Cognitive Similarity-Based Collaborative Filtering Recommendation System

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 cognit...

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Bibliographic Details
Published in:Applied sciences 2020-06, Vol.10 (12), p.4183
Main Authors: Nguyen, Luong Vuong, Hong, Min-Sung, Jung, Jason J., Sohn, Bong-Soo
Format: Article
Language:English
Subjects:
Algorithms
Analysis
cognitive similarity
Collaboration
collaborative filtering
Consumer education
Crowdsourcing
Datasets
Feedback
Filtration
Innovations
Internet filtering software
Preferences
Psychological aspects
Random variables
Ratings & rankings
recommendation system
Recommender systems
Similarity
Similarity (Psychology)
Similarity measures
Technology application
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app10124183