HYBRID PRODUCT RECOMMENDATION SYSTEM AND METHODS BASED ON REAL-TIME SYNCHRONIZATION OF CONSUMER PHYSICAL INTERACTIONS AND DIGITAL HISTORY WITH ADAPTIVE WEIGHTING

Abstract

This invention concerns a hybrid product recommendation system and method that integrates consumer physical interaction data in a retail environment with real-time digital history data to generate more personalized, contextual, and adaptive product recommendations. The system comprises a physical interaction detection module, a consumer identification module, a real-time data integration module, an adaptive weighting-based recommendation engine, and a multi-channel recommendation output module. The physical interaction detection module records consumer activity towards a product, including touching, picking up the product, interaction duration, direction of attention, and proximity to the display area using RFID sensors, computer vision-based cameras, or other sensors. The data is synchronized with the consumer's digital history, which includes search history, purchases, product clicks, and user preferences.The recommendation engine implements a time-decay-based adaptive weighting mechanism to dynamically balance the contributions of physical and digital data based on actual consumer behavior. The system also features an adaptive learning mechanism based on consumer feedback, enabling continuous updating of recommendations. This invention improves the relevance and accuracy of product recommendations and supports hybrid marketing strategies in modern retail environments.