A website's ranking on Google can spell the difference between success and failure for a new business. NCAA football ratings determine which schools get to play for the big money in postseason bowl games. Product ratings influence everything from the clothes we wear to the movies we select on Netflix. Ratings and rankings are everywhere, but how exactly do they work? Who's #1? offers an engaging and accessible account of how scientific rating and ranking methods are created and applied to a variety of uses. Amy Langville and Carl Meyer provide the first comprehensive overview of the mathematical algorithms and methods used to rate and rank sports teams, political candidates, products, Web pages, and more. In a series of interesting asides, Langville and Meyer provide fascinating insights into the ingenious contributions of many of the field's pioneers. They survey and compare the different methods employed today, showing why their strengths and weaknesses depend on the underlying goal, and explaining why and when a given method should be considered. Langville and Meyer also describe what can and can't be expected from the most widely used systems. The science of rating and ranking touches virtually every facet of our lives, and now you don't need to be an expert to understand how it really works. Who's #1? is the definitive introduction to the subject. It features easy-to-understand examples and interesting trivia and historical facts, and much of the required mathematics is included.

Forced ranking assesses employee performance relative to peers rather than against predetermined goals. It's a performance management tool that—when used right—has increased productivity, profitability, and shareholder value. Unfortunately, some firms have misunderstood what forced ranking is, or have implemented it poorly—resulting in confusion and controversy. In this hands-on book, renowned performance management expert Dick Grote dispels common misperceptions about forced ranking and offers a clear-headed, convincing argument for why it should be a necessary part of any robust performance appraisal system. Based on extensive research, case studies, and consulting experience, the book provides a practical framework for developing a forced-ranking system that is fair, humane, and effective. From establishing appropriate guidelines to accurately categorizing employees, to managing A, B, and C talent differently, Grote shows how managers can use this tool to identify future leaders, give honest performance feedback, and grow the talent that matters most to the firm's success. Transforming a controversial management practice into a practical and powerful leadership-development tool, Forced Ranking will help organizations and their employees reach new heights of performance success.

Human beings are competitive. We want to know who is the strongest, who is the richest, and who is the cleverest of all. Some situations, like ranking people based on height, can be ranked in objective ways. However, many "Top Ten" lists are based on subjective categorization and give only the illusion of objectivity. In fact, we don't always want to be seen objectively since we don't mind having a better image or rank than deserved. Ranking: The Unwritten Rules of the Social Game We All Play applies scientific theories to everyday experience by raising and answering questions like: Are college ranking lists objective? How do we rank and rate countries based on their fragility, level of corruption, or even happiness? How do we find the most relevant web pages? How are employees ranked? This book is for people who have a neighbor with a fancier car; employees, who are being ranked by their supervisors; managers, who are involved in ranking but may have qualms about the process; businesspeople interested in creating better visibility for their companies; scientists, writers, artists, and other competitors who would like to see themselves at the top of a success list; or college students who are just preparing to enter a new phase of social competition. Readers will engage in an intellectual adventure to better understand the difficulties of navigating between objectivity and subjectivity and to better identify and modify their place in real and virtual communities by combining human and computational intelligence.

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.

Context-aware ranking is an important task with many applications. E.g. in recommender systems items (products, movies, ...) and for search engines webpages should be ranked. In all these applications, the ranking is not global (i.e. always the same) but depends on the context. Simple examples for context are the user for recommender systems and the query for search engines. More complicated context includes time, last actions, etc. The major problem is that typically the variable domains (e.g. customers, products) are categorical and huge, the observations are very sparse and only positive events are observed. In this book, a generic method for context-aware ranking as well as its application are presented. For modelling a new factorization based on pairwise interactions is proposed and compared to other tensor factorization approaches. For learning, the `Bayesian Context-aware Ranking' framework consisting of an optimization criterion and algorithm is developed. The second main part of the book applies this general theory to the three scenarios of item, tag and sequential-set recommendation. Furthermore extensions of time-variant factors and one-class problems are studied. This book generalizes and builds on work that has received the `WWW 2010 Best Paper Award', the `WSDM 2010 Best Student Paper Award' and the `ECML/PKDD 2009 Best Discovery Challenge Award'.

Ranking queries are widely used in data exploration, data analysis and decision making scenarios. While most of the currently proposed ranking techniques focus on deterministic data, several emerging applications involve data that are imprecise or uncertain. Ranking uncertain data raises new challenges in query semantics and processing, making conventional methods inapplicable. Furthermore, the interplay between ranking and uncertainty models introduces new dimensions for ordering query results that do not exist in the traditional settings. This lecture describes new formulations and processing techniques for ranking queries on uncertain data. The formulations are based on marriage of traditional ranking semantics with possible worlds semantics under widely-adopted uncertainty models. In particular, we focus on discussing the impact of tuple-level and attribute-level uncertainty on the semantics and processing techniques of ranking queries. Under the tuple-level uncertainty model, we describe new processing techniques leveraging the capabilities of relational database systems to recognize and handle data uncertainty in score-based ranking. Under the attribute-level uncertainty model, we describe new probabilistic ranking models and a set of query evaluation algorithms, including sampling-based techniques. We also discuss supporting rank join queries on uncertain data, and we show how to extend current rank join methods to handle uncertainty in scoring attributes. Table of Contents: Introduction / Uncertainty Models / Query Semantics / Methodologies / Uncertain Rank Join / Conclusion