E-commerce Platform Product Recommendation Algorithm Combining SVM Algorithm and Attention Mechanism

The recommendation system of e-commerce platform is an important link to improve user experience and promote orders. This paper first improves the recommendation algorithm, then designs data preprocessing and feature engineering for machine learning algorithm, and demonstrates the model construction process. Finally, through carefully designed data stream processing, feature selection and model training, the recommendation system can truly understand and predict complex user behavior patterns. It can be seen from the experimental data that the recommended coverage rate of the system is stable at 81.2%-98.4%, which proves that the system has a wide range of products for users to recommend. At the same time, after adopting the attention mechanism, the accuracy of the system is rapidly improved, and the accuracy of the recommendation system will reach 99.5%. To sum up, this paper further optimizes the parameter structure and algorithm of the model, and optimizes the system security. The high load performance is due to its limited space.