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Improving the accuracy of item recommendations in collaborative filtering using time-variant system

Improving the accuracy of item recommendations in collaborative filtering using time-variant system Nowadays, the real challenge is to manage the dynamic web content in order to provide a prolific resource to the user. Web personalisation is an outcome of the challenge by which the web is a tailor made to a user. Recommendation systems access the user profile using collaborative filtering and content based filtering to provide better personalisation. This paper focuses on improving the accuracy of item recommendations, based on the dynamic item-based collaborative filtering by utilising time variant system which is implied on user ratings. Similarity between the items is found by using vector similarity and weight is calculated by Pearson correlation coefficient. Comparison of the results of traditional item-based collaborative filtering with dynamic item-based collaborative filtering is also discussed. Finally, it is observed that the user's dynamic voting average improves the accuracy of recommendations comparing to the normal voting average on items. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Electronic Government, an International Journal Inderscience Publishers

Improving the accuracy of item recommendations in collaborative filtering using time-variant system

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1740-7494
eISSN
1740-7508
DOI
10.1504/EG.2017.087948
Publisher site
See Article on Publisher Site

Abstract

Nowadays, the real challenge is to manage the dynamic web content in order to provide a prolific resource to the user. Web personalisation is an outcome of the challenge by which the web is a tailor made to a user. Recommendation systems access the user profile using collaborative filtering and content based filtering to provide better personalisation. This paper focuses on improving the accuracy of item recommendations, based on the dynamic item-based collaborative filtering by utilising time variant system which is implied on user ratings. Similarity between the items is found by using vector similarity and weight is calculated by Pearson correlation coefficient. Comparison of the results of traditional item-based collaborative filtering with dynamic item-based collaborative filtering is also discussed. Finally, it is observed that the user's dynamic voting average improves the accuracy of recommendations comparing to the normal voting average on items.

Journal

Electronic Government, an International JournalInderscience Publishers

Published: Jan 1, 2017

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