The document outlines a tutorial on collaborative information retrieval (IR), discussing definitions, techniques, and emerging topics related to collaboration in IR settings. It examines different collaborative frameworks such as user-user and user-system interactions, challenges faced in collaborative IR, and the impact of social media on information retrieval models. The tutorial aims to bridge the gap between research areas of collaborative IR and social media IR to enhance systems enabling collaborative information seeking.

1. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
Collaborative Information Retrieval:
Frameworks, Theoretical Models and Emerging Topics
Lynda Tamine
Paul Sabatier University
IRIT, Toulouse - France
Laure Soulier
Pierre and Marie Curie University
LIP6, Paris - France
Monday 17th October, 2016
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2. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
GOAL OF THE TUTORIAL
• Introducing the notion of collaboration and the different forms of collaborative
information retrieval and seeking
Positioning collaborative IR within the major theoretical approaches of IR
Identifying the Collaborative IR challenges
• Presenting state-of-the-art theoretical models for collaborative IR
Identifying the key factors affecting the design of collaborative IR models
Reviewing major research progress in the area
• Discussing promising research directions
Bridging the gap between two (close) research branches: collaborative IR and social media IR
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3. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
OUTLINE OF THE TUTORIAL
• Part 1: Collaboration in information seeking and retrieval
• Part 2: Models and techniques for document seeking and retrieval
• Part 3: Emerging topics around collaboration
• Part 4: Discussion
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4. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
PLAN
1. Collaboration in IS and IR
What does collaboration refer to (in IR)?
Collaborative information retrieval paradigms
Collaborative information retrieval challenges and issues
2. Collaborative IR techniques and models
3. Emerging topics around collaboration
4. Open ideas
5. Discussion
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5. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
WHAT DOES COLLABORATION REFER TO (IN IR)?
THE NOTION OF COLLABORATION
Collaboration
“A process through which parties who see different aspects of a problem can constructively explore
their differences and search for solutions that go beyond their own limited vision of what is possible.”
[Gray, 1989]
Collaboration
“Collaboration is a process in which autonomous actors interact through formal and informal
negotiation, jointly creating rules and structures governing their relationships and ways to act or decide
on the issues that brought them together; it is a process involving shared norms and mutually beneficial
interactions.” [Thomson and Perry, 2006]
Collaborative information seeking and retrieval
“The study of the systems and practices that enable individuals to collaborate during the seeking,
searching, and retrieval of information.” [Foster, 2006]
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6. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
WHAT DOES COLLABORATION REFER TO (IN IR)?
THE KNOWN FORMS OF COLLABORATION IN IR: ACCORDING TO THE NATURE OF ACTORS AND INTERACTIONS
• User-system collaboration
• User-user (and user-system) collaboration
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7. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
WHAT DOES COLLABORATION REFER TO (IN IR)?
THE KNOWN FORMS OF COLLABORATION IN IR: ACCORDING TO THE NATURE OF ACTORS AND INTERACTIONS
• User-system collaboration
What? Collaboration involves one user interacting with the system to solve an individual
search goal. The collaboration is system-mediated.
• User-user (and user-system) collaboration
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8. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
WHAT DOES COLLABORATION REFER TO (IN IR)?
THE KNOWN FORMS OF COLLABORATION IN IR: ACCORDING TO THE NATURE OF ACTORS AND INTERACTIONS
• User-system collaboration
What? Collaboration involves one user interacting with the system to solve an individual
search goal. The collaboration is system-mediated.
Why? Ensuring immediate or long-term search gains through one or multiple search
sessions respectively.
• User-user (and user-system) collaboration
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9. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
WHAT DOES COLLABORATION REFER TO (IN IR)?
THE KNOWN FORMS OF COLLABORATION IN IR: ACCORDING TO THE NATURE OF ACTORS AND INTERACTIONS
• User-system collaboration
What? Collaboration involves one user interacting with the system to solve an individual
search goal. The collaboration is system-mediated.
Why? Ensuring immediate or long-term search gains through one or multiple search
sessions respectively.
How? Exploiting relevance feedback, user’s personal and evolving behavioral data.
• User-user (and user-system) collaboration
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10. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
WHAT DOES COLLABORATION REFER TO (IN IR)?
