Deep auto encoder model with convolutional text networks for video recommendation

Abstract

Collaborative filtering (CF) approach has been successfully used in recommender system (RS). Sparsity and cold start are two common phenomena in the CF algorithms nearly for each data set. Hence, these drawbacks of the classical CF algorithms have limited the recommendation performance. Deep learning theory is a very useful tool to mine the latent features in many scientific areas, such as image processing, video processing, and signal processing. In this paper, a novel deep learning-based recommendation model is introduced to solve the sparsity and cold start recommendation problems by mining the auxiliary data of users' viewing behavior datasets (e.g., the user attribute features information and video item attribute features information) and to deeply mine the latent information and their correlations of the user features and item features. First of all, the user features and video item features are processed and deeply mined by the data preprocessing layer, embedding dense layer, convolution network layer, share layer, and the auto encoder layer of our proposed model. After that, the final predictive rating process is conducted in multi-layer perception by combining with the target rating vector data and the processed user and item feature data, which is deeply mined by the above-mentioned submodels of our proposed algorithm model. The extensive experiments have shown the benefits of the proposed algorithm in the measure of mean absolute error (MAE) and root mean square error (RMSE) compared with the state-of-the-art algorithms. Besides, the impact of choices of different components and parameters of our proposed algorithm model is also studied thoroughly.