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dc.contributor.authorMustafa, Ghulam
dc.contributor.authorFrommholz, Ingo
dc.date.accessioned2020-07-21T11:59:04Z
dc.date.available2020-07-21T11:59:04Z
dc.date.issued2015-10-26
dc.identifier.citationMustafa G, Frommholz I (2015) 'Performance comparison of top N recommendation algorithms', 2015 Fourth International Conference on Future Generation Communication Technology (FGCT) - Luton, Institute of Electrical and Electronics Engineers Inc..en_US
dc.identifier.isbn9781479982660
dc.identifier.doi10.1109/FGCT.2015.7300256
dc.identifier.urihttp://hdl.handle.net/10547/624258
dc.description.abstractIn traditional recommender systems, services/items are recommended to the user based on the initial ratings while the results comes from the predicted rating values are not considered which further refers to top N recommendations. In top N recommendation algorithms, recommendation process is further enhanced by predicting the missing ratings where the basic objective is to find the items that might be interest of a user. Performance comparison and evaluation of different top N recommendation algorithms is quite challenging for large datasets where selection of an appropriate algorithm can help to improve the recommendation process by predicting missing ratings. Therefore, in this paper we analyse and evaluate the 6 different top N recommendation algorithms using accuracy metrics such as precision and recall on Movie-lense 100K dataset from the Group-lens. Our main finding is the selection of Top N recommendation algorithm that perform significantly better than other recommender algorithms in pursuing the top-N recommendation process.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.urlhttps://ieeexplore.ieee.org/document/7300256en_US
dc.subjectMemory Based CFen_US
dc.subjectRecommender Systemen_US
dc.subjectROC Curveen_US
dc.subjectCollaborative Filteringen_US
dc.subjectSingular Value Decomposition (SVD)en_US
dc.subjectPrinciple Component Analysis (PCA)en_US
dc.subjectModel Based CFen_US
dc.titlePerformance comparison of top N recommendation algorithmsen_US
dc.typeConference papers, meetings and proceedingsen_US
dc.contributor.departmentUniversity of Bedfordshireen_US
dc.date.updated2020-07-21T11:57:07Z
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