THE KNOWN FORMS OF COLLABORATION IN IR: ACCORDING TO THE NATURE OF ACTORS AND INTERACTIONS
• User-system collaboration
What? Collaboration involves one user interacting with the system to solve an individual
search goal. The collaboration is system-mediated.
Why? Ensuring immediate or long-term search gains through one or multiple search
sessions respectively.
How? Exploiting relevance feedback, user’s personal and evolving behavioral data.
Main IR research branches: Interactive IR [Jansen et al., 2008, Lavrenko and Croft, 2001],
dynamic IR [Jin et al., 2013, Yang et al., 2016]
• User-user (and user-system) collaboration
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11. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
WHAT DOES COLLABORATION REFER TO (IN IR)?
THE KNOWN FORMS OF COLLABORATION IN IR: ACCORDING TO THE NATURE OF ACTORS AND INTERACTIONS
• User-system collaboration
What? Collaboration involves one user interacting with the system to solve an individual
search goal. The collaboration is system-mediated.
Why? Ensuring immediate or long-term search gains through one or multiple search
sessions respectively.
How? Exploiting relevance feedback, user’s personal and evolving behavioral data.
Main IR research branches: Interactive IR [Jansen et al., 2008, Lavrenko and Croft, 2001],
dynamic IR [Jin et al., 2013, Yang et al., 2016]
• User-user (and user-system) collaboration
What? Collaboration involves a group of users interacting intentionally or unintentionally
with each other and/or with the system to solve a shared/common search goal. The
collaboration is user-mediated and/or system-mediated.
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12. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
WHAT DOES COLLABORATION REFER TO (IN IR)?
THE KNOWN FORMS OF COLLABORATION IN IR: ACCORDING TO THE NATURE OF ACTORS AND INTERACTIONS
• User-system collaboration
What? Collaboration involves one user interacting with the system to solve an individual
search goal. The collaboration is system-mediated.
Why? Ensuring immediate or long-term search gains through one or multiple search
sessions respectively.
How? Exploiting relevance feedback, user’s personal and evolving behavioral data.
Main IR research branches: Interactive IR [Jansen et al., 2008, Lavrenko and Croft, 2001],
dynamic IR [Jin et al., 2013, Yang et al., 2016]
• User-user (and user-system) collaboration
What? Collaboration involves a group of users interacting intentionally or unintentionally
with each other and/or with the system to solve a shared/common search goal. The
collaboration is user-mediated and/or system-mediated.
Why? Ensuring long-term search gain and/or synergic effect
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13. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
WHAT DOES COLLABORATION REFER TO (IN IR)?
THE KNOWN FORMS OF COLLABORATION IN IR: ACCORDING TO THE NATURE OF ACTORS AND INTERACTIONS
• User-system collaboration
What? Collaboration involves one user interacting with the system to solve an individual
search goal. The collaboration is system-mediated.
Why? Ensuring immediate or long-term search gains through one or multiple search
sessions respectively.
How? Exploiting relevance feedback, user’s personal and evolving behavioral data.
Main IR research branches: Interactive IR [Jansen et al., 2008, Lavrenko and Croft, 2001],
dynamic IR [Jin et al., 2013, Yang et al., 2016]
• User-user (and user-system) collaboration
What? Collaboration involves a group of users interacting intentionally or unintentionally
with each other and/or with the system to solve a shared/common search goal. The
collaboration is user-mediated and/or system-mediated.
Why? Ensuring long-term search gain and/or synergic effect
How? Exploiting relevance feedback, using the group members’ social interactions, personal
and evolving behavioral data
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14. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
WHAT DOES COLLABORATION REFER TO (IN IR)?
THE KNOWN FORMS OF COLLABORATION IN IR: ACCORDING TO THE NATURE OF ACTORS AND INTERACTIONS
• User-system collaboration
What? Collaboration involves one user interacting with the system to solve an individual
search goal. The collaboration is system-mediated.
Why? Ensuring immediate or long-term search gains through one or multiple search
sessions respectively.
How? Exploiting relevance feedback, user’s personal and evolving behavioral data.
Main IR research branches: Interactive IR [Jansen et al., 2008, Lavrenko and Croft, 2001],
dynamic IR [Jin et al., 2013, Yang et al., 2016]
• User-user (and user-system) collaboration
What? Collaboration involves a group of users interacting intentionally or unintentionally
with each other and/or with the system to solve a shared/common search goal. The
collaboration is user-mediated and/or system-mediated.
