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Evolving Social Search - Presentation CIKM 2011
- 1. Bastian Karweg, C. Hütter, Prof. K. Böhm
“Evolving Social Search Based on Bookmarks
and Status Messages from Social Networks “
Institute for Program Structures and Data Organisation (IPD), Chair Prof. K. Böhm
KIT – University of the State of Baden-Wuerttemberg © Bastian Karweg, 2010 - 2011
and National Research Center of the Helmholtz Association www.kit.edu
- 2. Introduction and motivation
Scenario
Jane is planning to make some delicious
pancakes for her son’s birthday. A friend recently
recommended a link to a great recipe, but she
doesn’t remember neither who it was nor
on which social network he posted.
Goal:
Personalize Jane‘s search result set
in a way that to take all recommendations
into account and idealy show her the
pancake recipe she was looking for.
2 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation
(IPD), Chair Prof. K. Böhm
- 3. Influence factors:
The more you know about who is searching something,
the better are you able to target the results:
• language • age, gender
• location personalization • interests, actions
• search type • friends, contacts
1. Identification of each user has to be possible
2. His data has to be available, ideally in a
standardized form on a central location
3. The user needs sufficient control systems to
grant or deny access to all or part of his data
3 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation
(IPD), Chair Prof. K. Böhm
- 4. Example 1
http://www.onlineschools.org/blog/facebook-obsession/
4 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation
(IPD), Chair Prof. K. Böhm
- 5. Social Web
content interaction
user profile +
contacts (social graph)
most of them do not yet use
their social data for search.
http://www.theconversationprism.com/
5 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation
(IPD), Chair Prof. K. Böhm
- 6. Example 2
• social bookmarking service
• founded in 2006
• 1.5mm users
• 24mm bookmarks (links)
Also runs a standard fulltext based search engine.
Use available social data for search.
Aggregate data from different platforms.
6 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation
(IPD), Chair Prof. K. Böhm
- 7. Section 2/5
THEORETICAL
APPROACH
7 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation
(IPD), Chair Prof. K. Böhm
- 8. 3 Types of social search [ Evans2009 ]
„Collective“ „Collaborative“ „Friend-filtered“
Using the Using the Using
„wisdom of the crowds“ „village paradigm“ „personalized results“
=> The more popular a => queries are all => based on what friends
content, the better it ranks. answered by experts. have shared in the past.
As discussed in As discussed in Newest approach and
[Hotho2006] [Horowitz2010] main topic of our work.
Examples Examples Recent examples
Digg.com, reddit, Aarkvark.com, Bing social, GooglePlus,
Delicious Bookmarks Q&A Communities Blekko slashtag
Problems Problem Problems
Easy manipulation, No instant results; Not sure if hypotheses
„one-size-fits-all“ Needs reliable experts on work out as predicted.
many topics
8 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation
(IPD), Chair Prof. K. Böhm
- 9. Theoretical approach
Where to start?
A) Measure the amount of social interactions for each search result.
Engagement intensity
B) How strongly should somebody’s recommendation influence the
searchers results?
Trust levels
9 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation
(IPD), Chair Prof. K. Böhm
- 10. Engagement intensity
The more effort the user has to go through, the higher the value:
10 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation
(IPD), Chair Prof. K. Böhm
- 11. Trust Levels
• Trust is established as an asymetric relation between users
• The user can adjust the trust levels for each contact
• The system can assist in fine tuning within trust levels
11 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation
(IPD), Chair Prof. K. Böhm
- 12. Our model for SRS
Full-Text
1 Relevancy
(Top 1000)
„bookmarked“
„liked“
Adam „shared“
Eve „+1‘d“, (…)
Jane
John
2
Social Relevance Score (SRS):
12 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation
(IPD), Chair Prof. K. Böhm
- 13. Hypotheses
The number of links available for social search depends
on the number of friends a user has in his social graph.
There is a certain number of friends the user needs for
social search to work „properly“ for any query.
The result quality of classic full text search improves
when combining it with the Social Relevance Score.
We needed „enough data“ to back these up.
13 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation
(IPD), Chair Prof. K. Böhm
- 14. Section 3/5
FIELD STUDY
14 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation
(IPD), Chair Prof. K. Böhm
- 15. still
Platform: Social-Search.com available
one time only
pancake recipe
15 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation
(IPD), Chair Prof. K. Böhm
- 16. Field Study Size
Crawling social streams every 10 minutes for 58 days
(from 09th of Nov.10 until 05th of Jan.11)
2.385 testers 468.889 friends
430, 13%
217010, 4
1651, 51% 6% 251879, 5
1164, 36%
4%
facebook twitter facebook twitter
16 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation
(IPD), Chair Prof. K. Böhm
- 17. User-Graph
Folkd.com
Created using gephi.org
excerpt of 60.000 Relations
between 40.000 Nutzern
17 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation
(IPD), Chair Prof. K. Böhm
- 18. Resulting datasets:
Stream Data Search Data Search Simulation
Extracted and Analyzed a random Run a comparison
crawled sample of test with
428.522 2.098 36 query terms
link-recommendations. social search sessions. on all test-accounts.
18 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation
(IPD), Chair Prof. K. Böhm
- 19. Section 4/5
RESULTS AND
EVALUATION
19 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation
(IPD), Chair Prof. K. Böhm
- 20. Impact of the user‘s friend count
Log(10)
Log(10)
20 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation
(IPD), Chair Prof. K. Böhm
- 21. Results and Evaluation
So how many friends does one need
for social search to “work properly“?
Defining „work proper“:
minimum 1 social results for the average query
good 5 social results for the average query
contains the search term and has friend engagement
21 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation
(IPD), Chair Prof. K. Böhm
- 22. It depends on the search term: non-linear scale
50 300
22 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation
(IPD), Chair Prof. K. Böhm
- 23. Influence of SRS
1. 2. 3.
average number of clicks average time
click-position needed per search spent on search
5.70 => 2.92 1.39 => 1.14 24.46 sec => 13.56 sec
The user finds a suitable The user needs 0.25 The user detects
result on average clicks less to get to a relevant results
2.78 positions earlier! suitable result! significantly faster.
23 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation
(IPD), Chair Prof. K. Böhm
- 24. Section 5/5
SUMMARY &
QUESTIONS
24 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation
(IPD), Chair Prof. K. Böhm
- 25. Summary
Using SRS can measurably improve social search
Social Search will be a major part of all future search engines.
This development is confirmed by the current market developments
(Google Plus, Bing social, blekko slashtag …)
The success of a social search depends on:
Connectivity of the searching user
Popularity of the search term
Time since when the user is using social media
Size of available social data
25 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation
(IPD), Chair Prof. K. Böhm
- 26. Thanks for your attention!
Questions?
Bastian Karweg
Mobile Advertising GmbH (CEO)
Twitter: @timetrax
E-Mail: Karweg@mobile-advertising.com
Web: www.bastiankarweg.de
26 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation
(IPD), Chair Prof. K. Böhm