Enriching Information Retrieval

SIGIR 2011 Workshop

July 28, 2011

Beijing, China

Organizers:

Motivation and Goals

Most information retrieval systems and tasks are now embedded in a rich context. Documents no longer exist on their own; they are connected to other documents, they are associated with users and their position in a social network, and they can be mapped onto a variety of ontologies. Similarly, retrieval tasks have become more interactive and are solidly embedded in a user's geospatial, social, and historical context.

We conjecture that new breakthroughs in information retrieval will not come from smarter algorithms that better exploit existing information sources, but from new retrieval algorithms that can intelligently use and combine new sources of contextual metadata.

The goal of the Enriching Information Retrieval workshop is to explore how new and emerging sources of contextual metadata can be used for improving information retrieval, including ranking, personalization, diversification, and faceted search. In particular, we aim to focus on three themes:

A special focus of the workshop will be on metadata and retrieval tasks associated with social networks.

The full Call for Papers can be found here.

Schedule

Time Topic
9:00 - 10:00
Invited Talk:
Mining Social Network Activity to Understand and Predict User Behavior
Jennifer Neville (Purdue)
10:00 - 10:20
Contributed Talk:
Physiological Data as Metadata: A Position Paper
Michael Cole, Jacek Gwizdka and Nicholas J. Belkin
10:20 - 10:30

Discussion

10:30 - 11:00

Coffee Break

11:00 - 11:45

Poster Session with Spotlights

11:45 - 12:15

Discussion

12:15 - 14:00

Lunch

14:00 - 15:00
Invited Talk:
Evaluating Rich Models in Context
Filip Radlinski (Microsoft)
15:00 - 15:20
Contributed Talk:
Personalizing Local Search with Twitter
Lu Guo and Matthew Lease
15:20 - 15:50

Coffee Break

15:50 - 16:10
Contributed Talk:
Enriching Information Retrieval with Reading Level Prediction
Kevyn Collins-Thompson
16:10-16:30
Contributed Talk:
Future Retrieval: What Does the Future Talk About?
Gaël Dias, Ricardo Campos and Alípio Jorge
16:30-17:30

Discussion

Invited Speakers

Jennifer Neville

Jennifer Neville (Purdue University)

Mining Social Network Activity to Understand and Predict User Behavior

Abstract:

The recent popularity of online social networks has increased the amount of information available about users' behavior--including current activities and interactions among friends and family. This rich relational information can be used to predict user interests and preferences even when individual data is sparse, as birds of a feather do indeed flock together. Although these data offer several opportunities to improve search and retrieval, the characteristics of online social network data also present a number of challenges to accurately incorporate the information into retrieval algorithms. In this talk, I will describe our recently developed methods for (1) predicting relationship strength, (2) combining information from multiple social interaction networks, and (3) identifying sources of user correlation. While giving an overview of these methods, I will discuss their potential connections to retrieval tasks and highlight algorithmic and evaluation challenges that may arise from modeling the complex network dependencies.

Bio:

Jennifer Neville is an assistant professor at Purdue University with a joint appointment in the Departments of Computer Science and Statistics. She received her PhD from the University of Massachusetts Amherst in 2006. She received a DARPA IPTO Young Investigator Award in 2003 and was selected as a member of the DARPA Computer Science Study Group in 2007. In 2008, she was chosen by IEEE as one of "AI's 10 to watch." Her research focuses on developing data mining and machine learning techniques for relational domains, including citation analysis, fraud detection, and social network analysis.


Filip Radlinski

Filip Radlinski (Microsoft)

Evaluating Rich Models in Context

Abstract:

Contextual information can allow information retrieval systems to improve ranking quality substantially. The richer the model, the more likely it is to provide actionable information. I will show how users' historical interests can provide context to improve information retrieval, while preserving user privacy. Further, I will show how geographical context provides even more opportunities in information retrieval.

A second key question in context sensitive retrieval is that of evaluation: Traditional judgment based evaluation is not ideally suited to assessing the extent to which context based systems improve the information retrieval experience of users in practice. In presenting the above approaches, I will also detail an effective online evaluation approach for measuring improvements in this setting.

Bio:

Filip Radlinski is a researcher at Microsoft and a contributor to Bing, where he works on machine learning approaches to information retrieval. He completed his dissertation on learning to rank from implicit feedback at Cornell University in 2008. His recent research focuses on learning personalized rankings, measuring ambiguity in queries and user intents, and studying how to assess the quality of ranked lists of documents from the user perspective by using click information.

Abstracts

Posters

Poster instructions:

Posters will be presented on a stand, with a backing board provided of size 90cm x 120cm, which will accommodate up to A0 format size. You can find a photo of the poster setup posted here.

Program Committee