Perceptron Collaborative Filtering

TL;DR

This paper explores using perceptron neural networks as an alternative to logistic regression classifiers for collaborative filtering in recommender systems, emphasizing optimization techniques for improved performance.

Contribution

It introduces a neural network-based approach for collaborative filtering, highlighting the use of perceptrons with advanced optimization methods as a substitute for classical logistic classifiers.

Findings

Perceptron models can effectively predict user preferences.

Optimization techniques enhance neural network performance in recommendations.

Neural networks offer a flexible alternative to traditional classifiers.

Abstract

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.