Evolving Social Search - Presentation CIKM 2011

4.065 Aufrufe

Veröffentlicht am

This is the presentation i have given at #CIKM2011 about my paper called "Evolving Social Search Using Status Messages of Social Networks"

Veröffentlicht in: Technologie
0 Kommentare
0 Gefällt mir
Statistik
Notizen
  • Als Erste(r) kommentieren

  • Gehören Sie zu den Ersten, denen das gefällt!

Keine Downloads
Aufrufe
Aufrufe insgesamt
4.065
Auf SlideShare
0
Aus Einbettungen
0
Anzahl an Einbettungen
2.668
Aktionen
Geteilt
0
Downloads
0
Kommentare
0
Gefällt mir
0
Einbettungen 0
Keine Einbettungen

Keine Notizen für die Folie

Evolving Social Search - Presentation CIKM 2011

  1. 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. 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. 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 data3 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation (IPD), Chair Prof. K. Böhm
  4. 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. 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. 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. 7. Section 2/5 THEORETICAL APPROACH7 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation (IPD), Chair Prof. K. Böhm
  8. 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 topics8 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation (IPD), Chair Prof. K. Böhm
  9. 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 levels9 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation (IPD), Chair Prof. K. Böhm
  10. 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. 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 levels11 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation (IPD), Chair Prof. K. Böhm
  12. 12. Our model for SRS Full-Text 1 Relevancy (Top 1000) „bookmarked“ „liked“ Adam „shared“ Eve „+1‘d“, (…) Jane John2 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. 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. 14. Section 3/5 FIELD STUDY14 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation (IPD), Chair Prof. K. Böhm
  15. 15. stillPlatform: Social-Search.com available one time only pancake recipe15 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation (IPD), Chair Prof. K. Böhm
  16. 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 twitter16 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation (IPD), Chair Prof. K. Böhm
  17. 17. User-Graph Folkd.com Created using gephi.org excerpt of 60.000 Relations between 40.000 Nutzern17 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation (IPD), Chair Prof. K. Böhm
  18. 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. 19. Section 4/5 RESULTS AND EVALUATION19 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation (IPD), Chair Prof. K. Böhm
  20. 20. Impact of the user‘s friend countLog(10) Log(10)20 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation (IPD), Chair Prof. K. Böhm
  21. 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 engagement21 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation (IPD), Chair Prof. K. Böhm
  22. 22. It depends on the search term: non-linear scale 50 30022 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation (IPD), Chair Prof. K. Böhm
  23. 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. 24. Section 5/5 SUMMARY & QUESTIONS24 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation (IPD), Chair Prof. K. Böhm
  25. 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 data25 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation (IPD), Chair Prof. K. Böhm
  26. 26. Thanks for your attention! Questions? Bastian Karweg Mobile Advertising GmbH (CEO) Twitter: @timetrax E-Mail: Karweg@mobile-advertising.com Web: www.bastiankarweg.de26 06.12.2011 © Bastian Karweg, 2010 - 2011 Institute for Program Structures and Data Organisation (IPD), Chair Prof. K. Böhm

×