Perceptron Collaborative Filtering
<|reference_start|>Perceptron Collaborative Filtering: 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.<|reference_end|>
@article{chakraborty2024perceptron,
    title={Perceptron Collaborative Filtering},
    author={Arya Chakraborty},
    journal={International Journal for Research in Applied Science and
  Engineering Technology, Volume 11, Issue II (2023) 437-447},
    year={2024},
    doi={10.22214/ijraset.2023.49044},
    archivePrefix={arXiv},
    eprint={2407.00067},
    primaryClass={cs.IR cs.AI cs.LG}
}