Enterprise search: challenges, trends and solutions
With the growing amount of information leading to increased scale and complexity of intranets, Enterprise Search is becoming an extremely important Information Retrieval application. It is estimated that Enterprise Search industry is growing at 15-20% per year, and will continue to grow. Despite its increasing practical importance, until now Enterprise Search problems have received relatively little attention from the information retrieval community. While these challenges are not completely different from those that the web search community has faced for years, advanced web search solutions are often unable to address them properly. For example, many problems at enterprise scale cannot be solved by just throwing data at them, as long as enterprises are very diverse and generally are not that keen on sharing their data.
In this half-day tutorial we will give a research prospective on distinctive features of Enterprise Search, explain typical search scenarios, and review the most promising novel ranking techniques and algorithms. The tutorial will be focused on advanced approaches to support exploratory/faceted search in the Enterprise, to facilitate expertise location and to utilize explicit and implicit user feedback. We will illustrate the currents trends in Enterprise search by examples from the industrial practice in this area.
While advancing the state of the art in each of Web Search and Enterprise Search is important, we believe that the future is with unified search applications providing search over both of these information spaces. In this tutorial we will highlight the opportunities which exist for such an integration and identify the most critical research challenges that need to be addressed in order to achieve it.
Pavel Dmitriev is a Researcher at Microsoft, Bellevue, WA. He received a Ph.D. degree in Computer Science from Cornell University in 2007, and a B.S. degree in Applied Mathematics from Moscow State University in 2002. Pavel Dmitriev’s research interests include enterprise and web search, machine learning, and analysis of web usage data. On these topics, he published many papers in international conferences and journals, while working in different research groups in academia and industry. His WWW 2006 paper “Using Annotations in Enterprise Search” has over 50 citations. He co-taught the tutorial on Enterprise search at WWW 2010. More information and publication record can be found at http://www.pavel-dmitriev.org/.
Pavel Serdyukov is a Senior Researcher at Yandex, Moscow. He received his Ph.D. degree in Computer Science from Twente University in 2009. He was a visiting researcher at Yahoo! Research in Barcelona, Microsoft Research in Cambridge and until recently, a postdoc at Delft University. His research interests include expert/entity search in enterprises, socially-enhanced search, distributed search and geographical multimedia search. He co-taught the tutorial on Enterprise search at WWW 2010. He is organizing the Entity track at TREC for already 3 years and organized SIGIR workshops on ”Accessible search”’ in 2010 and "Entity-oriented search" in 2011. His publications may be seen at http://www.informatik.uni-trier.de/~ley/db/indices/a-tree/s/Serdyukov:Pavel.html
Mike Taylor works for Microsoft's SharePoint Product Group. He has spent last 10 years working at Microsoft Research in Cambridge on information retrieval, focusing on applications of machine learning to Enterprise Search. Also, he has published on general retrieval technologies, mainly in the field of learning to rank. His publications may be seen at http://research.microsoft.com/en-us/people/mitaylor/