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Enhancing Prediction Accuracy of a Multi-Criteria Recommender System using Adaptive Genetic Algorithm

dc.contributor.authorAbdulsalam, Ometere Latifat
dc.description.abstractRecommender systems are powerful intelligent systems considered to be the solution to the problems of information overload. They provide users with personalized lists of recommended items, using some machine learning techniques. Traditionally, existing recommender systems have used single rating techniques to estimate users' opinions on items. Because user preferences might depend on the attributes of several items, the efficiency of traditional single-rating recommender systems is considered to be limited, since they cannot account for various items' attributes. A multi-criteria recommendation is a new technique that uses ratings of various items' attributes to make more efficient predictions. Nevertheless, despite the proven accuracy improvements of multi-criteria recommendation techniques, research needs to be done continuously to establish an efficient way to model criteria ratings. This project, therefore, propose to use an adaptive genetic algorithm to model multi-criteria recommendation problems, using an aggregation function approach. The anticipated empirical results of the project show that the proposed approach provides more accurate predictions than traditional recommendation approaches.en_US
dc.description.sponsorshipAUST, AfDBen_US
dc.subjectrecommender systemsen_US
dc.subjectmulti-criteria recommendation techniquesen_US
dc.subjectprediction accuracyen_US
dc.subjectadaptive genetic algorithmen_US
dc.subjectAbdulsalam Ometere Latifaten_US
dc.subject2017 Computer Science and Engineering Thesesen_US
dc.subjectMohammed Hamadaen_US
dc.titleEnhancing Prediction Accuracy of a Multi-Criteria Recommender System using Adaptive Genetic Algorithmen_US

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  • Computer Science105

    This collection contains Computer Science Student's Theses from 2009-2022

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