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For the traditional collaborative filtering algorithm, only the scoring information cannot reflect the user's preference, which leads to low recommendation accuracy and easy over-fitting. This paper proposes an improved basic VAE model for film and television programs, adding auxiliary information as a priori of hidden variables. Distribution, will be the first method to use the heterogeneous auxiliary information in the VAE for a priori recommendation; at the same time, integrate multimedia information, add features such as pictures and texts, enrich the hidden variable space, and improve the recommendation effect; finally, open the data set. Experimental tests were performed on MovieLens. Compared with the PMF benchmark model, the algorithm is significantly better than the above method in the root mean square error index.

Collaborative Recommendation based on Variational Automatic Coding Machine