Proactive university library book recommender system

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dc.contributor.authorMekonnen, Tadesse Zewdu
dc.contributor.supervisorZuva, Tranos, Prof.
dc.date.accessioned2022-12-12T02:50:24Z
dc.date.available2022-12-12T02:50:24Z
dc.date.issued2021
dc.descriptionM. Tech. (Department of Information Communication Technology, Faculty of Applied and Computer Sciences), Vaal University of Technology.en_US
dc.description.abstractToo many options on the internet are the reason for the information overload problem to obtain relevant information. A recommender system is a technique that filters information from large sets of data and recommends the most relevant ones based on peopleā€Ÿs preferences. Collaborative and content-based techniques are the core techniques used to implement a recommender system. A combined use of both collaborative and content-based techniques called hybrid techniques provide relatively good recommendations by avoiding common problems arising from each technique. In this research, a proactive University Library Book Recommender System has been proposed in which hybrid filtering is used for enhanced and more accurate recommendations. The prototype designed was able to recommend the highest ten books for each user. We evaluated the accuracy of the results using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). A measure value of 0.84904 MAE and 0.9579 RMSE found by our system shows that the combined use of both techniques gives an improved prediction accuracy for the University Library Book Recommender System.en_US
dc.identifier.urihttp://hdl.handle.net/10352/580
dc.language.isoenen_US
dc.publisherVaal University of Technologyen_US
dc.subjectUniversity Library Book Recommender Systemen_US
dc.subjectCollaborative and content-based techniquesen_US
dc.subjectHybrid techniquesen_US
dc.subjectHybrid filteringen_US
dc.subjectMean Absolute Error (MAE)en_US
dc.subjectRoot Mean Squared Error (RMSE)en_US
dc.subject.lcshDissertations, Academic -- South Africaen_US
dc.subject.lcshArtificial intelligence -- Data processingen_US
dc.subject.lcshElectronic data processingen_US
dc.subject.lcshSelf-organizing systemsen_US
dc.subject.lcshRecommender systems (Information filtering)en_US
dc.titleProactive university library book recommender systemen_US
dc.typeThesisen_US
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