29,813 research outputs found
Dynamic Matrix Factorization with Priors on Unknown Values
Advanced and effective collaborative filtering methods based on explicit
feedback assume that unknown ratings do not follow the same model as the
observed ones (\emph{not missing at random}). In this work, we build on this
assumption, and introduce a novel dynamic matrix factorization framework that
allows to set an explicit prior on unknown values. When new ratings, users, or
items enter the system, we can update the factorization in time independent of
the size of data (number of users, items and ratings). Hence, we can quickly
recommend items even to very recent users. We test our methods on three large
datasets, including two very sparse ones, in static and dynamic conditions. In
each case, we outrank state-of-the-art matrix factorization methods that do not
use a prior on unknown ratings.Comment: in the Proceedings of 21st ACM SIGKDD Conference on Knowledge
Discovery and Data Mining 201
Chiron: A Robust Recommendation System with Graph Regularizer
Recommendation systems have been widely used by commercial service providers
for giving suggestions to users. Collaborative filtering (CF) systems, one of
the most popular recommendation systems, utilize the history of behaviors of
the aggregate user-base to provide individual recommendations and are effective
when almost all users faithfully express their opinions. However, they are
vulnerable to malicious users biasing their inputs in order to change the
overall ratings of a specific group of items. CF systems largely fall into two
categories - neighborhood-based and (matrix) factorization-based - and the
presence of adversarial input can influence recommendations in both categories,
leading to instabilities in estimation and prediction. Although the robustness
of different collaborative filtering algorithms has been extensively studied,
designing an efficient system that is immune to manipulation remains a
significant challenge. In this work we propose a novel "hybrid" recommendation
system with an adaptive graph-based user/item similarity-regularization -
"Chiron". Chiron ties the performance benefits of dimensionality reduction
(through factorization) with the advantage of neighborhood clustering (through
regularization). We demonstrate, using extensive comparative experiments, that
Chiron is resistant to manipulation by large and lethal attacks
How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility
Recommendation systems are ubiquitous and impact many domains; they have the
potential to influence product consumption, individuals' perceptions of the
world, and life-altering decisions. These systems are often evaluated or
trained with data from users already exposed to algorithmic recommendations;
this creates a pernicious feedback loop. Using simulations, we demonstrate how
using data confounded in this way homogenizes user behavior without increasing
utility
Stability of matrix factorization for collaborative filtering
We study the stability vis a vis adversarial noise of matrix factorization
algorithm for matrix completion. In particular, our results include: (I) we
bound the gap between the solution matrix of the factorization method and the
ground truth in terms of root mean square error; (II) we treat the matrix
factorization as a subspace fitting problem and analyze the difference between
the solution subspace and the ground truth; (III) we analyze the prediction
error of individual users based on the subspace stability. We apply these
results to the problem of collaborative filtering under manipulator attack,
which leads to useful insights and guidelines for collaborative filtering
system design.Comment: ICML201
Hierarchical Compound Poisson Factorization
Non-negative matrix factorization models based on a hierarchical
Gamma-Poisson structure capture user and item behavior effectively in extremely
sparse data sets, making them the ideal choice for collaborative filtering
applications. Hierarchical Poisson factorization (HPF) in particular has proved
successful for scalable recommendation systems with extreme sparsity. HPF,
however, suffers from a tight coupling of sparsity model (absence of a rating)
and response model (the value of the rating), which limits the expressiveness
of the latter. Here, we introduce hierarchical compound Poisson factorization
(HCPF) that has the favorable Gamma-Poisson structure and scalability of HPF to
high-dimensional extremely sparse matrices. More importantly, HCPF decouples
the sparsity model from the response model, allowing us to choose the most
suitable distribution for the response. HCPF can capture binary, non-negative
discrete, non-negative continuous, and zero-inflated continuous responses. We
compare HCPF with HPF on nine discrete and three continuous data sets and
conclude that HCPF captures the relationship between sparsity and response
better than HPF.Comment: Will appear on Proceedings of the 33 rd International Conference on
Machine Learning, New York, NY, USA, 2016. JMLR: W&CP volume 4
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