46,976 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
A Faster Algorithm to Build New Users Similarity List in Neighbourhood-based Collaborative Filtering
Neighbourhood-based Collaborative Filtering (CF) has been applied in the
industry for several decades, because of the easy implementation and high
recommendation accuracy. As the core of neighbourhood-based CF, the task of
dynamically maintaining users' similarity list is challenged by cold-start
problem and scalability problem. Recently, several methods are presented on
solving the two problems. However, these methods applied an algorithm
to compute the similarity list in a special case, where the new users, with
enough recommendation data, have the same rating list. To address the problem
of large computational cost caused by the special case, we design a faster
() algorithm, TwinSearch Algorithm, to avoid computing and
sorting the similarity list for the new users repeatedly to save the
computational resources. Both theoretical and experimental results show that
the TwinSearch Algorithm achieves better running time than the traditional
method
Compositional coding capsule network with k-means routing for text classification
Text classification is a challenging problem which aims to identify the
category of texts. Recently, Capsule Networks (CapsNets) are proposed for image
classification. It has been shown that CapsNets have several advantages over
Convolutional Neural Networks (CNNs), while, their validity in the domain of
text has less been explored. An effective method named deep compositional code
learning has been proposed lately. This method can save many parameters about
word embeddings without any significant sacrifices in performance. In this
paper, we introduce the Compositional Coding (CC) mechanism between capsules,
and we propose a new routing algorithm, which is based on k-means clustering
theory. Experiments conducted on eight challenging text classification datasets
show the proposed method achieves competitive accuracy compared to the
state-of-the-art approach with significantly fewer parameters
An operator splitting scheme for the fractional kinetic Fokker-Planck equation
In this paper, we develop an operator splitting scheme for the fractional
kinetic Fokker-Planck equation (FKFPE). The scheme consists of two phases: a
fractional diffusion phase and a kinetic transport phase. The first phase is
solved exactly using the convolution operator while the second one is solved
approximately using a variational scheme that minimizes an energy functional
with respect to a certain Kantorovich optimal transport cost functional. We
prove the convergence of the scheme to a weak solution to FKFPE. As a
by-product of our analysis, we also establish a variational formulation for a
kinetic transport equation that is relevant in the second phase. Finally, we
discuss some extensions of our analysis to more complex systems
When Globalization Meets Urbanization: Labor Market Reform, Income Inequality, and Economic Growth in the People's Republic of China
The development path that the People's Republic of China (PRC) has been following during the past thirty years has led to both internal and external economic imbalances, and is now greatly challenged by the global crisis. This unbalanced growth path was primarily a result of the PRC's labor market reform which took the years of the mid-1990s as its turning point. Before the mid-1990s, the scale of rural-to-urban migration was limited, but it has grown dramatically since then. 1996 also saw drastic employment restructuring in urban areas of the PRC. Labor market reform, accompanied by the foreign exchange system reform in 1994, confirmed the PRC's comparative advantage of low labor cost, and therefore further increased the PRC's reliance on exports. However, the increased income disparity that resulted from the labor market reform may jeopardize sustainable growth if no adjustment is made. To sustain the high economic growth, especially in face of the current crisis, the PRC needs to adjust its reform and development strategies to promote income equality.china labor market unemployment; china income inequality; china economic growth crisis
A Nonconvex Splitting Method for Symmetric Nonnegative Matrix Factorization: Convergence Analysis and Optimality
Symmetric nonnegative matrix factorization (SymNMF) has important
applications in data analytics problems such as document clustering, community
detection and image segmentation. In this paper, we propose a novel nonconvex
variable splitting method for solving SymNMF. The proposed algorithm is
guaranteed to converge to the set of Karush-Kuhn-Tucker (KKT) points of the
nonconvex SymNMF problem. Furthermore, it achieves a global sublinear
convergence rate. We also show that the algorithm can be efficiently
implemented in parallel. Further, sufficient conditions are provided which
guarantee the global and local optimality of the obtained solutions. Extensive
numerical results performed on both synthetic and real data sets suggest that
the proposed algorithm converges quickly to a local minimum solution.Comment: IEEE Transactions on Signal Processing (to appear
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