Opening statement at the "Looking forward" panel at the 25 years of TREC celebration event, Nov 15th, 2016. Webcast to appear within a week: https://www.nist.gov/news-events/events/2016/11/webcast-text-retrieval-conference

1. Looking Forward
Prof.dr.ir. Arjen P. de Vries
arjen@acm.org
Gaithersburg MD, November 15th, 2016

2. Q: “TREC Anniversary”

3. Top Result:
 50 years of Star Trek
(Article on the Verge about Facebook Like buttons)

4. Science Fiction
 Defining a TREC task or a track is like time-travel in Back
to the Future
Note to the audience: that is just 74 characters
You could even add the hashtag #TREC #TRECCelebrations
and my Twitter handle @arjenpdevries

6. Better Search – “Deep Personalization”
 “Even more broadly than trying to get people the right
content based on their context, we as a community need to
be thinking about how to support people through the entire
search experience.”
Jaime Teevan on “Slow Search”
 Search as a dialogue
My first journal paper:
De Vries, Van der Veer and Blanken: Let’s talk about it: dialogues with multimedia databases (1998)

7. Moving Forward
 Elements of the “Slow Search movement” at TREC today:
- Sessions
- Tasks
- Dynamic domains
- Total recall
- Complex Answer Retrieval (new!)

8. Missing from TREC!
 Access to rich personal data including email, browsing
history, documents read and contents of the user’s home
directory…

10. Trade log data!
IR-809: (2011) Feild, H., Allan, J. and Glatt, J.,
"CrowdLogging: Distributed, private, and
anonymous search logging," Proceedings of the
International Conference on Research and
Development in Information Retrieval (SIGIR'11),
pp. 375-384. [View bibtex]
We describe an approach for distributed search log collection, storage, and mining,
with the dual goals of preserving privacy and making the mined information broadly
available. [..] The approach works with any search behavior artifact that can be
extracted from a search log, including queries, query reformulations, and query-
click pairs.

11. Open challenges
 How to select the part of your log data you are willing to
trade?
 How to estimate the value of this log data?
 And a social challenge, not so much scientific:
How to get people to participate?

12. Branding

13. Branding (NL)

14. The TREC Brand
 A community that creates reusable test collections

15. Extra Slides

16. Reproducibility vs. Representativeness
 Increasing representativeness of a TREC task should not
come at the cost of sacrificing reproducibility
(104 characters )
Samar, T., Bellogín, A. & de Vries, A.P. Inf Retrieval J (2016) 19: 230.
doi: 10.1007/s10791-015-9276-9

17. Baltimore

18. Baltimore
 Title query of TREC topic 478 for the information need “Who is
the mayor of Baltimore”
 “The honest conclusion of this year’s evaluation should be that we
underestimated the problem of handling Web data. Surprising is
the performance of the title-only queries doing better than queries
including description or even narrative. It seems that the web-track
topics are really different from the previous TREC topics in the ad-
hoc task, for which we never weighted title terms different from
description or narrative.”
(Quote from the CWI TREC-9 paper)