Literature Review on Deep Learning for Recommendation Systems

Literature review

LITERATURE REVIEW
According to the viewpoint of Le, (2019), Deep learning is the model that represent the
nonlinear interactions in the data with nonlinear activations. Machine learning is the method for
the Analysis of data that automates analytical model building. It is a suitable branch of artificial
intelligence is based on suitable Idea and system that can learn from the data identify pattern and
leads to make decisions with the minimal human invention. Machine learning is the pattern and
the theory that computer can learn without being programmed for the particular task and the
researchers are also interested in the artificial intelligence wanted to see the computers that can
learn from the data.
It leads to make things possible to capture Complex and Critical user item interaction
pattern. In regard of this the conventional method like Matrix factorization machines are
important in the linear models (Steiner and et. al., 2019). It is well developed neural network it is
able to approximate any continuous function with the arbitrary precision that vary according to
the choices and combinations. In relation with is the deep learning is considered as the efficient
learning underline the suitable factors and useful to presentations from the input data.
Making proper use of this information tends to provide an effective way to advance the
understanding of the users and items. It is considered as the natural choice in order to apply the
dip neural networks regarding the learning for the recommended models. The benefits of
considering deep neural network are helpful in representing the learning considering the
reduction of the efforts for the hand craft feature design and also enable recommendation model
(Razzak, Naz and Zaib, 2018).
Along with this considering the growth for the volume of data available to the power
recommender system helps in providing speed to the data that increasing turning from more
traditional machine learning methods to the highly expressive deep learning models in order to
enhance the quality of the recommendations.
Apart from this the life cycle of deep learning for recommendation is classified into two
phases including training and inference. Considering the training factor this model is trained in
order to analyse user-item interaction probability by presenting it with the examples among the
users and items from the past (Agarap, 2018). The deep learning recommender model helps in
developing the existing techniques like factorization to the model with interactions among
several embedding and variables in order to handle categorised variables.