Next Article in Journal
Collision Avoidance Method Using Vector-Based Mobility Model in TDMA-Based Vehicular Ad Hoc Networks
Next Article in Special Issue
SoftRec: Multi-Relationship Fused Software Developer Recommendation
Previous Article in Journal
Time of Your Hate: The Challenge of Time in Hate Speech Detection on Social Media
Previous Article in Special Issue
Neighborhood Aggregation Collaborative Filtering Based on Knowledge Graph

Cognitive Similarity-Based Collaborative Filtering Recommendation System

Department of Computer Engineering, Chung-Ang University, 84 Heukseok, Seoul 156-756, Korea
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;
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
Show Figures

Figure 1

MDPI and ACS Style

Nguyen, L.V.; Hong, M.-S.; Jung, J.J.; Sohn, B.-S. Cognitive Similarity-Based Collaborative Filtering Recommendation System. Appl. Sci. 2020, 10, 4183.

AMA Style

Nguyen LV, Hong M-S, Jung JJ, Sohn B-S. Cognitive Similarity-Based Collaborative Filtering Recommendation System. Applied Sciences. 2020; 10(12):4183.

Chicago/Turabian Style

Nguyen, Luong Vuong, Min-Sung Hong, Jason J. Jung, and Bong-Soo Sohn. 2020. "Cognitive Similarity-Based Collaborative Filtering Recommendation System" Applied Sciences 10, no. 12: 4183.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop