On China’s Administrative Enforcement Procedure

本文在分析行政强制概念和种类的基础上，阐明了行政强制程序的功能、作用以及行政强制程序的特征，研究了英国、美国、德国、日本及我国台湾地区行政强制程序的立法和实践，针对我国目前行政强制程序的现状，对如何完善我国行政强制程序和我国行政强制程序违法的法律救济进行了探讨。全文除引言和结束语外，共分为五个部分：第一章：行政强制的概念和种类。概括和总结学术界关于行政强制概念的各种观点，阐释行政强制的概念和种类。第二章：行政强制程序概述。阐明程序的含义、行政强制程序的功能、作用以及行政强制程序的特征，介绍英国、美国、德国、日本及我国台湾地区行政强制程序的立法和实践。第三章：我国行政强制程序存在的问题及成因。分...Based on analysis of the concept and categories of Administrative Enforcement, this article is to explain the functions and characteristics of Administrative Enforcement by studying the legislation and legal practice of Administrative Enforcement Procedure in the United Kingdom, the United States of America, Germany, Japan and Taiwan. Focused on the current practice of Administrative Enforcement i...学位：法律硕士院系专业：法学院法律系_法律硕士(JM)学号：X20010804

地方创业投资引导基金运作模式探讨

近年来国内外创业投资引导基金发展迅速,有力推动了企业技术创新和产业转型升级。文章对国外的创业投资运作模式及特点进行了总结,结合福建省第一支创业投资引导基金运作情况,探讨了国内地方政府在设计创业投资引导基金运作模式时应注意的问题。福建省软科学研究计划资助项目(项目号:2011R0091

Explainable Recommendation: Theory and Applications

Although personalized recommendation has been investigated for decades, the wide adoption of Latent Factor Models (LFM) has made the explainability of recommendations a critical issue to both the research community and practical application of recommender systems. For example, in many practical systems the algorithm just provides a personalized item recommendation list to the users, without persuasive personalized explanation about why such an item is recommended while another is not. Unexplainable recommendations introduce negative effects to the trustworthiness of recommender systems, and thus affect the effectiveness of recommendation engines. In this work, we investigate explainable recommendation in aspects of data explainability, model explainability, and result explainability, and the main contributions are as follows: 1. Data Explainability: We propose Localized Matrix Factorization (LMF) framework based Bordered Block Diagonal Form (BBDF) matrices, and further applied this technique for parallelized matrix factorization. 2. Model Explainability: We propose Explicit Factor Models (EFM) based on phrase-level sentiment analysis, as well as dynamic user preference modeling based on time series analysis. In this work, we extract product features and user opinions towards different features from large-scale user textual reviews based on phrase-level sentiment analysis techniques, and introduce the EFM approach for explainable model learning and recommendation. 3. Economic Explainability: We propose the Total Surplus Maximization (TSM) framework for personalized recommendation, as well as the model specification in different types of online applications. Based on basic economic concepts, we provide the definitions of utility, cost, and surplus in the application scenario of Web services, and propose the general framework of web total surplus calculation and maximization.Comment: 169 pages, in Chinese, 3 main research chapter