Categorizing Approaches to Justify Recommendations

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dc.contributor.authorYacouba Kyelem
dc.contributor.authorT. Frédéric Ouedraogo
dc.contributor.authorK. Kisito Kabore
dc.date.accessioned2024-11-05T12:24:03Z
dc.date.available2024-11-05T12:24:03Z
dc.date.issued2024-10-28
dc.description.abstractRecommendation justification enables users to understand the reasons and motivation behind the recommendation of an item in a recommender system. It makes the recommendation model much more transparent, and improves user satisfaction. It is because of the important role assigned to the justification of recommendations that the present work aims to identify the approaches and methods for justifying recommendations that exist in the literature. The state of the art has enabled us to categorize the different approaches to recommendation justification. There are two approaches to recommendation justification: the linked model and the post-hoc models. The data used for justification are external or internal to the items.
dc.identifier.citationKyelemn Y., Ouédraogo T. F. & Kaboré K. K. (2023). “Categorizing Approaches to Justify Recommendations". In Proceedings of the International Conference on Artificial Intelligence and Robotics (MIRG-ICAIR 2023), pp. 33-38, MIRG
dc.identifier.isbn978-978-767-697-4
dc.identifier.urihttps://ir.unilag.edu.ng/handle/123456789/13033
dc.language.isoen
dc.publisherMIRG
dc.relation.ispartofseriesMIRG-ICAIR 2023
dc.titleCategorizing Approaches to Justify Recommendations
dc.typeArticle
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