An Improved Collaborative Filtering Algorithm Based on Artificial Intelligence and the Internet of Things for Graduate Employment Recommendations

In recent years, the steady increase in university graduates has intensified competition in the job market. Traditional career platforms often display static job listings without personalized guidance. To address this gap, we propose an intelligent employment recommendation system that integrates artificial intelligence (AI) and Internet of Things (IoT) data. We first extract graduate profiles using a hybrid conditional random field (CRF) and long short-term memory (LSTM) neural network. Next, we apply an optimized K-means clustering algorithm with weighted similarity to group similar profiles. We then enhance a user-based collaborative filtering model with enterprise popularity scores and influencer impact factors to compute job-seeking similarity. Experiments on data from a Xi’an university demonstrate that our method outperforms standard user-based and cluster-based approaches, improving recommendation accuracy by 17.8% and 15.9%, respectively. This system delivers context-aware job suggestions that increase the success rate of graduate employment.