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Deep Neural Networks (DNN) has attained impressive results in various natural language processing tasks. It attracts the researchers to apply DNN in the Recommender Systems (RS). Typically, majority of the recommendation algorithms apply Collaborative Filtering (CF) to recommend the items of user interest. Recently, so many researchers have applied CF with deep learning for RS. But most of the recommendations exploit only on the implicit data like user clicks, page visit, item description and employs matrix factorization with an inner product to obtain the correlations. To improve the performance of the recommendation system, we propose a novel architecture named Outer Product Based Residual CNN. The proposed model utilizes an explicit user-item sparse rating matrix and outer product function to learns high-order correlations that exist between the users and items latent features. The experimental result …