6,814 research outputs found
Hybrid Collaborative Filtering with Autoencoders
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
Solving Inverse Problems with Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity
A general framework for solving image inverse problems is introduced in this
paper. The approach is based on Gaussian mixture models, estimated via a
computationally efficient MAP-EM algorithm. A dual mathematical interpretation
of the proposed framework with structured sparse estimation is described, which
shows that the resulting piecewise linear estimate stabilizes the estimation
when compared to traditional sparse inverse problem techniques. This
interpretation also suggests an effective dictionary motivated initialization
for the MAP-EM algorithm. We demonstrate that in a number of image inverse
problems, including inpainting, zooming, and deblurring, the same algorithm
produces either equal, often significantly better, or very small margin worse
results than the best published ones, at a lower computational cost.Comment: 30 page
AUC Optimisation and Collaborative Filtering
In recommendation systems, one is interested in the ranking of the predicted
items as opposed to other losses such as the mean squared error. Although a
variety of ways to evaluate rankings exist in the literature, here we focus on
the Area Under the ROC Curve (AUC) as it widely used and has a strong
theoretical underpinning. In practical recommendation, only items at the top of
the ranked list are presented to the users. With this in mind, we propose a
class of objective functions over matrix factorisations which primarily
represent a smooth surrogate for the real AUC, and in a special case we show
how to prioritise the top of the list. The objectives are differentiable and
optimised through a carefully designed stochastic gradient-descent-based
algorithm which scales linearly with the size of the data. In the special case
of square loss we show how to improve computational complexity by leveraging
previously computed measures. To understand theoretically the underlying matrix
factorisation approaches we study both the consistency of the loss functions
with respect to AUC, and generalisation using Rademacher theory. The resulting
generalisation analysis gives strong motivation for the optimisation under
study. Finally, we provide computation results as to the efficacy of the
proposed method using synthetic and real data
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