Improving Top-N recommendations with user consuming profiles
Version 2 2024-06-04, 01:50Version 2 2024-06-04, 01:50
Version 1 2014-10-28, 10:01Version 1 2014-10-28, 10:01
conference contribution
posted on 2024-06-04, 01:50authored byY Ren, Gang LiGang Li, W Zhou
In this work, we observe that user consuming styles tend to change regularly following some profiles. Therefore, we propose a consuming profile model to capture the user consuming styles, then apply it to improve the Top-N recommendation. The basic idea is to model user consuming styles by constructing a representative subspace. Then, a set of candidate items can be estimated by measuring its reconstruction error from its projection on the representative subspace. The experiment results show that the proposed model can improve the accuracy of Top-N recommendations much better than the state-of-the-art algorithms.
History
Pagination
887-890
Location
Kuching, Malaysia
Start date
2012-09-03
End date
2012-09-07
ISSN
0302-9743
ISBN-13
9783642326943
Language
eng
Publication classification
E1 Full written paper - refereed
Extent
95
Editor/Contributor(s)
Anthony P, Ishizuka M, Lukose D
Title of proceedings
PRICAI 2012 : Trends in Artificial Intelligence : 12th Pacific Rim International Conference on Artificial Intelligence, Kuching, Malaysia September 3-7 2012 : proceedings
Event
Pacific Rim International Conference on Artificial Intelligence (12th : 2012 : Kuching, Malaysia)