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Cognitive similarity-based collaborative filtering recommendation systemopen access

Authors
Nguyen, L.V.Hong, M.-S.Jung, J.J.Sohn, B.-S.
Issue Date
Jun-2020
Publisher
MDPI AG
Keywords
Cognitive similarity; Collaborative filtering; Recommendation system
Citation
Applied Sciences (Switzerland), v.10, no.12, pp 1 - 14
Pages
14
Journal Title
Applied Sciences (Switzerland)
Volume
10
Number
12
Start Page
1
End Page
14
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/44072
DOI
10.3390/APP10124183
ISSN
2076-3417
2076-3417
Abstract
This paper provides a new approach that improves collaborative filtering results in recommendation systems. In particular, we aim to ensure the reliability of the data set collected which is to collect the cognition about the item similarity from the users. Hence, in this work, we collect the cognitive similarity of the user about similar movies. Besides, we introduce a three-layered architecture that consists of the network between the items (item layer), the network between the cognitive similarity of users (cognition layer) and the network between users occurring in their cognitive similarity (user layer). For instance, the similarity in the cognitive network can be extracted from a similarity measure on the item network. In order to evaluate our method, we conducted experiments in the movie domain. In addition, for better performance evaluation, we use the F-measure that is a combination of two criteria Precision and Recall. Compared with the Pearson Correlation, our method more accurate and achieves improvement over the baseline 11.1% in the best case. The result shows that our method achieved consistent improvement of 1.8% to 3.2% for various neighborhood sizes in MAE calculation, and from 2.0% to 4.1% in RMSE calculation. This indicates that our method improves recommendation performance. © 2020 by the authors.
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소프트웨어대학 (소프트웨어학부)
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