Personalized Recommendation Method of Economics Online Teaching Curriculum Resources Based on Fuzzy Analytic Hierarchy Process

At present, the digitalization and networking of educational resources has become an inevitable trend. In such a trend, there will inevitably be a large number of online teaching curriculum resources, which will make it difficult to retrieve and share resources, and learners will suffer from information overload and information maze. When applying conventional recommendation methods to recommend users’ personalities, the characteristics of resources themselves are often ignored, and there is a cold start problem. Therefore, a personalized recommendation method of economics online teaching curriculum resources based on fuzzy analytic hierarchy process is studied. The method first collects and processes user behavior data, and then uses the fuzzy analytic hierarchy process to evaluate the user’s rating of resources and establish a rating matrix. User based collaborative filtering recommendation is used to calculate user similarity within the scope of similar projects and find the nearest neighbor. The final prediction score is calculated according to the nearest neighbor and target user scores, and recommendations are made according to the predicted score. The results show that under the same number of neighbors, the average absolute deviation of the recommendation method based on fuzzy analytic hierarchy process is relatively smaller, and the precision coefficient is relatively larger, which indicates that the method has better recommendation quality.