  While multivariate logistic regression classifiers are a great way of
implementing collaborative filtering - a method of making automatic predictions
about the interests of a user by collecting preferences or taste information
from many other users, we can also achieve similar results using neural
networks. A recommender system is a subclass of information filtering system
that provide suggestions for items that are most pertinent to a particular
user. A perceptron or a neural network is a machine learning model designed for
fitting complex datasets using backpropagation and gradient descent. When
coupled with advanced optimization techniques, the model may prove to be a
great substitute for classical logistic classifiers. The optimizations include
feature scaling, mean normalization, regularization, hyperparameter tuning and
using stochastic/mini-batch gradient descent instead of regular gradient
descent. In this use case, we will use the perceptron in the recommender system
to fit the parameters i.e., the data from a multitude of users and use it to
predict the preference/interest of a particular user.
