Intelligent Location-Based Recommendation System for Enhancing Shopping Experience

The challenge of meeting the changing preferences of retail customers through personalized shopping experiences has become difficult due to the lack of data in offline stores. The proposed approach uses intelligent location-based recommendation systems powered by machine learning and artificial intelligence to improve the entire customer purchasing process. Using smart shopping carts equipped with tags and time-tracking capabilities, the proposed system monitors and analyzes the behavior of customers across the store. This data is then integrated with customer profiles to uncover valuable insights, including purchasing patterns, preferences, and correlations. Using decision trees, collaborative filtering, content-based filtering, and a rule mining algorithm, the proposed system optimizes inventory management and store layout and provides personalized product recommendations. Through these techniques, retailers can effectively personalize the shopping experience and ultimately increase sales and customer satisfaction.