Reinforcement Learning-Based Recommendation System
Digital Marketing: AI Recommendation
Fig. 1
Fig. 2
The aim of the project is to develop an intelligent system for product design and retailing. The system consists of two modules. The first module is the emotion recognition and processing module which estimates the emotional state of the users based on their biometric data including facial expressions, body gestures, electroencephalogram (EEG), electrocardiogram (ECG) and eye movement when being shown different items. This determines the personal preferences of the users. Fuzzy inference system is used for this process. The second module is the recommender module, which provides personalized product and design recommendation to customers based on the information generated by the emotion recognition and processing system. Utilizing reinforcement learning, the system learns to react to the users’ emotional responses. With these modules combined, a system is developed that provides personalized recommendations based on biometric data. Fig. 1 illustrates the framework of the system. The developed system is then integrated into an immersive virtual shopping experience in virtual reality (VR). Fig. 2 shows a screenshot of the prototype application in development.
By bypassing potentially unreliable self-report and directly examining cognitions and emotions through biometric data, accurate information on customer preferences can be extracted. The provision of personalized recommendation in turn improves the probability of sales and customers’ user experience by eliminating information overload. It is also in line with the direction towards hyper-personalization and marketing automation in retailing. Furthermore, the application of the system in immersive environment demonstrates the systems’ viability and potential in the emerging Metaverse.
We are excited to showcase the capabilities of our intelligent system for product design and retailing through a video demo on personalized furniture recommendations. Our system utilizes biometric data, including facial expressions, body gestures, EEG, ECG, and eye movement, to estimate the emotional state of the user and determine their personal preferences. The system then provides personalized product and design recommendations to the customers through a recommender module. The immersive virtual shopping experience in virtual reality further enhances the user experience and allows for the seamless integration of our system into the emerging Metaverse. The video demo demonstrates how our system can revolutionize the retail industry by providing accurate and personalized recommendations while improving the probability of sales and customer satisfaction.
