标题： 感知机协同过滤 显示英文标题

标题： Perceptron Collaborative Filtering

摘要： 虽然多变量逻辑回归分类器是实现协同过滤的一种好方法——一种通过从许多其他用户那里收集偏好或口味信息来自动预测用户兴趣的方法，但我们也可以使用神经网络达到类似的结果。 推荐系统是信息过滤系统的一个子类，它为特定用户提供建议最相关的项目。 感知机或神经网络是一种机器学习模型，旨在使用反向传播和梯度下降来拟合复杂的数据集。 当与先进的优化技术结合时，该模型可能证明是经典逻辑分类器的优秀替代品。 优化包括特征缩放、均值归一化、正则化、超参数调优以及使用随机/小批量梯度下降代替常规梯度下降。 在本用例中，我们将使用推荐系统中的感知机来拟合参数，即来自众多用户的数据，并用它来预测特定用户的偏好/兴趣。 显示英文摘要

摘要： 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.