The NY Times has an article that focuses on the company Gild and how they go headhunting among the reputational rankings on coder sites: How Big Data Is Playing Recruiter for Specialized Workers
"Gild is not the only company now scouring for information. TalentBin, another San Francisco start-up firm, searches the Internet for talented programmers, trawling sites where they gather, collecting “data exhaust,” according to the company Web site, and creating lists of potential hires for employers. Another competitor is RemarkableHire, which assesses a person’s talents by looking at how his or her online contributions are rated by others.
"And there’s Entelo, which tries to figure out who might be looking for a job before they even start their exploration. According to its Web site, the company uses more than 70 variables to find indications of possible career change, such as how someone presents herself on social sites. The Web site reads: “We crunch the data so you don’t have to.”
Manlove, D. (2013) Algorithmics Of Matching Under Preferences. Series: Theoretical Computer Science . World Scientific Publishing. ISBN 9789814425247
Publisher's URL: http://www.worldscientific.com/doi/pdf/10.1142/9789814425254_fmatter
A new book by Dr David Manlove of the School of Computing Science has recently been published by World Scientific as part of their Series on Theoretical Computer Science. This book, called “Algorithmics of Matching Under Preferences”, deals with algorithms and complexity issues surrounding the matching of agents to one another when preferences are involved. For example, in several countries, centralised matching schemes handle the annual allocation of intending junior doctors to hospitals based on their preferences over one another. Efficient algorithms required to solve the underlying theoretical matching problems. Similar examples arise in the allocation of pupils to schools, students to projects, kidney patients to donors, and so on. The book surveys algorithmic results for a range of matching problems involving preferences, with practical applications areas including those mentioned above. It covers the classical Stable Marriage, Hospitals/Residents and Stable Roommates problems, where so-called stable matchings are sought, thereby providing an update to “The Stable Marriage problem, Structure and Algorithms”, by Dan Gusfield and Rob Irving, published by MIT Press in 1989. It also extends the coverage to the House Allocation problem, where stability is no longer the key requirement for a matching, and other definitions of optimality hold. This book builds on the author’s prior research in this area, and also his practical experience of developing, with colleagues including Rob Irving and Gregg O’Malley, algorithms for matching kidney patients to donors in the UK (collaborating with NHS Blood and Transplant), for assigning medical students to hospitals in Scotland (in collaboration with NHS Education for Scotland), and for allocating students to elective courses and projects (within the Schools of Medicine and Computing Science at the University of Glasgow, respectively). The book is also timely, as the research area recently came to the forefront in 2012 following the award of the Nobel Prize in Economic Sciences to Alvin Roth and Lloyd Shapley, two leading contributors to the field of matching theory and its application in practical settings, whose work is described in detail throughout the book. A Foreword is contributed by Kurt Mehlhorn of Max-Planck Institut fur Informatik, Saarbrucken, who wrote: “This book covers the research area in its full breadth and beauty. Written by one of the foremost experts in the area, it is a timely update to “The Stable Marriage Problem: Structure and Algorithms” (D. Gusﬁeld and R.W. Irving, 1989). This book will be required reading for anybody working on the subject; it has a good chance of becoming a classic.”