3 research outputs found
Probabilistic Matrix Factorization with Personalized Differential Privacy
Probabilistic matrix factorization (PMF) plays a crucial role in
recommendation systems. It requires a large amount of user data (such as user
shopping records and movie ratings) to predict personal preferences, and
thereby provides users high-quality recommendation services, which expose the
risk of leakage of user privacy. Differential privacy, as a provable privacy
protection framework, has been applied widely to recommendation systems. It is
common that different individuals have different levels of privacy requirements
on items. However, traditional differential privacy can only provide a uniform
level of privacy protection for all users.
In this paper, we mainly propose a probabilistic matrix factorization
recommendation scheme with personalized differential privacy (PDP-PMF). It aims
to meet users' privacy requirements specified at the item-level instead of
giving the same level of privacy guarantees for all. We then develop a modified
sampling mechanism (with bounded differential privacy) for achieving PDP. We
also perform a theoretical analysis of the PDP-PMF scheme and demonstrate the
privacy of the PDP-PMF scheme. In addition, we implement the probabilistic
matrix factorization schemes both with traditional and with personalized
differential privacy (DP-PMF, PDP-PMF) and compare them through a series of
experiments. The results show that the PDP-PMF scheme performs well on
protecting the privacy of each user and its recommendation quality is much
better than the DP-PMF scheme.Comment: 24 pages, 12 figures, 4 table
Successive Point-of-Interest Recommendation with Local Differential Privacy
A point-of-interest (POI) recommendation system plays an important role in
location-based services (LBS) because it can help people to explore new
locations and promote advertisers to launch ads to target users. Exiting POI
recommendation methods need users' raw check-in data, which can raise location
privacy breaches. Even worse, several privacy-preserving recommendation systems
could not utilize the transition pattern in the human movement. To address
these problems, we propose Successive Point-of-Interest REcommendation with
Local differential privacy (SPIREL) framework. SPIREL employs two types of
sources from users' check-in history: a transition pattern between two POIs and
visiting counts of POIs. We propose a novel objective function for learning the
user-POI and POI-POI relationships simultaneously. We further propose two
privacy-preserving mechanisms to train our recommendation system. Experiments
using two public datasets demonstrate that SPIREL achieves better POI
recommendation quality while preserving stronger privacy for check-in history.Comment: 12 pages, 11 figure
Providing reliability in Recommender Systems through Bernoulli Matrix Factorization
Beyond accuracy, quality measures are gaining importance in modern
recommender systems, with reliability being one of the most important
indicators in the context of collaborative filtering. This paper proposes
Bernoulli Matrix Factorization (BeMF), which is a matrix factorization model,
to provide both prediction values and reliability values. BeMF is a very
innovative approach from several perspectives: a) it acts on model-based
collaborative filtering rather than on memory-based filtering, b) it does not
use external methods or extended architectures, such as existing solutions, to
provide reliability, c) it is based on a classification-based model instead of
traditional regression-based models, and d) matrix factorization formalism is
supported by the Bernoulli distribution to exploit the binary nature of the
designed classification model. The experimental results show that the more
reliable a prediction is, the less liable it is to be wrong: recommendation
quality improves after the most reliable predictions are selected.
State-of-the-art quality measures for reliability have been tested, which shows
that BeMF outperforms previous baseline methods and models.Comment: 28 pages, 8 figures, 8 table