Web users are increasingly relying on social interaction to complete and validate the results of their search activities. While search systems are superior machines to get world-wide information, the opinions collected within friends and expert/local communities can ultimately determine our decisions: human curiosity and creativity is often capable of going much beyond the capabilities of search systems in scouting “interesting” results, or suggesting new, unexpected search directions. Such personalized interaction occurs in most times aside of the search systems and processes, possibly instrumented and mediated by a social network; when such interaction is completed and users resort to the use of search systems, they do it through new queries, loosely related to the previous search or to the social interaction. In this paper we propose CrowdSearcher, a novel search paradigm that embodies crowds as first-class sources for the information seeking process. CrowdSearcher aims at filling the gap between generalized search systems, which operate upon world-wide information - including facts and recommendations as crawled and indexed by computerized systems – with social systems, capable of interacting with real people, in real time, to capture their opinions, suggestions, emotions. The technical contribution of this paper is the discussion of a model and architecture for integrating computerized search with human interaction, by showing how search systems can drive and encapsulate social systems. In particular we show how social platforms, such as Facebook, LinkedIn and Twitter, can be used for crowdsourcing search-related tasks; we demonstrate our approach with several prototypes and we report on our experiment upon real user communities.

1. Politecnico Di Milano
Dipartimento Di Elettronica e Informazione
Answering Search Queries
with CrowdSearcher
A crowdsourcing approach to search
Alessandro Bozzon, Marco Brambilla, Stefano Ceri
WWW 2012, Lyon, France
April, 20th 2012

2. Context
• Web is a huge, heterogeneous data source:
• Structured, unstructured and semi-structured data
• Known problems of trust, reputation, consistency
• User needs to solve real-life problems, not to find a web site
Answering Search Queries with CrowdSearcher

3. Context
Answering Search Queries with CrowdSearcher

4. Context
• User needs to solve real-life problems, not to find a web site
• Web queries get increasingly complex and specialized
• Exploratory search
• From document search to object search
• Search as a service
• Viability of systems based upon search service orchestration
Answering Search Queries with CrowdSearcher

5. Background: semantic multi-domain search
“… search for upcoming concerts close to an attractive location (like a
beach, lake, mountain, natural park, and so on), considering also
availability of good, close-by hotels …”
Answering Search Queries with CrowdSearcher

6. Background: semantic multi-domain search
“… expand the search to get information about available restaurants
near the candidate concert locations, news associated to the event and
possible options to combine further events …”
Answering Search Queries with CrowdSearcher

7. Liquid Query:
Query Submission [WWW
2010]
Example Scenario 1: Trip planner for events
Concert Hotels
query conditions query conditions
Answering Search Queries with CrowdSearcher

8. Liquid Query:
Query Execution
Answering Search Queries with CrowdSearcher

9. Liquid Query: alternative visualizations
and domain-independent platform
Example Scenario 2: Scientific Publication search
Answering Search Queries with CrowdSearcher

10. Problem Statement
• When dealing with real-life problems, people do not trust the web
completely
• Want to go back to discussion with people
• Expect insights, opinions, reassurance
Answering Search Queries with CrowdSearcher

11. Problem Statement
• When dealing with real-life problems, people do not trust the web
completely
• Want to go back to discussion with people
• Expect insights, opinions, reassurance
Answering Search Queries with CrowdSearcher

12. Problem Statement
• When dealing with real-life problems, people do not trust the web
completely
• Want to go back to discussion with people
• Expect insights, opinions, reassurance
• Our proposal
Interleaving and integration
of exploratory search
and social community input
Answering Search Queries with CrowdSearcher

13. Social Search: increasing quality in search
• From exploratory search to friends and experts feedback
Initial
query
Exploration
Exploratory step Human
Search Search
System System
Exploration
step
System API Social API
Database / Crowd /
IR index Community
Answering Search Queries with CrowdSearcher

