A Group Recommendation Model Using Diversification Techniques

dc.contributor.authorOliveira, Amanda
dc.contributor.authorDurao, Frederico
dc.date.accessioned2020-12-24T19:32:38Z
dc.date.available2020-12-24T19:32:38Z
dc.date.issued2021-01-05
dc.description.abstractIn 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.extent10 pages
dc.identifier.doi10.24251/HICSS.2021.326
dc.identifier.isbn978-0-9981331-4-0
dc.identifier.urihttp://hdl.handle.net/10125/70940
dc.language.isoEnglish
dc.relation.ispartofProceedings of the 54th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData Analytics, Data Mining and Machine Learning for Social Media
dc.subjectconsensus
dc.subjectdiversity
dc.subjectgroup recommendation
dc.titleA Group Recommendation Model Using Diversification Techniques
prism.startingpage2669

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