Optimizing Content Recommendation System in Innovation and Entrepreneurship Education Platform Using Adaptive Boosting Algorithm

In the current innovation and entrepreneurship education platform, the content recommendation system faces the challenge of diversified and dynamic changes in user interests, resulting in a poor match between recommended content and user needs, affecting learning outcomes and user satisfaction. To solve this problem, this paper proposes an optimization method based on an adaptive Boosting algorithm to improve the accuracy and personalization of the recommendation system. This method first constructs an initial training set by collecting user behavior data and content features; then uses an adaptive Boosting algorithm to iteratively train multiple weak classifiers, where each classifier focuses on samples that the previous classifier failed to correctly classify, and finally these weak classifiers are weighted and combined into a strong classifier. In each iteration, the algorithm adjusts the sample weights based on the classification error to ensure that the model can better capture changes in user interests. The accuracy of the optimized recommendation system has increased to 98%. Specific data show that the click-through rate of recommended content has increased from 79% to 92%. This study proves the effectiveness of the adaptive Boosting algorithm in optimizing the content recommendation system of the innovation and entrepreneurship education platform, providing strong support for improving user experience and learning effects.