Performance comparison of top N recommendation algorithms

Ghulam Mustafa
,
Ingo Frommholz

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Abstract

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.

Publication Information

Output type

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Original language

English

Publication milestones

Published - 26/10/2015

Publication status

Published - 26/10/2015

Publisher

Institute of Electrical and Electronics Engineers Inc., United States

ISBN (Print)

9781479982660

External Publication IDs

handle.net: 10547/624258
Scopus: 84963610871

Host publication title

2015 Fourth International Conference on Future Generation Communication Technology (FGCT)

Access to documents

10.1109/FGCT.2015.7300256

Publication metrics

Metrics

Scopus