Research on User Profile and Personalized Recommendation Based on Collaborative Filtering Algorithm

Due to the rapid development of information technology, fundamental changes have taken place in the way of information production and dissemination, resulting in an increasingly dense information environment for people. In response to the above problems, a personalized recommendation system for immovable cultural relics based on user portraits is planned to be developed. This system can not only make full use of the problems of digital technology, but also reduce the time for users to find information and help users to discover new hobbies. Aiming at the problems of difficult item feature extraction and low degree of personalization in the content-based recommendation algorithm, and the problem of data sparseness and cold start in the collaborative filtering recommendation algorithm, according to the specific requirements of the recommendation scenario, the collaborative filtering recommendation algorithm is mainly adopted, and the content-based recommendation algorithm is mainly based on the collaborative filtering recommendation algorithm. The hybrid recommendation algorithm supplemented by the recommendation algorithm realizes the personalized recommendation function. At the same time, in order to improve system performance and alleviate the problem of cold start, the popular recommendation algorithm is specially adopted. This algorithm extracts the data of registered users in the database, comprehensively analyzes and counts high-frequency information, and recommends it to users after screening.