A New Look at Old Tricks: The Fertile Roots of Current Research
[This tutorial assumes mathematical knowledge at the level of a Master's degree in Computer Science or Computer Engineering.]
While some might think that an examination of the roots is of merely historical interest, it has practical value as well. When you know which earlier research has provided the origins for the things that you are interested in, you can use that fact to trace its other descendants, and often find rich and rewarding ideas in a literature that you would not normally reach, because it was not considered important by your instructors when you were learning about the problems. In addition to pattern recognition and citation analysis, the tutorial will also expose and review some of the relations to the fields of statistics and operations research.
Participants will become familiar with roots in Pattern Analysis, Statistics, Information Science and other sources of key ideas that reappear in the current development of Information Retrieval as it applies to Search Engines, Social Media, and Collaborative Systems. They will be able to separate problems from algorithms, and algorithms from heuristics, in the application of these ideas to their own research and/or development activities Specific Topics include: Origins: Kent & Luhn; Vectors: Salton, Fox and Singhal; Probabilities: Maron & Kuhns; Toward Theory: Good; Robertson, Sparck-Jones; Generative Approaches - Lafferty, Croft, Blei; Network Approaches: Small, Narin, Kleinberg, Page; Tying it all together.
Course materials will be made available on a Web site.
Paul Kantor (http://comminfo.rutgers.edu/~cgal/Final_CV_v2.htm) is Professor II (Distinguished) of Information Science at Rutgers University. He is a founding editor of the journal Information Retrieval (http://www.springer.com/computer/database+management+%26+information+retrieval/journal/10791) and serves on the editorial boards of Information Retrieval, Information Processing and Management, and the Journal of the American Society for Information Science and Technology.