3,430 research outputs found
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
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
PrivateJobMatch: A Privacy-Oriented Deferred Multi-Match Recommender System for Stable Employment
Coordination failure reduces match quality among employers and candidates in
the job market, resulting in a large number of unfilled positions and/or
unstable, short-term employment. Centralized job search engines provide a
platform that connects directly employers with job-seekers. However, they
require users to disclose a significant amount of personal data, i.e., build a
user profile, in order to provide meaningful recommendations. In this paper, we
present PrivateJobMatch -- a privacy-oriented deferred multi-match recommender
system -- which generates stable pairings while requiring users to provide only
a partial ranking of their preferences. PrivateJobMatch explores a series of
adaptations of the game-theoretic Gale-Shapley deferred-acceptance algorithm
which combine the flexibility of decentralized markets with the intelligence of
centralized matching. We identify the shortcomings of the original algorithm
when applied to a job market and propose novel solutions that rely on machine
learning techniques. Experimental results on real and synthetic data confirm
the benefits of the proposed algorithms across several quality measures. Over
the past year, we have implemented a PrivateJobMatch prototype and deployed it
in an active job market economy. Using the gathered real-user preference data,
we find that the match-recommendations are superior to a typical decentralized
job market---while requiring only a partial ranking of the user preferences.Comment: 45 pages, 28 figures, RecSys 201
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