Research on food short video recommendation based on collaborative filtering algorithm

In response to the demand for personalized recommendation of food videos, this paper proposes a food video recommendation algorithm based on the collaborative filtering algorithm. By analyzing user behavior data and video content features, a user-video rating matrix is constructed, and an improved collaborative filtering algorithm is used for similarity calculation and recommendation generation. Experimental results show that the proposed algorithm outperforms traditional methods in terms of recommendation accuracy and user satisfaction, providing an effective solution for personalized recommendation on food video platforms.