AffiliationUniversity of Bedfordshire
SubjectsMemory Based CF
Singular Value Decomposition (SVD)
Principle Component Analysis (PCA)
Model Based CF
MetadataShow full item record
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.
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..
TypeConference papers, meetings and proceedings