  Traditional Collaborative Filtering (CF) based methods are applied to
understand the personal preferences of users/customers for items or products
from the rating matrix. Usually, the rating matrix is sparse in nature. So
there are some improved variants of the CF method that apply the increasing
amount of side information to handle the sparsity problem. Only linear kernel
or only non-linear kernel is applied in most of the available
recommendation-related work to understand user-item latent feature embeddings
from data. Only linear kernel or only non-linear kernel is not sufficient to
learn complex user-item features from side information of users. Recently, some
researchers have focused on hybrid models that learn some features with
non-linear kernels and some other features with linear kernels. But it is very
difficult to understand which features can be learned accurately with linear
kernels or with non-linear kernels. To overcome this problem, we propose a
novel deep fusion model named FusionDeepMF and the novel attempts of this model
are i) learning user-item rating matrix and side information through linear and
non-linear kernel simultaneously, ii) application of a tuning parameter
determining the trade-off between the dual embeddings that are generated from
linear and non-linear kernels. Extensive experiments on online review datasets
establish that FusionDeepMF can be remarkably futuristic compared to other
baseline approaches. Empirical evidence also shows that FusionDeepMF achieves
better performances compared to the linear kernels of Matrix Factorization (MF)
and the non-linear kernels of Multi-layer Perceptron (MLP).
