Jie Lu

Recommender systems provide users (businesses or individuals) with personalized online recommendations of products or information, to address the problem of information overload and improve personalized services. Recent successful applications of recommender systems are providing solutions to transform online services for e-government, e-business, e-commerce, e-shopping, e-library, e-learning, e-tourism, and more.This unique compendium not only describes theoretical research but also reports on new application developments, prototypes, and real-world case studies of recommender systems. The comprehensive volume provides readers with a timely snapshot of how new recommendation methods and algorithms can overcome challenging issues. Furthermore, the monograph systematically presents three dimensions of recommender systems — basic recommender system concepts, advanced recommender system methods, and real-world recommender system applications.By providing state-of-the-art knowledge, this excellent reference text will immensely benefit researchers, managers, and professionals in business, government, and education to understand the concepts, methods, algorithms and application developments in recommender systems.

This monograph presents new developments in multi-level decision-making theory, technique and method in both modeling and solution issues. It especially presents how a decision support system can support managers in reaching a solution to a multi-level decision problem in practice. This monograph combines decision theories, methods, algorithms and applications effectively. It discusses in detail the models and solution algorithms of each issue of bi-level and tri-level decision-making, such as multi-leaders, multi-followers, multi-objectives, rule-set-based, and fuzzy parameters.Potential readers include organizational managers and practicing professionals, who can use the methods and software provided to solve their real decision problems; PhD students and researchers in the areas of bi-level and multi-level decision-making and decision support systems; students at an advanced undergraduate, master’s level in information systems, business administration, or the application of computer science.

This monograph presents new developments in multi-level decision-making theory, technique and method in both modeling and solution issues. It especially presents how a decision support system can support managers in reaching a solution to a multi-level decision problem in practice. This monograph combines decision theories, methods, algorithms and applications effectively. It discusses in detail the models and solution algorithms of each issue of bi-level and tri-level decision-making, such as multi-leaders, multi-followers, multi-objectives, rule-set-based, and fuzzy parameters.Potential readers include organizational managers and practicing professionals, who can use the methods and software provided to solve their real decision problems; PhD students and researchers in the areas of bi-level and multi-level decision-making and decision support systems; students at an advanced undergraduate, master’s level in information systems, business administration, or the application of computer science.

This book proposes a set of models to describe fuzzy multi-objective decision making (MODM), fuzzy multi-criteria decision making (MCDM), fuzzy group decision making (GDM) and fuzzy multi-objective group decision-making problems, respectively. It also gives a set of related methods (including algorithms) to solve these problems. One distinguishing feature of this book is that it provides two decision support systems software for readers to apply these proposed methods. A set of real-world applications and some new directions in this area are then described to further instruct readers how to use these methods and software in their practice.

The rise of authoritarian populism and declining popular confidence in democratic institutions has, of late, prompted many declarations that democracy is in crisis. Nevertheless, democracy still enjoys its supremacy in contemporary political discourse, with limited meaningful alternatives. Few of today's authoritarian leaders publicly denounce democracy; instead, they are more inclined to present their regimes as democracies. Furthermore, public opinion surveys show that ordinary citizens still widely and deeply appreciate democracy, even in societies with limited practice of democracy. Democracy is still the "only game" in contemporary political discourse. Given the popularity of democracy, the hotly debated and discussed crisis of democracy is puzzling. If most people love democracy and politicians have to live with democracy (whether they like it or not), how can democracy be in trouble? More specifically, if people love democracy, shouldn't they despise authoritarian leaders and regimes, or even join the advocates of democracy to rebel against authoritarianism? To address these questions, Understandings of Democracy looks at both the practices of political elites and key dynamics that drive mass attitudes and behaviors. Jie Lu and Yun-han Chu argue that mass attitudes and behaviors are greatly affected by how people understand democracy. More specifically, they make the following arguments: people hold distinct understandings of democracy; popular conceptions of democracy are significantly shaped by surrounding socioeconomic and political contexts; such varying conceptions generate different baselines for people to assess democratic practices and to establish their views of democracy; and such distinct conceptions also drive political participation in different ways. Overall, popular understandings of democracy shape how citizens respond to authoritarian or populist practices and offer significant explanatory power for understanding why democracy is in trouble in today's world, even when most people profess to love democracy. Using large-scale comparative surveys and survey experiments from seventy-two societies and a national survey in the United States, Understandings of Democracy captures how people respond when presented with the tradeoffs between the intrinsic and instrumental values of democracy, as well as the attitudinal and behavioral implications of such responses.

This book presents innovative theories, methodologies, and techniques in the field of risk management and decision making. It introduces new research developments and provides a comprehensive image of their potential applications to readers interested in the area. The collection includes: computational intelligence applications in decision making, multi-criteria decision making under risk, risk modelling,forecasting and evaluation, public security and community safety, risk management in supply chain and other business decision making, political risk management and disaster response systems.The book is directed to academic and applied researchers working on risk management, decision making, and management information systems.

