Design of a Web Content Personalized Recommendation System Based on Collaborative Filtering Improved by Combining k-Means and LightGBM

To improve the precision of Web content personalized recommendation, a Web content personalized recommendation system based on collaborative filtering improved by combining k-means and LightGBM is proposed. Firstly, the k-means clustering algorithm (k-means) is improved by using the Rat Swarm Optimizer (RSO) algorithm to cluster and group users and Web content. At the same time, Light Gradient Boosting Machine (LightGBM) algorithm is introduced to predict the level of interest of users in web content, and collaborative filtering recommendation method improved by combining k-means and LightGBM is proposed. Then, simulation experiments are conducted, thus verifying the recommendation method. Finally, B/S architecture is used to design and test the recommendation system. The results reveal that MAE and RMSE of the collaborative filtering recommendation method is improved by combining k-means and LightGBM for recommendation on the UserBehavior dataset are 1.08% and 2.41%, respectively, and its precision, recall and F1 are 98.76%, 98.64% and 98.53%, respectively. Therefore, a Web content personalized recommendation system based on collaborative filtering improved by combining k-means and LightGBM has perfect functional modules, and it can meet Web content personalized recommendation, which has certain practical application value.