Why? Ensuring long-term search gain and/or synergic effect
How? Exploiting relevance feedback, using the group members’ social interactions, personal
and evolving behavioral data
Main IR research branches: Social media IR
[Evans and Chi, 2008, Horowitz and Kamvar, 2010], collaborative filtering
[Sarwar et al., 2001], collaborative IR [Shah et al., 2010, Soulier et al., 2014b]
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15. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
WHAT DOES COLLABORATION REFER TO (IN IR)?
USER-SYSTEM COLLABORATION
• Conceptual models of IR:
Static IR: system-based IR, does not learn from users
eg. VSM [Salton, 1971], BM25 [Robertson et al., 1995] LM [Ponte and Croft, 1998], PageRank
and Hits [Brin and Page, 1998]
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16. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
WHAT DOES COLLABORATION REFER TO (IN IR)?
USER-SYSTEM COLLABORATION
• Conceptual models of IR:
Static IR: system-based IR, does not learn from users
eg. VSM [Salton, 1971], BM25 [Robertson et al., 1995] LM [Ponte and Croft, 1998], PageRank
and Hits [Brin and Page, 1998]
Interactive IR: exploiting feedback from users
eg. Rocchio [Rocchio, 1971], Relevance-based LM [Lavrenko and Croft, 2001]
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17. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
WHAT DOES COLLABORATION REFER TO (IN IR)?
USER-SYSTEM COLLABORATION
• Conceptual models of IR:
Static IR: system-based IR, does not learn from users
eg. VSM [Salton, 1971], BM25 [Robertson et al., 1995] LM [Ponte and Croft, 1998], PageRank
and Hits [Brin and Page, 1998]
Interactive IR: exploiting feedback from users
eg. Rocchio [Rocchio, 1971], Relevance-based LM [Lavrenko and Croft, 2001]
Dynamic IR: learning dynamically from past user-system interactions and predicts future
eg. iPRP [Fuhr, 2008], interactive exploratory search [Jin et al., 2013]
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18. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
WHAT DOES COLLABORATION REFER TO (IN IR)?
USER-SYSTEM COLLABORATION
• Conceptual models of IR:
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19. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
WHAT DOES COLLABORATION REFER TO (IN IR)?
USER-SYSTEM COLLABORATION
• Conceptual models of IR:
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20. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
WHAT DOES COLLABORATION REFER TO (IN IR)?
USER-USER (AND USER-SYSTEM) COLLABORATION
The social collaborative IR dimensions [Golovchinsky et al., 2009]:
• Intent: explicit vs. implicit search goal
• Depth of mediation: interface vs. algorithms (system)
• Concurrency: synchronous vs. asynchronous
• Location: co-located vs. remote
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21. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
WHAT DOES COLLABORATION REFER TO (IN IR)?
USER-USER (AND USER-SYSTEM) COLLABORATION
• Main IR research branches involving user-user collaboration
Collaborative IR Social media IR Collaborative
filtering
Intent Explicit Implicit Implicit
Depth of mediation Interface/Algorithms Algorithms Algorithms
Concurrency Synchronous/ Asynchronous Asynchronous
Asynchronous
Location Co-located/ Re-
mote
Remote Remote
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22. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
WHAT DOES COLLABORATION REFER TO (IN IR)?
USER-USER (AND USER-SYSTEM) COLLABORATION
• Collaborative IR [Foster, 2006, Golovchinsky et al., 2009]
Optimizing the synergic effect of co-searching
How?
Applying collaboration paradigms: division of labor,
sharing of knowledge, awareness
Supporting mediation between users
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23. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
WHAT DOES COLLABORATION REFER TO (IN IR)?
USER-USER (AND USER-SYSTEM) COLLABORATION
• Collaborative IR [Foster, 2006, Golovchinsky et al., 2009]
Optimizing the synergic effect of co-searching
How?
Applying collaboration paradigms: division of labor,
sharing of knowledge, awareness
Supporting mediation between users
• Collaborative filtering [Resnick et al., 1994, Ma et al., 2009]
Recommending search results using ratings/preferences
of other users
How?
Inferring user’s own preferences from other users’
preferences
Personalizing search results
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24. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
WHAT DOES COLLABORATION REFER TO (IN IR)?
