Exploration of Precise Knowledge Service Mode of Video Websites Based on Group User Portrait

With the advent of the information age, video-sharing websites have become an integral part of people's lives as a medium of information. The pandemic has further established these platforms as the optimal carriers for knowledge dissemination. Faced with users' growing demand for diverse knowledge, video-sharing websites need to pursue precise service models to meet user needs and increase retention rates. Therefore, this paper designs a precise knowledge service model for video-sharing websites based on group user profiles using methods such as K-means clustering analysis. A subset of users from the Bilibili platform is selected for empirical validation. The study shows that the result set obtained through collaborative filtering significantly enhances user satisfaction compared to the initial result set, with a noticeable improvement. The findings indicate that the proposed knowledge service model based on group user profiles effectively improves search accuracy and user satisfaction.