Individualized extreme dominance (IndED) : a new preference- based method for multi-objective recommender systems.

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dc.contributor.authorFortes, Reinaldo Silva
dc.contributor.authorSousa, Daniel Xavier de
dc.contributor.authorCoelho, Dayanne Gouveia
dc.contributor.authorLacerda, Anisio Mendes
dc.contributor.authorGonçalves, Marcos André
dc.date.accessioned2022-10-14T20:48:37Z
dc.date.available2022-10-14T20:48:37Z
dc.date.issued2021pt_BR
dc.description.abstractRecommender Systems (RSs) make personalized suggestions of relevant items to users. However, the concept of relevance may involve different quality aspects (objectives), such as accuracy, novelty, and diversity. In addition, users may have their own expectations regarding what characterizes a good recommendation. More specifically, individual users may wish to prioritize the multiple objectives in different proportions based on their preferences. Previous studies on Multi-Objective (MO) recommendation do not prioritize objectives according to the individual users’ preferences systematically or are biased towards a single objective as in re-ranking strategies. Moreover, traditional preference-based multi-objective solutions do not address the specificities of RSs. In this work, we pro- pose IndED (Individualized Extreme Dominance), a new preference-based method for MORSs. IndED explores the concepts of Extreme Dominance and Statistical Significance Tests in order to define a new Pareto-based dominance relation that guides the optimization search considering users’ preferences. We also consider a new decision making process that minimizes the distance to the individual user’s preferences. Experiments show that IndED outperformed competitive baselines, obtaining results closer to the users’ preferences and better balancing the objectives trade-offs. IndED is also the method that obtains the best performance regarding the most difficult objective in each considered scenario.pt_BR
dc.identifier.citationFORTES, R. S. et al. Individualized extreme dominance (IndED): a new preference- based method for multi-objective recommender systems. Information Sciences, v. 572, p. 558-573, 2021. Disponível em: <https://www.sciencedirect.com/science/article/pii/S0020025521004977>. Acesso em: 06 jul. 2022.pt_BR
dc.identifier.doihttps://doi.org/10.1016/j.ins.2021.05.037pt_BR
dc.identifier.issn0020-0255
dc.identifier.urihttp://www.repositorio.ufop.br/jspui/handle/123456789/15692
dc.identifier.uri2https://www.sciencedirect.com/science/article/pii/S0020025521004977pt_BR
dc.language.isoen_USpt_BR
dc.rightsrestritopt_BR
dc.subjectInformation filteringpt_BR
dc.subjectMulti-objective optimizationpt_BR
dc.subjectDecision makingpt_BR
dc.subjectHybrid filteringpt_BR
dc.titleIndividualized extreme dominance (IndED) : a new preference- based method for multi-objective recommender systems.pt_BR
dc.typeArtigo publicado em periodicopt_BR
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