11,016 research outputs found
An Accuracy-Assured Privacy-Preserving Recommender System for Internet Commerce
Recommender systems, tool for predicting users' potential preferences by
computing history data and users' interests, show an increasing importance in
various Internet applications such as online shopping. As a well-known
recommendation method, neighbourhood-based collaborative filtering has
attracted considerable attention recently. The risk of revealing users' private
information during the process of filtering has attracted noticeable research
interests. Among the current solutions, the probabilistic techniques have shown
a powerful privacy preserving effect. When facing Nearest Neighbour attack,
all the existing methods provide no data utility guarantee, for the
introduction of global randomness. In this paper, to overcome the problem of
recommendation accuracy loss, we propose a novel approach, Partitioned
Probabilistic Neighbour Selection, to ensure a required prediction accuracy
while maintaining high security against NN attack. We define the sum of
neighbours' similarity as the accuracy metric alpha, the number of user
partitions, across which we select the neighbours, as the security metric
beta. We generalise the Nearest Neighbour attack to beta k Nearest
Neighbours attack. Differing from the existing approach that selects neighbours
across the entire candidate list randomly, our method selects neighbours from
each exclusive partition of size with a decreasing probability. Theoretical
and experimental analysis show that to provide an accuracy-assured
recommendation, our Partitioned Probabilistic Neighbour Selection method yields
a better trade-off between the recommendation accuracy and system security.Comment: replacement for the previous versio
Fairness of Exposure in Rankings
Rankings are ubiquitous in the online world today. As we have transitioned
from finding books in libraries to ranking products, jobs, job applicants,
opinions and potential romantic partners, there is a substantial precedent that
ranking systems have a responsibility not only to their users but also to the
items being ranked. To address these often conflicting responsibilities, we
propose a conceptual and computational framework that allows the formulation of
fairness constraints on rankings in terms of exposure allocation. As part of
this framework, we develop efficient algorithms for finding rankings that
maximize the utility for the user while provably satisfying a specifiable
notion of fairness. Since fairness goals can be application specific, we show
how a broad range of fairness constraints can be implemented using our
framework, including forms of demographic parity, disparate treatment, and
disparate impact constraints. We illustrate the effect of these constraints by
providing empirical results on two ranking problems.Comment: In Proceedings of the 24th ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining, London, UK, 201
Scalable Recommendation with Poisson Factorization
We develop a Bayesian Poisson matrix factorization model for forming
recommendations from sparse user behavior data. These data are large user/item
matrices where each user has provided feedback on only a small subset of items,
either explicitly (e.g., through star ratings) or implicitly (e.g., through
views or purchases). In contrast to traditional matrix factorization
approaches, Poisson factorization implicitly models each user's limited
attention to consume items. Moreover, because of the mathematical form of the
Poisson likelihood, the model needs only to explicitly consider the observed
entries in the matrix, leading to both scalable computation and good predictive
performance. We develop a variational inference algorithm for approximate
posterior inference that scales up to massive data sets. This is an efficient
algorithm that iterates over the observed entries and adjusts an approximate
posterior over the user/item representations. We apply our method to large
real-world user data containing users rating movies, users listening to songs,
and users reading scientific papers. In all these settings, Bayesian Poisson
factorization outperforms state-of-the-art matrix factorization methods
Creating Capsule Wardrobes from Fashion Images
We propose to automatically create capsule wardrobes. Given an inventory of
candidate garments and accessories, the algorithm must assemble a minimal set
of items that provides maximal mix-and-match outfits. We pose the task as a
subset selection problem. To permit efficient subset selection over the space
of all outfit combinations, we develop submodular objective functions capturing
the key ingredients of visual compatibility, versatility, and user-specific
preference. Since adding garments to a capsule only expands its possible
outfits, we devise an iterative approach to allow near-optimal submodular
function maximization. Finally, we present an unsupervised approach to learn
visual compatibility from "in the wild" full body outfit photos; the
compatibility metric translates well to cleaner catalog photos and improves
over existing methods. Our results on thousands of pieces from popular fashion
websites show that automatic capsule creation has potential to mimic skilled
fashionistas in assembling flexible wardrobes, while being significantly more
scalable.Comment: Accepted to CVPR 201
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