Matrix Factorization Model in Collaborative Filtering Algorithms Based on Feedback Datasets

In recent years, with the advancement of internet technology, an increasing number of applications rely on recommendation systems to provide personalized recommendations to users in order to increase profits. The recommendation system has generated significant economic benefits and has become a popular research area. Collaborative Filtering (CF) is currently the most widely used method for building recommendation systems. CF techniques use user-item ratings in the form of user behavior as a source of information for prediction. The challenges in CF are being effectively addressed by Matrix Factorization (MF) algorithms. Implicit data, which has many advantages as a more accessible type of data, is increasingly being used in recommendation systems. This paper provides a detailed introduction to the knowledge framework of the collaborative filtering algorithm based on implicit feedback, describes the model for utilizing implicit data in this algorithm, and serves as a reference for future research. It is believed that this research has significant implications for promoting the development of personalized information services.