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Recommendation techniques are widely used in many areas to deal with the information overload problem. However, the recommendation theory has suffered from the sparseness problem, which decreases the precision of the recommendation algorithms heavily. Deep learning theory has proven to be a very efficient tool to mine the latent information of data. In this paper, a novel scalable multi-channel and fusion encoding strategy-based auto encoder (MCFE-AE) model is introduced to make recommendations by deeply mining the latent features of users and video items of the data. The detail of the proposed algorithm is summarized as follows. First, the rating data that represent the users’ preference are sent to the input port of the proposed MCFE-AE model as raw input data. Second, the latent features of users and items have been deeply mined by the multi-channel and fusion encoding process of the proposed MCFE-AE model. Third, the final rating prediction result has been obtained by the decoding process of the proposed MCFE-AE model. The extensive experiments have shown the benefits of the proposed algorithm on the measure of mean absolute error (MAE) and root mean square error (RMSE) compared with the state-of-the-art algorithms. Besides, the number of channels of the MCFE-AE, the <inline-formula> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula> regularization method, the learning rate, and the important regularization parameter <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> have been studied thoroughly in this paper.

Multi-Channel and Fusion Encoding Strategy Based Auto Encoder Model for Video Recommendation