Research on Career Counselling Platform Based on Collaborative Filtering Recommendation Algorithm

The amount of information on the Internet is too large and the information is overloaded, which cannot meet the growing personalized needs of people. The recommendation system is deeply applied in real life, changing the way people obtain information, from active query to the era of personalized recommendation. With the gradual development and improvement of machine learning technology, the recommendation system is gradually using the ideas of machine learning to make recommendations. Collaborative filtering recommendation algorithm is the most mature and widely used personalized recommendation technology. The traditional collaborative filtering recommendation algorithm has data sparsity and cold start problems, and the collaborative filtering recommendation algorithm based on cluster analysis can effectively solve these problems. The collaborative filtering recommendation algorithm based on cluster analysis develops a career consulting platform, which can effectively solve the problems faced by college students’ career planning, assist career planning education, and improve the employment rate and future career development level of college students. The collaborative filtering recommendation algorithm achieved high values of 97.80% accuracy.