Evolving Deep Neural Networks for Collaborative Filtering

Evolving Deep Neural Networks

Œe success of deep learning depends on €nding an architecture to €t the task. As deep learning has scaled up to more challenging tasks, the architectures have become dicult to design by hand. Œis paper proposes an automated method, CoDeepNEAT, for optimizing deep learning architectures through evolution. By extending existing neuroevolution methods to topology, components, and hyperparameters,...

Cooperative Co-Evolving Neural Networks for Robosoccer Simulation

Evolving Neural Networks with Collaborative Species

We present a coevolutionary architecture for solving decomposable problems and apply it to the evolution of artificial neural networks. Although this work is preliminary in nature it has a number of advantages over non-coevolutionary approaches. The coevolutionary approach utilizes a divide-and-conquer technique in which species representing simpler subtasks are evolved in separate instances of...

Hybrid Collaborative Filtering with Neural Networks

Collaborative Filtering aims at exploiting the feedback of users to provide personalised recommendations. Such algorithms look for latent variables in a large sparse matrix of ratings. They can be enhanced by adding side information to tackle the well-known cold start problem. While Neural Networks have tremendous success in image and speech recognition, they have received less attention in Col...

Collaborative Filtering with Recurrent Neural Networks

We show that collaborative filtering can be viewed as a sequence prediction problem, and that given this interpretation, recurrent neural networks offer very competitive approach. In particular we study how the long short-term memory (LSTM) can be applied to collaborative filtering, and how it compares to standard nearest neighbors and matrix factorization methods on movie recommendation. We sh...