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Open AccessArticle

Cognitive Similarity-Based Collaborative Filtering Recommendation System

1
Department of Computer Engineering, Chung-Ang University, 84 Heukseok, Seoul 156-756, Korea
2
Big Data Research Group, Western Norway Research Institute, Box 163, NO-6851 Sogndal, Norway
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(12), 4183; https://doi.org/10.3390/app10124183
Received: 20 May 2020 / Revised: 16 June 2020 / Accepted: 16 June 2020 / Published: 18 June 2020
(This article belongs to the Special Issue Recommender Systems and Collaborative Filtering)
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 P r e c i s i o n and R e c a l l . 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. View Full-Text
Keywords: cognitive similarity; recommendation system; collaborative filtering cognitive similarity; recommendation system; collaborative filtering
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Nguyen, L.V.; Hong, M.-S.; Jung, J.J.; Sohn, B.-S. Cognitive Similarity-Based Collaborative Filtering Recommendation System. Appl. Sci. 2020, 10, 4183.

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