14. From crowds to communities –
The problems
• Crowds vs. social networks
• Friends or workforce?
• Complex interleaving of factors. Including:
• Intensity of social activity of the asker
• Motivation of the responders
• Topic
• Information diffusion
• Timing of the post (hour of the day, day of the week)
• Context and language barrier
Answering Search Queries with CrowdSearcher

15. Task management problems
Typical crowdsourcing problems:
• Task splitting: the input data collection is too complex relative to the cognitive
capabilities of users.
• Task structuring: the query is too complex or too critical to be executed in
one shot.
• Task routing: a query can be distributed according to the values of some
attribute of the collection.
Plus:
• Platform/community assignment: a task can be assigned to different
communities or social platforms based on its focus
Answering Search Queries with CrowdSearcher

16. Social Search – query properties
• Invited community
• Engagement platform
• Execution platform
• Query type: Like, Add, Sort / Rank, Comment, Modify
• Visibility: public or private
• Diffusion: enabled or not
• Timespan
Answering Search Queries with CrowdSearcher

17. Deployment: search on the social network
• Multi-platform deployment
Generated query template
Embedded External
Native Standalone
application application application
API
Social/ Crowd platform
Native
Embedding behaviours
Community / Crowd
Answering Search Queries with CrowdSearcher

18. Deployment: search on the social network
• Multi-platform deployment
Answering Search Queries with CrowdSearcher

19. Deployment: search on the social network
• Multi-platform deployment
Answering Search Queries with CrowdSearcher

20. Deployment: search on the social network
• Multi-platform deployment
Answering Search Queries with CrowdSearcher

21. Deployment: search on the social network
• Multi-platform deployment
Answering Search Queries with CrowdSearcher

22. Example: Find your next job (exploration)
Answering Search Queries with CrowdSearcher

23. Example: Find your job (social invitation)
Answering Search Queries with CrowdSearcher

24. Example: Find your job (social invitation)
Selected data items
can be transferred
to the crowd question
Answering Search Queries with CrowdSearcher

25. Find your job (response submission)
Answering Search Queries with CrowdSearcher

26. Experimental setting
• Some 150 users
• Two classes of experiments:
• Random questions on fixed topics: interests (e.g. restaurants in the vicinity
of Politecnico), to famous 2011 songs, or to top-quality EU soccer teams
• Questions independently submitted by the users
• Different invitation strategies:
• Random invitation
• Explicit selection of responders by the asker
• Outcome
• 175 like and insert queries
• 1536 invitations to friends
• 95 questions (~55%) got at least one answer
• 230 collected answers
Answering Search Queries with CrowdSearcher

27. Experiments: Manual and random questions
Answering Search Queries with CrowdSearcher

28. Experiments: Interest and relationship
• Manually written and assigned questions
are consistently more responded in time
Answering Search Queries with CrowdSearcher

29. Experiments: Query type
• Engagement depends on the difficulty of the task
• Like vs. Add tasks:
Answering Search Queries with CrowdSearcher

30. Experiments: Distribution of answers/invitation
• Sometimes: more answers than invitations (limited cases)
Answering Search Queries with CrowdSearcher

31. Experiment: Social platform
• The question enactment platform role
• Facebook vs. Doodle
Answering Search Queries with CrowdSearcher

32. Experiment: Social platform
• The question enactment platform role
• Facebook vs. Doodle
Answering Search Queries with CrowdSearcher

33. Experiment: Posting time
• The question enactment platform role
• Facebook vs. Doodle
Answering Search Queries with CrowdSearcher

34. Conclusions and future work
Status
• the chances to get responses depend a lot on the consistency of
the users’ community and on the mechanisms that are exploited
for inviting the users and for collecting the responses
Future work
• More experiments (e.g., vs. sociality of users, vs. crowds, …)
• Not only search: active integration of web structured data and
social sensors
Some ads
• Search Computing book series (Springer LNCS)
• Workshop Very Large Data Search at VLDB
• VLDB Journal special issue (deadline Sept 2012)
Answering Search Queries with CrowdSearcher

35. www.search-computing.org
@searchcomputing
Thanks!
Questions?
@marcobrambi
marco.brambilla@polimi.it