A Group Recommendation Model Using Diversification Techniques

dc.contributor.author Oliveira, Amanda
dc.contributor.author Durao, Frederico
dc.date.accessioned 2020-12-24T19:32:38Z
dc.date.available 2020-12-24T19:32:38Z
dc.date.issued 2021-01-05
dc.description.abstract In daily life groups are formed naturally, such as watching a movie with friends, or going out for dinner. In all these scenarios, using Recommendation Systems can be helpful by suggesting pieces of information (e.g. movies or restaurants) that satisfies all rather than a single member in the group. To do so, it is crucial to aggregate individual preferences of the group members aiming at satisfying all. Although there are consensus techniques to create the group profile, the recommendations still may be repetitive and overspecialized. This drawback sets precedent for adopting diversification techniques to group recommendations. In this paper, we propose a group recommendation model using diversification techniques that exploits different aggregation techniques over group preferences matrix. The experiments evaluate accuracy and diversity goals for the group recommendations. Results from the experiments point out that our approach achieved 1.8% of diversity increase and 3.8% of precision improvement over compared methods.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2021.326
dc.identifier.isbn 978-0-9981331-4-0
dc.identifier.uri http://hdl.handle.net/10125/70940
dc.language.iso English
dc.relation.ispartof Proceedings of the 54th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Data Analytics, Data Mining and Machine Learning for Social Media
dc.subject consensus
dc.subject diversity
dc.subject group recommendation
dc.title A Group Recommendation Model Using Diversification Techniques
prism.startingpage 2669
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