Algorithm Design and Optimization of Data Mining Technology in Reader Service Recommendation System

With the continuous development of the information age, data mining technology has become a hot research topic in modern society. This article optimized and designed a reader service recommendation system based on literature analysis and experimental research methods. Firstly, the application status and existing problems of data mining algorithms in relevant fields at home and abroad were introduced. After that, an improved idea was proposed, which combined the association rule extraction model (log statistics) with the construction of artificial neural networks to make it easier for users to discover potential rule features and improve the user experience. Finally, data mining algorithms were used to classify the content information of readers’ text, and the performance of this algorithm was tested afterwards. The test results showed that the availability of the algorithm system was over 90%, the reliability of use was over 80%, the time between failures was between 3 seconds and 6 seconds, and the page response time was between 1 second and 3 seconds. The reader service recommendation system has discovered that data mining techniques can help select the most relevant features from a vast array of features, reducing computational complexity and improving model performance. Research has found that by using feature selection and dimensionality reduction techniques, redundant and irrelevant features can be eliminated, thereby improving the efficiency and accuracy of recommendation algorithms.