| Title | Toward a BCI-Based Personalized Recommender System Using Deep Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Li, Sukun, Liu, Xiaoxing |
| Conference Name | 2022 IEEE 8th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS) |
| Date Published | may |
| Keywords | Big Data, Brain modeling, brain-computer interfaces, brain-computer Interfaces (BCI), Computational modeling, Conferences, Deep Learning, eeg, Human Behavior, human factors, Predictive models, preference prediction, pubcrawl, recommender system, recommender systems, resilience, Resiliency, Scalability |
| Abstract | A recommender system is a filtering application based on personalized information from acquired big data to predict a user's preference. Traditional recommender systems primarily rely on keywords or scene patterns. Users' subjective emotion data are rarely utilized for preference prediction. Novel Brain Computer Interfaces hold incredible promise and potential for intelligent applications that rely on collected user data like a recommender system. This paper describes a deep learning method that uses Brain Computer Interfaces (BCI) based neural measures to predict a user's preference on short music videos. Our models are employed on both population-wide and individualized preference predictions. The recognition method is based on dynamic histogram measurement and deep neural network for distinctive feature extraction and improved classification. Our models achieve 97.21%, 94.72%, 94.86%, and 96.34% classification accuracy on two-class, three-class, four-class, and nine-class individualized predictions. The findings provide evidence that a personalized recommender system on an implicit BCI has the potential to succeed. |
| DOI | 10.1109/BigDataSecurityHPSCIDS54978.2022.00042 |
| Citation Key | li_toward_2022 |
Toward a BCI-Based Personalized Recommender System Using Deep Learning