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Hybrid Collaborative Filtering with Neural Networks

Florian Strub 1, 2 Jérémie Mary 2, 1 Romaric Gaudel 3
1 SEQUEL - Sequential Learning
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : 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.
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Contributor : Florian Strub <>
Submitted on : Wednesday, March 2, 2016 - 6:25:44 PM
Last modification on : Tuesday, September 29, 2020 - 12:24:08 PM
Long-term archiving on: : Friday, June 3, 2016 - 11:43:13 AM


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  • HAL Id : hal-01281794, version 1
  • ARXIV : 1603.00806


Florian Strub, Jérémie Mary, Romaric Gaudel. Hybrid Collaborative Filtering with Neural Networks. 2016. ⟨hal-01281794v1⟩



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