This is a presentation associated with my paper at the SIGIR 2014 conference.

1. Tien-Thanh Vu
Computing and Communications Department
The Open University
Improving Search Personalisation
with Dynamic Group Formation

2. Table of Contents
 Motivation
 Methodology
 A pilot study
 Conclusions and Future Work

3.  Motivation
 Methodology
 A pilot study
 Conclusions and Future Work

4. What is search personalisation?

5. The search performance
depends on
the richness of a user profile
J. Teevan, M. R. Morris, and S. Bush. Discovering and using groups to improve personalized search. WSDM’09,
pages 15-24, USA, 2009. ACM

6. How can we enrich a user
profile?
 Use information of the group of users who share
common interests
R. W. White, W. Chu, A. Hassan, X. He, Y. Song, and H. Wang. Enhancing personalized search by mining and
modeling task behavior. WWW '13, pages 1411-1420, Switzerland, 2013. ACM

7. What is the main research problem?
 Construct groups statically using some
predetermined criterions such as common clicked
documents
Users in a group may have different interests on
different topics w.r.t the input query
Z. Dou, R. Song, and J.-R. Wen. A large-scale evaluation and analysis of personalized search strategies. WWW '07,
pages 581-590, NY, USA, 2007. ACM.

8. Our proposal
 The groups should be dynamically constructed
in response to the user’s input query

9. Research question
How can we improve the performance of search
personalisation with dynamic group formation?
 How can we dynamically group users who share
common interests?
 How can we enrich user profiles with group
information?
 Can enriched user profiles help to improve search
performance?

10.  Motivation
 Methodology
 A pilot study
 Conclusions and Future Work

11. Constructing a user profile
 Construct implicitly using the relevant data
extracted from each user’s search history (i.e.
query logs)
 Extract related topics from the data
 Build a user profile based on the topics

12. Constructing a user profile
 Average the relevant documents over topics

13. Query-dependent user grouping
 Construct shared user profiles
 Use the input query as an indicator for grouping
users

14. Constructing a shared user
profile

15. Query-dependent user grouping

16. Query-dependent user grouping
 CosineSimilarity sp, q =
sp.q
sp .|q|
= t∈T wpt.wqt
t∈T wpt
2
t∈T wqt
2
 E.g. sp = (1,2,5); q = (4,5,6)
CosineSimilarity sp, q =
1∗4+2∗5+5∗6
12+22+52 42+52+62
≈ 0.915
The 2-nearest users

17. Enriching a user profile
 Average all users in the group over topics

18. Re-ranking search results
 For each input query
 Download the top n ranked search results from the
search engine
 Compute a personalised score for each web page d
given the current user u
 Combine the personalised score p(d|u) and the
original rank r(q,d), to get a final score
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),|(
dqr
udp
qudf 

19. Re-ranking search results

20.  Motivation
 Methodology
 A pilot study
 Conclusions and Future Work

21. A pilot study
 Evaluation metrics
 Dataset
 Preliminary results

22. Evaluation metric
 Personalisation Gain (P-Gain)
worse#+better#
worse#-better#
 GainP

23. Dataset
 Query logs from Bing search engine for 15 days
from 1st to 15th July 2012, 106 anonymous users
 A relevant document is a click with dwell time of
at least 30 seconds or the last click in a session
(SAT click)
Item ALL Training Test
#days 15 10 5
#users 106 106 106
#queries 17947 11695 6252
#distinct queries 8008 5237 3102
#clicks 24041 15688 8353
#SAT clicks 16166 10607 5559
#SAT clicks/#queries 0.9008 0.9069 0.8892

24. Preliminary results
 Baseline and Personalisation Strategies
 Baseline: The original ranked results from Bing
 S_Profile: Use only the current user profile
 S_Group: Enrich the profile with static group
 D_Group: Enrich the profile with dynamic group

25. Preliminary results
 Overall Performance
Strategy #Better #Worse P-Gain
S_Profile 913 664 0.1579
S_Group 882 491 0.2848
D_Group 884 450 0.3253

26.  Motivation
 Methodology
 A pilot study
 Conclusions and Future Work

27. Conclusions
 Grouping improves search performance
 Dynamic grouping outperforms static grouping

28. Outlook
 Carry out evaluation on larger-scale data sets
 Extend the model to capture user’s interests that
change over time

29. Thank You!
Any Questions?

31. Constructing a user profile
 Use a topic modelling method to learn topics from
the data

32. How can we build a user profile?
 Ask the user explicitly to provide her interests
(e.g. questionnaires)
 Infer her interests implicitly using the user’s
search history