Item-based collaborative filtering (i.e., ICF) technique has been widely recruited to make service recommendations in the big data environment. However, the ICF technique only performs well when the data for service recommendation decision-making are stored in a physically centralized manner, while they often fail to recommend appropriate services to a target user in the distributed environment where the involved multiple parties are reluctant to release their data to each other due to privacy concerns. Considering this drawback, we improve the traditional ICF approach by integrating the locality-sensitive hashing (LSH) technique, to realize secure and reliable data publishing. Furthermore, through integrating the published data with little privacy across different platforms, appropriate services are recommended based on our suggested recommendation approach named ICF LSH . At last, simulated experiments are conducted on WS-DREAM data set. Experiment results show that ICF LSH performs better than the competitive approaches in terms of service recommendation accuracy, efficiency, and the capability of privacy-preservation.

Privacy-Aware Data Publishing and Integration for Collaborative Service Recommendation

Large and moderate deviations for a renewal randomly indexed branching process

Large deviations for a Poisson random indexed branching process

This paper considers the non negative integer-valued autoregressive process with order one  (INAR(1)), where the autoregression parameter is close to unity Using the methods introduced by Yu, Wang, and Chen (2016), the large and moderate deviations with explicit rate functions for the total population of this process can be obtained

Large and moderate deviations for the total population in the nearly unstable INAR(1) model

In this paper, we consider the moderate deviation of the nearly unstable sub-critical Galton-Waston process with immigration for the centered total population arising. We discuss the main influence...

Moderate deviations for the total population arising from a nearly unstable sub-critical Galton-Watson process with immigration

Large and moderate deviations for a class of renewal random indexed branching process

Diffusion is a commonly used technique for spreading information from point to point on a graph. The rationale behind diffusion is not clear. And the multi-types Galton-Watson forest is a random model of population growth without space or any other resource constraints. In this paper, we use the degenerated multi-types Galton-Watson forest (MGWF) to interpret the diffusion process and establish an equivalent relationship between them. With the two-phase setting of the MGWF, one can interpret the diffusion process and the Google PageRank system explicitly. It also improves the convergence behavior of the iterative diffusion process and Google PageRank system. We validate the proposal by experiment while providing new research directions.

Zhenlong Gao

Papers

Large and moderate deviations for a class of renewal random indexed branching process

Large and moderate deviations for a renewal randomly indexed branching process

Large deviations for a Poisson random indexed branching process

A Novel Exploration of Diffusion Process based on Multi-types Galton-Watson Forests