Hybrid Collaborative Filtering with Autoencoders
This work addresses the cold start problem in recommendation systems for users and platforms, though it is incremental as it builds on existing neural network and matrix factorization approaches.
The paper tackles the problem of personalized recommendations in collaborative filtering by introducing a neural network architecture (CFN) that computes non-linear matrix factorization from sparse ratings and side information, achieving state-of-the-art performance on MovieLens and Douban datasets.
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 Neu-ral Networks have tremendous success in image and speech recognition, they have received less attention in Collaborative Filtering. This is all the more surprising that Neural Networks are able to discover latent variables in large and heterogeneous datasets. In this paper, we introduce a Collaborative Filtering Neural network architecture aka CFN which computes a non-linear Matrix Factorization from sparse rating inputs and side information. We show experimentally on the MovieLens and Douban dataset that CFN outper-forms the state of the art and benefits from side information. We provide an implementation of the algorithm as a reusable plugin for Torch, a popular Neural Network framework.