Design of Personalized Recommendation System for E‐Commerce Marketing Based on Sensor Data Aggregation and User Behavior Analysis

With the rapid development of e‐commerce, personalized recommendation systems can enhance user shopping experience and promote merchant sales. However, traditional personalized recommendation systems often only rely on user behavior data, ignoring the potential value of sensor data in user purchasing decisions. The research aims to develop a personalized e‐commerce marketing recommendation system based on sensor data aggregation and user behavior analysis, in order to better understand and utilize the impact of sensor data on user purchasing decisions. The system collects user data through sensing devices and aggregates and analyzes the collected sensing data using machine learning algorithms and data mining techniques. By aggregating sensor data, the system can extract useful information and patterns, thereby better understanding the impact of different environmental factors on user purchasing decisions. The system also analyzes user behavior data to understand their preferences, purchasing habits, and personalized needs. Based on the user's current environmental conditions and behavioral preferences, the system can accurately predict the products that the user may be interested in and recommend these products to the user. This way, the user can receive personalized product recommendations that meet their needs, improve the shopping experience and satisfaction.