USER-USER (AND USER-SYSTEM) COLLABORATION
• Collaborative IR [Foster, 2006, Golovchinsky et al., 2009]
Optimizing the synergic effect of co-searching
How?
Applying collaboration paradigms: division of labor,
sharing of knowledge, awareness
Supporting mediation between users
• Collaborative filtering [Resnick et al., 1994, Ma et al., 2009]
Recommending search results using ratings/preferences
of other users
How?
Inferring user’s own preferences from other users’
preferences
Personalizing search results
• Social Information Retrieval [Amer-Yahia et al., 2007, Pal and Counts, 2011]
Exploiting social media platforms to retrieve
document/users...
How?
Social network analysis (graph structure, information
diffusion, ...)
Integrating social-based features within the document
relevance scoring
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25. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
WHAT DOES COLLABORATION REFER TO (IN IR)?
USER-USER (AND USER-SYSTEM) COLLABORATION
• Collaborative IR [Foster, 2006, Golovchinsky et al., 2009]
Optimizing the synergic effect of co-searching
How?
Applying collaboration paradigms: division of labor,
sharing of knowledge, awareness
Supporting mediation between users
• Collaborative filtering [Resnick et al., 1994, Ma et al., 2009]
Recommending search results using ratings/preferences
of other users
How?
Inferring user’s own preferences from other users’
preferences
Personalizing search results
• Social Information Retrieval [Amer-Yahia et al., 2007, Pal and Counts, 2011]
Exploiting social media platforms to retrieve
document/users...
How?
Social network analysis (graph structure, information
diffusion, ...)
Integrating social-based features within the document
relevance scoring
Let’s have a more in-depth look on...
Collaborative Information Retrieval
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26. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
WHAT DOES COLLABORATION REFER TO (IN IR)?
USER-USER (AND USER-SYSTEM) COLLABORATION
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27. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
WHAT DOES COLLABORATION REFER TO (IN IR)?
THE 5WS OF THE COLLABORATION AS SEEN IN CIR [MORRIS AND TEEVAN, 2009, SHAH, 2010]
What?
Tasks: Complex, exploratory or fact-finding tasks, ...
Application domains: Bibliographic, medical, e-Discovery, academic search
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28. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
WHAT DOES COLLABORATION REFER TO (IN IR)?
THE 5WS OF THE COLLABORATION AS SEEN IN CIR [MORRIS AND TEEVAN, 2009, SHAH, 2010]
What?
Tasks: Complex, exploratory or fact-finding tasks, ...
Application domains: Bibliographic, medical, e-Discovery, academic search
Why?
Shared interests
Insufficient knowledge
Mutual beneficial goals
Division of labor
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29. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
WHAT DOES COLLABORATION REFER TO (IN IR)?
THE 5WS OF THE COLLABORATION AS SEEN IN CIR [MORRIS AND TEEVAN, 2009, SHAH, 2010]
What?
Tasks: Complex, exploratory or fact-finding tasks, ...
Application domains: Bibliographic, medical, e-Discovery, academic search
Why?
Shared interests
Insufficient knowledge
Mutual beneficial goals
Division of labor
Who?
Groups vs. Communities
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30. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
WHAT DOES COLLABORATION REFER TO (IN IR)?
THE 5WS OF THE COLLABORATION AS SEEN IN CIR [MORRIS AND TEEVAN, 2009, SHAH, 2010]
What?
Tasks: Complex, exploratory or fact-finding tasks, ...
Application domains: Bibliographic, medical, e-Discovery, academic search
Why?
Shared interests
Insufficient knowledge
Mutual beneficial goals
Division of labor
Who?
Groups vs. Communities
When?
Synchronous vs. Asynchronous
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31. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
WHAT DOES COLLABORATION REFER TO (IN IR)?
THE 5WS OF THE COLLABORATION AS SEEN IN CIR [MORRIS AND TEEVAN, 2009, SHAH, 2010]
What?
Tasks: Complex, exploratory or fact-finding tasks, ...
Application domains: Bibliographic, medical, e-Discovery, academic search
Why?
Shared interests
Insufficient knowledge
Mutual beneficial goals
Division of labor
Who?
Groups vs. Communities
When?
Synchronous vs. Asynchronous
Where?
Colocated vs. Remote
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32. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
WHAT DOES COLLABORATION REFER TO (IN IR)?
THE 5WS OF THE COLLABORATION AS SEEN IN CIR [MORRIS AND TEEVAN, 2009, SHAH, 2010]
What?
