Evolving Social Search - Presentation CIKM 2011

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

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

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


Example 1

http://www.onlineschools.org/blog/facebook-obsession/


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/


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.


Section 2/5

THEORETICAL
APPROACH


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


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


Engagement intensity

The more effort the user has to go through, the higher the value:


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


Our model for SRS
Full-Text
1 Relevancy
(Top 1000)

„bookmarked“
„liked“
Adam „shared“
Eve „+1‘d“, (…)
Jane
John
2

Social Relevance Score (SRS):


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.


Section 3/5

FIELD STUDY


still
Platform: Social-Search.com available

one time only

pancake recipe


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


User-Graph

Folkd.com

Created using gephi.org
excerpt of 60.000 Relations
between 40.000 Nutzern


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.


Section 4/5

RESULTS AND
EVALUATION


Impact of the user‘s friend count
Log(10)

Log(10)


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


It depends on the search term: non-linear scale

50 300


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.
  


Section 5/5

SUMMARY &
QUESTIONS


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


Thanks for your attention!

Questions?

Bastian Karweg
Mobile Advertising GmbH (CEO)

Twitter: @timetrax
E-Mail: Karweg@mobile-advertising.com
Web: www.bastiankarweg.de


Evolving Social Search - Presentation CIKM 2011

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