Tien-Thanh Vu
Computing and Communications Department
The Open University
Improving Search Personalisation
with Dynamic Gr...
Table of Contents
 Motivation
 Methodology
 A pilot study
 Conclusions and Future Work
 Motivation
 Methodology
 A pilot study
 Conclusions and Future Work
What is search personalisation?
The search performance
depends on
the richness of a user profile
J. Teevan, M. R. Morris, and S. Bush. Discovering and usi...
How can we enrich a user
profile?
 Use information of the group of users who share
common interests
R. W. White, W. Chu, ...
What is the main research problem?
 Construct groups statically using some
predetermined criterions such as common clicke...
Our proposal
 The groups should be dynamically constructed
in response to the user’s input query
Research question
How can we improve the performance of search
personalisation with dynamic group formation?
 How can we ...
 Motivation
 Methodology
 A pilot study
 Conclusions and Future Work
Constructing a user profile
 Construct implicitly using the relevant data
extracted from each user’s search history (i.e....
Constructing a user profile
 Average the relevant documents over topics
Query-dependent user grouping
 Construct shared user profiles
 Use the input query as an indicator for grouping
users
Constructing a shared user
profile
Query-dependent user grouping
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...
Enriching a user profile
 Average all users in the group over topics
Re-ranking search results
 For each input query
 Download the top n ranked search results from the
search engine
 Compu...
Re-ranking search results
 Motivation
 Methodology
 A pilot study
 Conclusions and Future Work
A pilot study
 Evaluation metrics
 Dataset
 Preliminary results
Evaluation metric
 Personalisation Gain (P-Gain)
worse#+better#
worse#-better#
 GainP
Dataset
 Query logs from Bing search engine for 15 days
from 1st to 15th July 2012, 106 anonymous users
 A relevant docu...
Preliminary results
 Baseline and Personalisation Strategies
 Baseline: The original ranked results from Bing
 S_Profil...
Preliminary results
 Overall Performance
Strategy #Better #Worse P-Gain
S_Profile 913 664 0.1579
S_Group 882 491 0.2848
D...
 Motivation
 Methodology
 A pilot study
 Conclusions and Future Work
Conclusions
 Grouping improves search performance
 Dynamic grouping outperforms static grouping
Outlook
 Carry out evaluation on larger-scale data sets
 Extend the model to capture user’s interests that
change over t...
Thank You!
Any Questions?
Constructing a user profile
 Use a topic modelling method to learn topics from
the data
How can we build a user profile?
 Ask the user explicitly to provide her interests
(e.g. questionnaires)
 Infer her inte...
Improving search personalisation with dynamic group formation
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Improving search personalisation with dynamic group formation

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

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Improving search personalisation with dynamic group formation

  1. 1. Tien-Thanh Vu Computing and Communications Department The Open University Improving Search Personalisation with Dynamic Group Formation
  2. 2. Table of Contents  Motivation  Methodology  A pilot study  Conclusions and Future Work
  3. 3.  Motivation  Methodology  A pilot study  Conclusions and Future Work
  4. 4. What is search personalisation?
  5. 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. 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. 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. 8. Our proposal  The groups should be dynamically constructed in response to the user’s input query
  9. 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. 10.  Motivation  Methodology  A pilot study  Conclusions and Future Work
  11. 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. 12. Constructing a user profile  Average the relevant documents over topics
  13. 13. Query-dependent user grouping  Construct shared user profiles  Use the input query as an indicator for grouping users
  14. 14. Constructing a shared user profile
  15. 15. Query-dependent user grouping
  16. 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. 17. Enriching a user profile  Average all users in the group over topics
  18. 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 ),( )|( ),|( dqr udp qudf 
  19. 19. Re-ranking search results
  20. 20.  Motivation  Methodology  A pilot study  Conclusions and Future Work
  21. 21. A pilot study  Evaluation metrics  Dataset  Preliminary results
  22. 22. Evaluation metric  Personalisation Gain (P-Gain) worse#+better# worse#-better#  GainP
  23. 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. 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. 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. 26.  Motivation  Methodology  A pilot study  Conclusions and Future Work
  27. 27. Conclusions  Grouping improves search performance  Dynamic grouping outperforms static grouping
  28. 28. Outlook  Carry out evaluation on larger-scale data sets  Extend the model to capture user’s interests that change over time
  29. 29. Thank You! Any Questions?
  30. 30. Constructing a user profile  Use a topic modelling method to learn topics from the data
  31. 31. 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

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