Tasks: Complex, exploratory or fact-finding tasks, ...
Application domains: Bibliographic, medical, e-Discovery, academic search
Why?
Shared interests
Insufficient knowledge
Mutual beneficial goals
Division of labor
Who?
Groups vs. Communities
When?
Synchronous vs. Asynchronous
Where?
Colocated vs. Remote
How?
Crowdsourcing
Implicit vs. Explicit intent
User mediation
System mediation
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33. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
CIR PARADIGMS [FOLEY AND SMEATON, 2010,
KELLY AND PAYNE, 2013, SHAH AND MARCHIONINI, 2010]
Division of labor • Role-based division of labor
• Document-based division of labor
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34. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
CIR PARADIGMS [FOLEY AND SMEATON, 2010,
KELLY AND PAYNE, 2013, SHAH AND MARCHIONINI, 2010]
Division of labor • Role-based division of labor
• Document-based division of labor
Sharing of knowledge • Communication and shared workspace
• Ranking based on relevance judgements
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35. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
CIR PARADIGMS [FOLEY AND SMEATON, 2010,
KELLY AND PAYNE, 2013, SHAH AND MARCHIONINI, 2010]
Division of labor • Role-based division of labor
• Document-based division of labor
Sharing of knowledge • Communication and shared workspace
• Ranking based on relevance judgements
Awareness • Collaborators’ actions
• Collaborators’ context
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36. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
THEORETICAL CHALLENGES
Typical structure of a collaborative search session
Challenges and issues
1 Learning from user and user-user past interactions
2 Adaptation to multi-faceted and multi-user contexts: skills, expertise, role, etc.
3 Aggregating relevant information nuggets
4 Supporting synchronous vs. asynchronous coordination
5 Modeling collaboration paradigms: division of labor, sharing of knowledge
6 Optimizing the search cost: balance in work (search) and group benefit (task outcome)
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37. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
PLAN
1. Collaboration in IS and IR
2. Collaborative IR techniques and models
Understanding Collaborative IR
Overview
System-mediated CIR models
User-Driven System-mediated CIR models
Roadmap
3. Emerging topics around collaboration
4. Open ideas
5. Discussion
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38. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
Objectives
1 Investigating user behavior and search patterns
Search processes [Shah and Gonz´alez-Ib´a˜nez, 2010, Yue et al., 2014]
Search tactics and practices [Hansen and J¨arvelin, 2005, Morris, 2013,
Amershi and Morris, 2008, Tao and Tombros, 2013, Capra, 2013]
Role assignment [Imazu et al., 2011, Tamine and Soulier, 2015]
2 Studying the impact of collaborative search settings on performance
Impact of collaboration on search performance
[Shah and Gonz´alez-Ib´a˜nez, 2011, Gonz´alez-Ib´a˜nez et al., 2013]
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39. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
GOAL: EXPLORING COLLABORATIVE SEARCH PROCESSES
• Study objective: Testing the feasibility of the Kuhlthau’s model of the information
seeking process in a collaborative information seeking situation
[Shah and Gonz´alez-Ib´a˜nez, 2010]
Stage Feeling Thoughts Actions
(Affective) (Cognitive)
Initiation Uncertainty General/Vague Actions
Selection Optimism
Exploration Confusion, Frustration, Doubt Seeking relevant informa-
tion
Formulation Clarity Narrowed, Clearer
Collection Sense of direction,
Confidence
Increased interest Seeking relevant or focused
information
Presentation Relief, Satisfaction or disap-
pointment
Clearer or focused
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40. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
GOAL: EXPLORING COLLABORATIVE SEARCH PROCESSES
• Study objective: Testing the feasibility of the Kuhlthau’s model in collaborative
information seeking situations [Shah and Gonz´alez-Ib´a˜nez, 2010]
Participants: 42 dyads, students or university employees who already did a collaborative work
together
System: Coagmento 1
Sessions: two sessions (S1, S2) running in 7 main phases: (1) tutorial on system, (2)
demographic questionnaire, (3) task description, (4) timely-bounded task achievement, (5)
post-questionnaire, (6) report compilation, (7) questionnaire and interview
Tasks: simulated work tasks.
