Prediction of future user activities from their history, all past activities, is a challenging problem. One reason is that the number of all potential histories grows exponentially with the length of the 4 history. Recently, deep-learning models have been proposed for solving this problem. It is easy to learn a simple predictor of future user activities, by averaging all past activities of the user and then learning an activity classifier from this average representation, for instance by logistic regression. This approach tends to have a high bias, due to using a simple feature representation and model. It is also easy to apply sequence models in deep learning to learn a predictor of future user activities. This approach tends to have a high variance, because of the large number of parameters in the neural network.
Authors
Brano Kveton
Related Content
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
Matrix completion models are among the most common formulations of recommender systems. Recent works have showed a boost of performance....
Constructing Deep Neural Networks by Bayesian Network Structure....
We introduce a principled approach for unsupervised structure learning of deep neural networks. We propose a new interpretation for depth....
Towards Understanding End-of-trip Instructions in a Taxi Ride....
We introduce a dataset containing human-authored descriptions of target locations in an "end-of-trip in a taxi ride" scenario. We describe....
Deep Learning under Privileged Information Using Heteroscedastic Dropout
Unlike machines, humans learn through rapid, abstract model-building. The role of a teacher is not simply to hammer home right....
