Intelligent Recommendation Algorithm for Website Content Update and Maintenance

With the rapid development of the current digital information age, personalized content recommendation will become the key to improve user experience. Compared with traditional recommendation systems based on rules or simple algorithms, intelligent recommendation algorithms can provide more dynamic and personalized recommendations, so as to attract and maintain user interest and participation more effectively in the updating and maintenance of website content. Therefore, this paper first explains the real-time content update mechanism of the website, then analyzes the user behavior analysis and content recommendation algorithm based on collaborative filtering, and then explores the user feedback loop and algorithm adjustment. Finally, through two sets of simulation experiments, the following conclusions are drawn: Compared with the traditional recommendation algorithm, the page stay time of collaborative filtering algorithm is increased by 1.8 minutes, the click-through rate is increased by 0.5%, and the relevance score is increased by 1.3. This shows that the intelligent recommendation algorithm based on collaborative filtering has a good effect in practical application. The research of this intelligent recommendation algorithm promotes the development of relevant website content update and maintenance technology, and plays a certain role in promoting the understanding and application of big data applications.