Personalized Recommendation Method of Online Career Guidance Curriculum Resources Based on Collaborative Filtering

Aiming at the problems of low recommendation efficiency and inaccurate recommendation results of existing resource recommendation algorithms, this paper proposes a personalized recommendation method design of online career guidance curriculum resources based on collaborative filtering. Firstly, analyze the principle of personalized recommendation of course resources, then establish a user social network model, and calculate the similarity based on user interest preferences and course resource ratings. Finally, based on this, complete the design of personalized recommendation methods for online employment guidance course resources. The feasibility of the proposed method was demonstrated through comparative experiments. The test results showed that the MAE value of the proposed method was between 0.7 and 0.78, the average recommendation time was less than 13.3 ms, and the F-value was higher than 0.95, which is superior to the comparative method. The recommendation efficiency is higher and the recommendation results are more accurate, indicating good application value.