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September 16, 2011 by Gary Price

Research Paper: "Information Seeking: Convergence of Search, Recommendations and Advertising"

September 16, 2011 by Gary Price

Title: Information Seeking: Convergence of Search, Recommendations and Advertising (2011)
Source: Communications of the ACM (Accepted) via Stanford University InfoLab
Authors: Hector Garcia-Molina, Georgia Koutrika, Parameswaran, Aditya Parameswaran
Affiliations (All Authors): Stanford University

From the Introduction:

All of us are faced with a “deluge of data” [2], in our work- places and our homes: an ever-growing World Wide Web, digital books and magazines, photographs, blogs, tweets, e- mails, databases, activity logs, sensor streams, on-line videos, movies and music, and so on. Thus, one of the fundamen- tal problems in Computer Science has become even more critical today: how to identify objects satisfying a user’s information need. The goal is to present to the user only information that is of interest and relevance, at the right place and time.

At least three types of information providing mechanisms have been developed over the years to satisfy user informa- tion needs:

  • A search mechanism takes as input a query that de- scribes the current user interests. A body of objects (e.g., documents, records) is searched, and ones that somehow match the query are returned. For example, in a Web search engine, a user may enter a sequence of words (the query), and is given a ranked list of Web pages that contain the desired words. In a database system, the query is typically more structured (e.g., I want the names of products with price less than 100 dollars) and the search is over structured records.
  • A recommendation mechanism typically does not use an explicit query but rather analyzes the user context (e.g., what the user has recently purchased or read), and if available, a user profile (e.g., the user likes mys- tery novels). Then the recommendation mechanism presents to the user one or more descriptions of objects (e.g., books, people, movies) that may be of interest. For example, in an electronic commerce site, when a user purchases one object, he may be shown a set of similar objects, or objects that other people have pur- chased together with the just purchased object. In a Web search engine, when a user types in one query, he may be shown a list of related queries that others have entered following the first query.

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Filed under: Journal Articles, News

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About Gary Price

Gary Price (gprice@gmail.com) is a librarian, writer, consultant, and frequent conference speaker based in the Washington D.C. metro area. He earned his MLIS degree from Wayne State University in Detroit. Price has won several awards including the SLA Innovations in Technology Award and Alumnus of the Year from the Wayne St. University Library and Information Science Program. From 2006-2009 he was Director of Online Information Services at Ask.com. Gary is also the co-founder of infoDJ an innovation research consultancy supporting corporate product and business model teams with just-in-time fact and insight finding.

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