It is well understood that "good institutions" are essential for good governance. But even institutions that follow similar designs vary significantly with regard to performance across countries and even across regions within the same country. Following China's abolishment of the Commune system to accommodate market-oriented reforms in the 1980s, decentralized, grassroots democracy was introduced in rural China in order to improve the quality of local governance. In this book, Jie Lu looks at variance among local governance institutions in China to examine under what conditions indigenously cultivated institutions are able to succeed, particularly under pressures of economic modernization. Lu argues that any governance institution can perform effectively as long as it can produce collective action and accountability, but that the relative effectiveness of institutions is contingent upon the social environment in which they are embedded. When economic conditions prompt outward migration, social environments are reshaped such that rules-based national institutions will trump indigenous forms. In identifying the optimal social conditions for the good performance of different governance institutions and theorizing the effects of social change on these institutions, Lu deepens understanding of how institutions, particularly in developing countries, change, and under what conditions institutional modernization or engineering may succeed or fail. Varieties of Governance in China is the first book to use a coherent framework to simultaneously examine various aspects of rural China's governance-including public goods provision, conflict resolution, disaster and crisis relief, and raising modest credit and small loans-covering both formal and informal institutions. It is also the first book to systematically examine how community structural transformation, primarily driven by rural-urban migration, affects the performance and change of institutions in rural China, as well as their implications for Chinese villages' decentralized governance.

Cognition-driven decision support system (DSS) has been recognized as a paradigm in the research and development of business intelligence (BI). Cognitive decision support aims to help managers in their decision making from human cognitive aspects, such as thinking, sensing, understanding and predicting, and fully reuse their experience. Among these cognitive aspects, decision makers’ situation awareness (SA) and mental models are considered to be two important prerequisites for decision making, particularly in ill-structured and dynamic decision situations with uncertainties, time pressure and high personal stake. In today’s business domain, decision making is becoming increasingly complex. To make a successful decision, managers’ SA about their business environments becomes a critical factor. This book presents theoretical models as well practical techniques of cognitiondriven DSS. It first introduces some important concepts of cognition orientation in decision making process and some techniques in related research areas including DSS, data warehouse and BI, offering readers a preliminary for moving forward in this book. It then proposes a cognition-driven decision process (CDDP) model which incorporates SA and experience (mental models) as its central components. The goal of the CDDP model is to facilitate cognitive decision support to managers on the basis of BI systems. It also presents relevant techniques developed to support the implementation of the CDDP model in a BI environment. Key issues addressed of a typical business decision cycle in the CDDP model include: natural language interface for a manager’s SA input, extraction of SA semantics, construction of data warehouse queries based on the manger’s SA and experience, situation information retrieval from data warehouse, how the manager perceives situation information and update SA, how the manager’s SA leads to a final decision. Finally, a cognition-driven DSS, FACETS, and twoillustrative applications of this system are discussed.

This book presents innovative theories, methodologies, and techniques in the field of risk management and decision making. It introduces new research developments and provides a comprehensive image of their potential applications to readers interested in the area. The collection includes: computational intelligence applications in decision making, multi-criteria decision making under risk, risk modelling,forecasting and evaluation, public security and community safety, risk management in supply chain and other business decision making, political risk management and disaster response systems.The book is directed to academic and applied researchers working on risk management, decision making, and management information systems.

Cognition-driven decision support system (DSS) has been recognized as a paradigm in the research and development of business intelligence (BI). Cognitive decision support aims to help managers in their decision making from human cognitive aspects, such as thinking, sensing, understanding and predicting, and fully reuse their experience. Among these cognitive aspects, decision makers’ situation awareness (SA) and mental models are considered to be two important prerequisites for decision making, particularly in ill-structured and dynamic decision situations with uncertainties, time pressure and high personal stake. In today’s business domain, decision making is becoming increasingly complex. To make a successful decision, managers’ SA about their business environments becomes a critical factor. This book presents theoretical models as well practical techniques of cognitiondriven DSS. It first introduces some important concepts of cognition orientation in decision making process and some techniques in related research areas including DSS, data warehouse and BI, offering readers a preliminary for moving forward in this book. It then proposes a cognition-driven decision process (CDDP) model which incorporates SA and experience (mental models) as its central components. The goal of the CDDP model is to facilitate cognitive decision support to managers on the basis of BI systems. It also presents relevant techniques developed to support the implementation of the CDDP model in a BI environment. Key issues addressed of a typical business decision cycle in the CDDP model include: natural language interface for a manager’s SA input, extraction of SA semantics, construction of data warehouse queries based on the manger’s SA and experience, situation information retrieval from data warehouse, how the manager perceives situation information and update SA, how the manager’s SA leads to a final decision. Finally, a cognition-driven DSS, FACETS, and twoillustrative applications of this system are discussed.