eg. Task 1: Economic recession
”A leading newspaper has hired your team to create a comprehensive report on the causes and consequences
of the current economic recession in the US. As a part of your contract, you are required to collect all the
relevant information from any available online sources that you can find. ... Your report on this topic should
address the following issues: reasons behind this recession, effects on some major areas, such as health-care,
home ownership, and financial sector (stock market), unemployment statistics over a period of time, proposal
execution, and effects of the economy simulation plan, and people’s opinions and reactions on economy’s
downfall”
1
http://www.coagmento.org/
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41. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
GOAL: EXPLORING COLLABORATIVE SEARCH PROCESSES
• (Main) Study results:
The Kuhlthau’s model stages map collaborative tasks
• Initiation: number of chat
messages at the stage and
between stages
• Selection: number of chat
messages discussing the
strategy
• Exploration: number of
search queries
• Formulation: number of
visited webpages
• Collection: number of
collected webpages
• Presentation: number of
moving actions for
organizing collected
snippets
Figure: c [Shah and Gonz´alez-Ib´a˜nez, 2010]
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42. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
GOAL: EXPLORING COLLABORATIVE SEARCH PROCESSES
• (Main) Study results:
The Kuhlthau’s model stages map collaborative tasks
• Initiation: number of chat
messages at the stage and
between stages
• Selection: number of chat
messages discussing the
strategy
• Exploration: number of
search queries
• Formulation: number of
visited webpages
• Collection: number of
collected webpages
• Presentation: number of
moving actions for
organizing collected
snippets
Figure: c [Shah and Gonz´alez-Ib´a˜nez, 2010]
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43. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
GOAL: EXPLORING SEARCH TACTICS AND PRACTICES
• Study objective: Analyzing query (re)formulations and related term sources based on
participants’ actions [Yue et al., 2014]
Participants: 20 dyads, students who already knew each other in advance
System: Collabsearch
Session: one session running in running in 7 main phases: (1) tutorial on system, (2)
demographic questionnaire, (3) task description, (4) timely-bounded task achievement, (5)
post-questionnaire, (6) report compilation, (7) questionnaire and interview
Tasks: (T1) academic literature search, (T2) travel planning
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44. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
GOAL: EXPLORING SEARCH TACTICS AND PRACTICES
• (Main) Study results:
Individual action-based query reformulation (V, S, Q):
No (significant) new findings
Collaborative action-based query reformulation (SP, QP, C):
Influence of communication (C) is task-dependent.
Influence of collaborators’ queries (QP) is significantly higher than previous own queries (Q).
Less influence of collaborators’ workspace (SP) than own workspace (S).
• V: percentage of queries for which
participants viewed results, one
term originated from at least one
page
• S: percentage of queries for which
participants saved results, one term
originated from at least one page
• Q: percentage of queries with at
least one overlapping term with
previous queries
• SP: percentage of queries for which
at least one term originated from
collaborators’ workspace
• QP: percentage of queries for which
at least one term originated from
collaborators’ previous queries
• C: percentage of queries for which
at least one term originated from
collaborators’ communication
Figure: c [Yue et al., 2014]
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45. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
GOAL: STUDYING ROLE ASSIGNMENT
• Study objective: Understanding differences in users’ behavior in role-oriented and
non-role- oriented collaborative search sessions
Participants: 75 dyads, students who already knew each other
Settings: 25 dyads without roles, 50 dyads with roles (25 PM roles, 25 GS roles)
System: open-source Coagmento plugin
Session: one session running in 7 main phases: (1) tutorial on system, (2) demographic
questionnaire, (3) task description, (4) timely-bounded task achievement, (5)
post-questionnaire, (6) report compilation, (7) questionnaire and interview
Tasks: Three (3) exploratory search tasks, topics from Interactive TREC track2
Tamine, L. and Soulier, L. (2015). Understanding the impact of the
role factor in collaborative information retrieval. In Proceedings of
the ACM International on Conference on Information and
Knowledge Management, CIKM 15, pages 4352.
