Due to the fast growth of the Web and the difficulties in finding desired information, efficient and effective information retrieval systems have become more important than ever, and the search engine has become an essential tool for many people. The ranker, a central component in every search engine, is responsible for the matching between processed queries and indexed documents. Because of its central role, great attention has been paid to the research and development of ranking technologies. In addition, ranking is also pivotal for many other information retrieval applications, such as collaborative filtering, definition ranking, question answering, multimedia retrieval, text summarization, and online advertisement. Leveraging machine learning technologies in the ranking process has led to innovative and more effective ranking models, and eventually to a completely new research area called “learning to rank”. Liu first gives a comprehensive review of the major approaches to learning to rank. For each approach he presents the basic framework, with example algorithms, and he discusses its advantages and disadvantages. He continues with some recent advances in learning to rank that cannot be simply categorized into the three major approaches – these include relational ranking, query-dependent ranking, transfer ranking, and semisupervised ranking. His presentation is completed by several examples that apply these technologies to solve real information retrieval problems, and by theoretical discussions on guarantees for ranking performance. This book is written for researchers and graduate students in both information retrieval and machine learning. They will find here the only comprehensive description of the state of the art in a field that has driven the recent advances in search engine development.

The Global Academic Rankings Game provides a much-needed perspective on how countries and universities react to academic rankings. Based on a unified case methodology of eleven key countries and academic institutions, this comprehensive volume provides expert analysis on this emerging phenomenon at a time when world rankings are becoming increasingly visible and influential on the international stage. Each chapter provides an overview of government and national policies as well as an in-depth examination of the impact that rankings have played on policy, practice, and academic life in Australia, Chile, China, Germany, Malaysia, the Netherlands, Poland, Russia, Turkey, the United Kingdom, and the United States. The Global Academic Rankings Game contributes to the continuing debate about the influence of rankings in higher education and is an invaluable resource for higher education scholars and administrators as they tackle rankings in their own national and institutional contexts.

This book, written by three generations of rankings academics with considerable experience from three very different regions of the globe, lifts the lid on the real impact of higher education ranking systems (HERS) on universities and their stakeholders. It critically analyses the criteria that make up the ‘Big Three’ global ranking systems and, using interviews with senior administrators, academics and managers, discusses their impact on universities from four very different continents. Higher education continues to be dominated by a reputational hierarchy of institutions that sustains and is reinforced by HERS. Despite all the opinions and arguments about the legitimacy of the rankings as a construct, it seems experts agree that they are here to stay. The question, therefore, seems to be less about whether or not universities should be compared and ranked, but the manner in which this is undertaken. Delivering a fresh perspective on global rankings, this book summarizes the development of HERS and provides a critical evaluation of the effects of HERS on four different major regions – South Africa, the Arab region, South East Asia, and Australia. It will appeal to any academic, student, university administrator or governing body interested in or affected by global higher education ranking systems.

Directory to current state and local level statistical services in the USA for the fields of population, health, welfare, local government and the state economy - includes such areas as education, demographic data, employment, banking, insurance, public finance, commerce, infrastructure, agriculture, police and law enforcement, etc.

This book focuses on the widespread use of deep neural networks and their various techniques in session-based recommender systems (SBRS). It presents the success of using deep learning techniques in many SBRS applications from different perspectives. For this purpose, the concepts and fundamentals of SBRS are fully elaborated, and different deep learning techniques focusing on the development of SBRS are studied. The book is well-modularized, and each chapter can be read in a stand-alone manner based on individual interests and needs. In the first chapter of the book, definitions and concepts related to SBRS are reviewed, and a taxonomy of different SBRS approaches is presented, where the characteristics and applications of each class are discussed separately. The second chapter starts with the basic concepts of deep learning and the characteristics of each model. Then, each deep learning model, along with its architecture and mathematical foundations, is introduced. Next, chapter 3 analyses different approaches of deep discriminative models in session-based recommender systems. In the fourth chapter, session-based recommender systems that benefit from deep generative neural networks are discussed. Subsequently, chapter 5 discusses session-based recommender systems using advanced/hybrid deep learning models. Eventually, chapter 6 reviews different learning-to-rank methods focusing on information retrieval and recommender system domains. Finally, the results of the investigations and findings from the research review conducted throughout the book are presented in a conclusive summary. This book aims at researchers who intend to use deep learning models to solve the challenges related to SBRS. The target audience includes researchers entering the field, graduate students specializing in recommender systems, web data mining, information retrieval, or machine/deep learning, and advanced industry developers working on recommender systems.

Surveys the changing landscape of American higher education, from academic freedom to virtual universities, from campus crime to Pell Grants, from the Student Privacy Act to student diversity. In the years following World War II, college and university enrollment doubled, students revolted, faculty unionized, and community colleges evolved. Tuition and technology soared, as did the number of first-generation, minority, and women students. These changes radically transformed the American system of postsecondary education. Today, that system is in trouble. Its aging professoriate prepares for retirement, but low academic salaries can no longer attract the best minds to replace them. A flood of corporate dollars funds commercial research, but money for basic research—the seedbed of American scientific preeminence—has dried up. Colleges and universities also face heated competition with for-profit education providers for students, faculty, and external financial support, along with the costs of providing remedial education to growing numbers of students who are unprepared for postsecondary education. Higher Education in the United States provides a comprehensive analysis of these issues and others that scholars and practitioners of higher education study, discuss, and grapple with on a daily basis.

This ground-breaking and exhaustive analysis of university ranking surveys scrutinizes their theoretical bases, methodological issues, societal impact, and policy implications, providing readers with a deep understanding of these controversial comparators. The authors propose that university rankings are misused by policymakers and institutional leaders alike. They assert that these interested parties overlook the highly problematic internal logic of ranking methodologies even as they obsess over the surveys’ assessment of their status. The result is that institutions suffer from short-termism, realigning their resources to maximize their relative rankings. While rankings are widely used in policy and academic discussions, this is the first book to explore the theoretical and methodological issues of ranking itself. It is a welcome contribution to an often highly charged debate. Far from showing how to manipulate the system, this collection of work by key researchers aims to enlighten interested parties.