Research on E-Commerce Short Video Recommendation System based on User Behavior Data Mining and Feature Extraction

This paper investigates the development of an e-commerce short video recommendation system based on user behavior data mining and feature extraction, and the core of our approach is the application of convolutional neural networks (CNNS) in the broader scope of data-driven insights. By harnessing the power of CNN, we aspire to dig deep into the vast library of e-commerce short videos and decipher the visual cues and patterns that define user preferences and product characteristics. Through meticulous model training, we aim to achieve high-fidelity identification of key video frames and then automatically label and classify video content based on user interests and product categories. We then designed a CNN architecture specifically tailored to the nuances of e-commerce video content, optimizing it to recognize not only product features, but also subtle contexts that affect user engagement. Using an extensive data set with user behavior patterns and product metadata annotations, we conducted an exhaustive training regimen that allows the model to proficiently grasp and interpret the various product demonstrations and user interactions depicted in the videos. Initial evaluations show that our proposed framework significantly improves the accuracy of video classification.