SIGIR 2003: Workshop on Implicit Measures of User
                     Interests and Preferences

Organizers:
Susan Dumais, Microsoft Research
Krishna Bharat, Google
Thorsen Joachims, Cornell University
Andreas Weigend, Amazon
 
Important Dates:
June 2, 2003 - Position statements and abstract submissions due
June 16, 2003 - Notification of acceptance
July 7, 2003 - Final notebook statements and abstracts due
Aug 1, 2003 - Workshop
NOTE: All people wishing to attend the workshop must submit a position statement before June 2,
2003.  Submission details below.
 
Workshop Overview

The goal of the workshop on Implicit Measures is to explore how
various implicit measures of user interests can be used in information
retrieval and filtering applications where it is difficult to obtain
explicit user feedback.  Since this is the first workshop on this
topic at SIGIR, we encourage participation from people with different
backgrounds and perspectives including predictive modeling,
experimental analysis, and applications development.  Applications
from information retrieval, collaborative filtering, e-commerce, user
modeling, and human-computer interaction are encouraged.  As an
outcome of the workshop we hope to identify key theoretical modeling
issues, systematize engineering principles and best practices, and
spark new research directions.
 
In most information retrieval or filtering applications, it is
difficult to get explicit feedback from users about the relevance of
the results, the appropriateness of the presentation, and more
generally about the quality of their experience.  Yet explicit
judgments are assumed by researchers for many activities like the
tuning and selection of ranking algorithms, information combination,
user modeling, information presentation, etc.  This workshop will
explore how implicit measures of user interest (such as dwell time,
click through, and user activities like annotation, printing, and
purchasing) can be used to develop predictive models for a variety of
purposes.  Example uses in the context of information retrieval
include: improved ranking and relevance assessment (e.g., the extent
to which implicit measures can be used to evaluate the quality of
systems, ranking algorithms and recommendations, or as input to
relevance feedback algorithms); personalization in search, filtering
or presentation; personalization in the large considering both
individual and aggregate data; browsing or searching agents; automatic
hyperlink generation; and adaptive web site design.  An examination of
theoretical issues such as modeling approaches (Bayesian techniques
and other predictive models), gold standards for user behavior (e.g.,
relevance judgments, purchases), combining implicit and explicit
preferences, and biases introduced by reliance on implicit measures
are also encouraged.

We encourage presentation and participation from both researchers who
are interested in theoretical issues, and practitioners who have
deployed systems that make use of implicit indicators of user
interest.

Topics of interest include, but are not limited to: 

     Novel techniques for collecting and using user activity data

     Information retrieval or collaborative filtering applications that use implicit indicators of
     interest (e.g., buying, usage or reading patterns) rather than ratings from users

     Integrating information about individual and aggregate group behaviors for search or
     collaborative filtering

     Adaptive web presentation and content selection based on inferred preferences (e.g., surfing
     assistants, intelligent agents, personalize web pages and sites)

     Practical issues in deployment of personalized systems (e.g., how do users react; are users
     confused by non-repeatability; how can users manage state)

     Personal profiles and feedback influencing search results

     Merging ranking from multiple sources using implicit usage information

     Search or relevance evaluation with implicit measures

     Modeling the evolution of interests and preference over time or task

     Effective predictive modeling techniques

     Limitations and biases encountered when relying on implicit measures

     Privacy issues

Planned Activities:

Planned activities include invited talks or introductory overviews in
key areas, short talks grouped by topic along with substantial
discussion periods.  The workshop will also include a closing
discussion on lessons learned, open issues, and potential follow-ups.

Submission Process for Participants

Two kinds of participation are solicited:

   1.Attendees: Attendance by researchers and practitioners with
   interests in implicit measures.

   2.Presenters: Presentations about predictive modeling techniques or
   experiences with systems
     that have used implicit measures of user interest, preferences, etc.

All applicants must submit a short position statement (maximum 250
words) describing their background and interest in implicit measures
of user interest and preferences.  Participants who would like to
present should submit an abstract (maximum 750 words) in addition
detailing the major points and/or results they would present during a
talk.  Submissions of both kinds will be reviewed by the program
committee and invitations issued.  Workshop attendance will be limited
to presenters and attendees selected by the committee based on their
submissions.

Submissions are due on June 2, 2003 (5pm PST).  Email your submissions (ascii or pdf preferred)
to Susan Dumais, sdumais@microsoft.com.

A notebook of participants' position statements and presentation
descriptions will be distributed to all attendees.  A summary of
workshop activities and emerging issues and trends will be prepared
for the SIGIR Forum by the organizers.