Dynamic particle swarm optimization for personalized recommender system based on electroencephalography feedback

Rapid growth of recommender systems (RSs) had proved its potential in the generation of personalized recommendations in various application domains. Generally, RSs learn the user's preferences and interests to suggest relevant items to the users. RSs are widely employed in various domains such as movies, e-commerce, travel, etc. Due to rapid growth in travel applications, Travel Recommender Systems (TRSs) had received a significant attention from researchers. Though existing TRS help users as digital support assistants in the travel, still the TRSs faces huge barriers in understanding user interests based on user's current emotional context. In this paper, to generate effective personalized Point of Interest (POI) recommendations, we present a Dynamic Particle Swarm Optimization and Hierarchy Induced K-Means (DPSOHiK) approach for the better POI clustering through utilizing electroencephalography (EEG) feedback. The DPSOHiK approach, with its capabilities to adapt the changing attributes helps in the POI clustering process. The clustered POIs are utilized in the recommendation process and based on the user's personal preferences the POIs are ranked to meet the requirements of the user. We have experimentally evaluated our proposed recommendation approach to demonstrate the recommendation potential and compared the obtained results with the baseline approaches. The experimental results depict the importance of EEG feedback in the enhancement of recommendation accuracy and provide helpful insights to the researchers to utilize EEG in the RSs research and development.