Bilian Chen

TL;DR: A new hybrid approach to solve the portfolio selection problem with skewness and kurtosis is proposed, which includes not only the multi-objective optimization but also the data-driven asset selection and return prediction, where the techniques of two-stage clustering, radial basis function neural network and genetic algorithm are employed.

TL;DR: A novel algorithm, called modularity optimization with k-plexes (MOKP), is proposed, which can identify communities smaller than a scale and can effectively detect small communities in terms of a newly defined index, namely small community level, on multiple networks as well.

TL;DR: This article proposes a geographical location privacy-preserving algorithm (GLP) that achieves 〈 r, h 〉 -privacy and presents a friend relationship Privacy Preserving Algorithm (FRP) through adding Laplacian distributed noise for fusing the user trusts and demonstrates a good trade-off between privacy and accuracy of the proposed recommendation system.

TL;DR: The specialty of the model is that the ranks of nonnegative Tucker decomposition are no longer constants, while they all become a part of the decisions to be optimized for regularized multiconvex optimization.

TL;DR: A new social tag expansion model (STEM) to generate a dynamic user profile to improve the recommendation performance and has consistently outperformed state-of-art tag-aware recommendation methods in these extensive experiments.