Due to the expansion of e-business, the availability of products on the internet has massively increased. Finding suitable stuff from the vast array of products available on the internet is a time-consuming task. Collaborative Filtering (CF) is the most effective recommendation method for providing users with the ability to identify relevant content and, therefore, increase engagement. However, CF has several flaws, including data sparsity and cold start problems. These are ongoing research questions that pose major hurdles to the precision of the algorithms. Therefore, in this work, a novel neural recommendation model is proposed based on non-independent and identically distributed (Non-IID) for CF by incorporating explicit and implicit coupling interaction. The explicit interactions consist of two models, namely Intra-coupling interactions within users and items, and Inter-coupling interactions between different users and items concerning the attributes of users and items. The Intra-coupled model learns using deep learning convolutional neural networks and is combined with the Inter-coupled model. Besides explicit coupling interactions, we present a Generalized Matrix Factorization Bias (GMFB) model that systematically trains the implicit user-item coupling. Finally, we combined with explicit and implicit coupling interactions within and between users and items accompanying the extra information about users and items under a framework called “IntegrateCF.” Extensive experiments on two large real-world datasets have shown that the proposed model performs better than existing methods.