Some(what) Grand Challenges for Information Retrieval
Although we see the positive results of information retrieval research embodied throughout the Internet, on our computer desktops, and in many other aspects of daily life, at the same time we notice that people still have a wide variety of difficulties in finding information that is useful in resolving their problematic situations. This suggests that there still remain substantial challenges for research in IR. Already in 1988, on the occasion of receiving the ACM SIGIR Gerard Salton Award, Karen Spärck Jones suggested that substantial progress in information retrieval was likely only to come through addressing issues associated with users (actual or potential) of IR systems, rather than continuing IR research's almost exclusive focus on document representation and matching and ranking techniques. In recent years it appears that her message has begun to be heard, yet we still have relatively few substantive results that respond to it. In this talk, I identify a few challenges for IR research which fall within the scope of association with users, and which I believe, if properly addressed, are likely to lead to substantial increases in the usefulness, usability and pleasurability of information retrieval.

You are a document too: Web mining and IR for next-generation information literacy
Information retrieval and data mining often assume a simple world: There are people with information needs who search - and find - information in sources such as documents or databases. Hence, the user-oriented goals are

(a) information literacy: the users' ability to locate, evaluate, and use effectively the needed information, and
(b) tools that obviate the need for some of the technical parts of this information literacy. Examples of such tools are search-engine interfaces that direct each user's attention to only an individualised part of the "information overload" universe.

In this talk, I will argue that such simple-world assumptions are no longer justified, advocate a shift in focus, and outline concrete steps for using technology to further a more comprehensive form of information literacy. I will focus on data, documents, and information-related activities on the Web, which are analysed in Web mining and (Web) IR:

1. In today's (Web) information society,
   • the problem is not just information overload, but also information sparsity
   • information-related activities involve disclosing and withholding (the latter known under names such as "privacy" or "business secrets")
   • each information-related activity has (at least) one source, one manifestation as data/document, one user and one stakeholder; network effects abound.
   • Most importantly, the dichotomy of information-seeking users and information-containing data/documents has vanished in a time when virtually every activity generates data/documents.
2. These considerations lead to a new operationalisation of information literacy, understood in its broader sense as a set of competencies that a citizen of an information society ought to possess to participate intelligently and actively in that society.
3. Based on a range of concrete examples, I will illustrate how tools can support this type of information literacy (and obviate the need to know some technical details).

Biography
Amit Singhal is a Google Fellow. According to the New York Times, Mr. Singhal is the master of what Google calls its “ranking algorithm” — the formulas that decide which Web pages best answer each user’s question. A native of India, Amit got his bachelors degree in Computer Science from IIT Roorkee in 1989. Amit holds a MS in Computer Science from University of Minnesota, Duluth, and a Ph.D. in Computer Science from Cornell University in Ithaca, NY. At Cornell Amit studied with Gerard Salton, a pioneer in the field of Information Retrieval. Amit runs a team in Google's Search Quality group. He is and his team are responsible for the Google search algorithms.