  User evaluations include a significant quantity of information across online
platforms. This information source has been neglected by the majority of
existing recommendation systems, despite its potential to ease the sparsity
issue and enhance the quality of suggestions. This work presents a deep model
for concurrently learning item attributes and user behaviour from review text.
Deep Cooperative Neural Network (DeepCoNN) is the suggested model consisting of
two parallel neural networks connected in their final layers. One of the
networks focuses on learning user behaviour from reviews submitted by the user,
while the other network learns item attributes from user reviews. On top, a
shared layer is added to connect these two networks. Similar to factorization
machine approaches, the shared layer allows latent factors acquired for people
and things to interact with each other. On a number of datasets, DeepCoNN
surpasses all baseline recommendation systems, according to experimental
findings.
