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Communication Papers of the 17th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 32

Evaluating Diversification in Group Recommender Systems

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DOI: http://dx.doi.org/10.15439/2022F75

Citation: Communication Papers of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 32, pages 47–54 ()

Full text

Abstract. The formation of groups is an ordinary event in ourroutines. For example, people used to lunch, travel, or hang out in groups. Conversely, getting a consensus over an item may be difficult for some groups as the number of digital information increases. Group Recommender Systems (GRS) rise to assist in this task, as they filter which items may be more relevant to the group. Although there are consensus techniques to help in this matter, recommendations to groups can become monotonous, and this opens space for applying diversification techniques to improve recommendations. In this paper, we expose a model for recommendation to groups using diversification techniques and present the results of the online experiment where the proposal obtained an increase in precision at all levels compared with baseline.

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