2
http://trec.nist.gov/data/t8i/t8i.html
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46. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
GOAL: STUDYING ROLE ASSIGNMENT
• (Main) Study results
Users with assigned roles significantly behave differently than users with roles
Mean(s.d.)
npq dt nf qn ql qo nbm
W/Role
GS
Group 1.71(1.06) 9.99(3.37) 58.52(27.13) 65.91(31.54) 4.64(1.11) 0.44(0.18) 20(14.50)
IGDiffp
-0.52 -3.47*** 1.30*** 2.09*** 1.16*** 0.14*** 2.23***
PM
Group 1.88(1.53) 10.47(3.11) 56.31(27.95) 56.31(27.95) 2.79(0.70) 0.39(0.08) 15(12.88)
IGDiffp
0.24*** 1.45*** -2.42*** -1.69*** 0.06*** 0-0.23*** 0.05***
W/oRole
Group 2.09(1.01) 13.16(3.92) 24.13(12.81) 43.58(16.28) 3.67(0.67) 0.45(0.10) 19(11.34)
p-value/GS *** *** *** *** *** ***
p-value/PM *** *** *** *** *** *** *
W/Role
vs.
W/oRole
ANOVA p-val.
** *** ** *
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47. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
GOAL: STUDYING ROLE ASSIGNMENT
• (Main) Study results
Early and high level of coordination of participants without role
Role drift for participants with PM role
(a) GS (b) PM (c) W/oRole
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48. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
GOAL: EVALUATING THE IMPACT OF COLLABORATION ON SEARCH PERFORMANCE
• Study objective: Evaluating the synergic effect of collaboration in information seeking
[Shah and Gonz´alez-Ib´a˜nez, 2011]
Participants: 70 participants, 10 as single users, 30 as dyads
Settings: C1 (single users), C2 (artificial formed teams), C3 (co-located teams, different
computers), C4 (co-located teams, same computer), C5 remotely located teams
System: Coagmento
Session: one session running in running in 7 main phases: (1) tutorial on system, (2)
demographic questionnaire, (3) task description, (4) timely-bounded task achievement, (5)
post-questionnaire, (6) report compilation, (7) questionnaire and interview
Tasks: One exploratory search task, topic ”gulf oil spill”
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49. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
GOAL: EVALUATING THE IMPACT OF COLLABORATION ON SEARCH PERFORMANCE
• (Main) Study results
Value of remote collaboration when the task has clear independent components
Remotely located teams able to leverage real interactions leading to synergic collaboration
Cognitive load in a collaborative setting not significantly higher than in an individual one
Figure: c [Shah and Gonz´alez-Ib´a˜nez, 2011]
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50. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
Lessons learned
• Small-group (critical mass) collaborative search is a common practice despite the lack of
specific tools
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51. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
Lessons learned
• Small-group (critical mass) collaborative search is a common practice despite the lack of
specific tools
• The whole is greater than the sum of all
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52. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
Lessons learned
• Small-group (critical mass) collaborative search is a common practice despite the lack of
specific tools
• The whole is greater than the sum of all
• Collaborative search behavior differs from individual search behavior while some
phases of theoretical models of individual search are still valid for collaborative search
28 / 102

53. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
Lessons learned
• Small-group (critical mass) collaborative search is a common practice despite the lack of
specific tools
• The whole is greater than the sum of all
• Collaborative search behavior differs from individual search behavior while some
phases of theoretical models of individual search are still valid for collaborative search
• Algorithmic mediation lowers the coordination cost
28 / 102

54. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
Lessons learned
• Small-group (critical mass) collaborative search is a common practice despite the lack of
specific tools
• The whole is greater than the sum of all
• Collaborative search behavior differs from individual search behavior while some
phases of theoretical models of individual search are still valid for collaborative search
• Algorithmic mediation lowers the coordination cost
• Roles structure the collaboration but do not guarantee performance improvement in
comparison to no roles
28 / 102

55. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
Lessons learned
• Small-group (critical mass) collaborative search is a common practice despite the lack of
specific tools
• The whole is greater than the sum of all
• Collaborative search behavior differs from individual search behavior while some
phases of theoretical models of individual search are still valid for collaborative search
• Algorithmic mediation lowers the coordination cost
• Roles structure the collaboration but do not guarantee performance improvement in
comparison to no roles
Design implications: revisit IR models and techniques
28 / 102

56. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
Lessons learned
• Small-group (critical mass) collaborative search is a common practice despite the lack of
specific tools
• The whole is greater than the sum of all
• Collaborative search behavior differs from individual search behavior while some
phases of theoretical models of individual search are still valid for collaborative search
• Algorithmic mediation lowers the coordination cost
• Roles structure the collaboration but do not guarantee performance improvement in
comparison to no roles
Design implications: revisit IR models and techniques
• Back to the axiomatic relevance hypothesis (Fang et al. 2011)
28 / 102

57. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
Lessons learned
• Small-group (critical mass) collaborative search is a common practice despite the lack of
specific tools
• The whole is greater than the sum of all
• Collaborative search behavior differs from individual search behavior while some
phases of theoretical models of individual search are still valid for collaborative search
• Algorithmic mediation lowers the coordination cost
• Roles structure the collaboration but do not guarantee performance improvement in
comparison to no roles
Design implications: revisit IR models and techniques
• Back to the axiomatic relevance hypothesis (Fang et al. 2011)
• Role as a novel variable in the IR models ?
