Design of Personalized Book Recommendation System for Smart Library Based on Recommendation Algorithm and User Behavior Analysis

This study aims to build a personalized recommendation system for smart libraries, which combines recommendation algorithms and user behavior analysis to improve the personalization level and user experience of library services. In the system launch experiment, it was found that the error rate was 2.5% before the system was launched. This indicates that there is an approximately 2.5% probability of errors occurring when the system is processing user requests. After the system went online, the error rate decreased to 1.2%. This indicates that the probability of errors significantly decreases when processing user requests after the system goes online. Through the comparison of error rates, we found that the system has made significant improvements in error handling after going live, which helps to enhance user satisfaction and overall system performance. This study provides practical guidance for the design and optimization of smart libraries, and offers new ideas and methods for the development and application of recommendation systems.