Research on oil painting recommendation algorithm based on collaborative filtering
Shutong Wang, Songjie Gong · 2023
The traditional oil painting search method is to use search engines like Baidu and Google to find useful oil painting information based on the oil painting keywords entered by users, such as oil painting name, author and other information. As the data volume of oil painting works is increasing, it is increasingly difficult for people to find information that they are interested in and useful to themselves. On the one hand, the keywords of oil paintings entered by users into search engines may be inaccurate or too long; On the other hand, because of the differences in the information content of oil paintings, some oil paintings cannot be accurately searched by search engines, resulting in the flooding of oil paintings that users are interested in. With the rapid increase of the amount of data shared on the Internet, it is more important to use human feedback when retrieving information. Collaborative filtering of information is a promising method for retrieving useful information. Since a single system that provides the best performance for each type of object is unlikely, or at least may be too complex in terms of implementation, it is more practical to use multiple systems, each of which handles a specific goal. This design is to design an oil painting recommendation algorithm based on collaborative filtering based on the research of collaborative filtering algorithm. This algorithm implements user based collaborative filtering algorithm and project scoring prediction based collaborative filtering algorithm, and can show users the results of personalized recommendations.