…Taking advantage of previous research on single user RSs, group recommendations are generated by extending them. Thus, there exist two basic approaches: Rating aggregation , which consists of aggregating individual ratings of each member to compute an aggregated group rating profile or pseudouser and perform individual recommendation using this profile as input. Prediction aggregation , which aggregates each member's recommendations list into a list targeted to the group (as illustrated in Figure ). …”

Section: Preliminariesmentioning

confidence: 99%

A Consensus-Driven Group Recommender System

Castro

Quesada

Palomares

et al. 2015

Int. J. Intell. Syst.

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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.

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