Abstract
Collaborative filtering is the mainstream approach to personalized recommendation. For the cold start problem faced in collaborative filtering, it is a hot research topic to introduce the user’s side information into the recommendation model. Different from the matrix decomposition idea adopted in the existing methods, we propose a Top-N recommendation model using the side information of the user based on the reconstruction function of the stacked denoising auto-encoder. Experimental results show that the model outperforms the existing method in Recall. In addition, we explore the influence of missing ratings and user side information vector into the loss computation. The experimental results show that ignoring the missing ratings in the loss function is beneficial to improve the performance of the model.
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