Affiliation
University of BedfordshireIssue Date
2015-10-26Subjects
Memory Based CFRecommender System
ROC Curve
Collaborative Filtering
Singular Value Decomposition (SVD)
Principle Component Analysis (PCA)
Model Based CF
Metadata
Show full item recordAbstract
In 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.Citation
Mustafa 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..Additional Links
https://ieeexplore.ieee.org/document/7300256Type
Conference papers, meetings and proceedingsLanguage
enISBN
9781479982660ae974a485f413a2113503eed53cd6c53
10.1109/FGCT.2015.7300256