Collaborative filtering recommendation based on K-nearest neighbor and non-negative matrix factorization algorithm

Traditional collaborative filtering recommendation algorithms suffer from low recommendation efficiency and poor accuracy when calculating similarities between users or items. To address this issue and improve the efficiency of recommendation systems, the paper introduces an algorithm called K-nearest neighbors and non-negative matrix factorization (KNNCNMF) collaborative filtering recommendation algorithm. When calculating the similarity between users or items, the algorithm extracts the latent factors of users and items through matrix decomposition, constructs a low-dimensional dense “user–item factor” matrix, and inputs it into the classifier for rating prediction, which replaces the complex similarity calculation and further improves the efficiency of the user–item similarity calculation. We use performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Precision, and Recall to measure our method. The experimental results show that compared to other algorithms, our method improves the MAE metric by 1.78% on average, the RMSE metric by 4.48% on average, the Precision metric by 4.66% on average, and the Recall metric by 7.95% on average. It proves the effectiveness of our proposed method.