Abstract: In order to solve the problem of sparse scoring data in the field of video service, the traditional collaborative filtering algorithm is usually used, but the video similarity calculation of the algorithm only uses score matrix, which results in low recommendution accuracy. In this paper, a graph based collaborative filtering algorithm is proposed for the scene of the movie in video resources. Combining the correlation between movie attributes and user preferences, the map elements of film information such as types, directors and actors are mapped, and the similarity of film resources is calculated by combining the features of graph structure. This method replaces the similarity calculation method of scoring matrix in traditional collaborative filtering algorithm, which alleviates the problem that the recommendation accuracy is affected by the sparse data. Experiment verifies the effectiveness of the proposed algorithm.

Key words: correlation analysis, collaborative filtering algorithm, graph structure, personalized recommendation