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58. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
Lessons learned
• Small-group (critical mass) collaborative search is a common practice despite the lack of
specific tools
• The whole is greater than the sum of all
• Collaborative search behavior differs from individual search behavior while some
phases of theoretical models of individual search are still valid for collaborative search
• Algorithmic mediation lowers the coordination cost
• Roles structure the collaboration but do not guarantee performance improvement in
comparison to no roles
Design implications: revisit IR models and techniques
• Back to the axiomatic relevance hypothesis (Fang et al. 2011)
• Role as a novel variable in the IR models ?
• Learning to rank from user-system, user-user interactions within multi-session search
tasks?
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59. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
OVERVIEW OF IR MODELS AND TECHNIQUES
DESIGNING COLLABORATIVE IR MODELS: A YOUNG RESEARCH AREA
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60. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
OVERVIEW OF IR MODELS AND TECHNIQUES
DESIGNING COLLABORATIVE IR MODELS: A YOUNG RESEARCH AREA
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61. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
OVERVIEW OF IR MODELS AND TECHNIQUES
Collaborative IR models are based on algorithmic mediation:
Systems re-use users’ search activity data to mediate the search
• Data?
Click-through data, queries, viewed results, result rankings, ...
User-user communication
• Mediation?
Rooting/suggesting/enhance the queries
Building personalized document rankings
Automatically set-up division of labor
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62. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
OVERVIEW OF IR MODELS AND TECHNIQUES
Collaborative IR models are based on algorithmic mediation:
Systems re-use users’ search activity data to mediate the search
• Data?
Click-through data, queries, viewed results, result rankings, ...
User-user communication
• Mediation?
Rooting/suggesting/enhance the queries
Building personalized document rankings
Automatically set-up division of labor
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63. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
OVERVIEW OF IR MODELS AND TECHNIQUES
Notations
Notation Description
d Document
q Query
uj User j
g Collaborative group
ti term i
RSV(d, q) Relevance Status Value given (d,q)
N Document collection size
ni Number of documents in the collection in which term ti occurs
R Number of relevant documents in the collection
Ruj
Number of relevant documents in the collection for user uj
r
uj
i Number of relevant documents of user uj in which term ti occurs
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64. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
SYSTEM-MEDIATED CIR MODELS
USER GROUP-BASED MEDIATION
• Enhancing collaborative search with users’ context
[Morris et al., 2008, Foley and Smeaton, 2009a, Han et al., 2016]
Division of labor: dividing the work by non-overlapping browsing
Sharing of knowledge: exploiting personal relevance judgments, user’s authority
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65. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
SYSTEM-MEDIATED CIR MODELS
USER/GROUP-BASED MEDIATION: GROUPIZATION, SMART SPLITTING, GROUP-HIGHLIGHTING [MORRIS ET AL., 2008]
• Hypothesis setting: one or a few synchronous search query(ies)
• 3 approaches
Smart splitting: splitting top ranked web results using a round-robin technique,
personalized-splitting of remaining results (document ranking level)
Groupization: reusing individual personalization techniques towards groups (document ranking
level)
Hit Highlighting: highlighting user’s keywords (document browsing level)
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66. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
SYSTEM-MEDIATED CIR MODELS
USER/GROUP-BASED MEDIATION: SMART-SPLITTING [MORRIS ET AL., 2008]
Personalizing the document ranking: use the revisited BM25 weighting scheme
[Teevan et al., 2005]
RSV(d, q, uj) =
ti∈d∩q
wBM25(ti, uj) (1)
wBM25(ti, uj) = log
(ri + 0.5)(N − ni − Ruj + r
uj
i + 0.5)
(ni − r
uj
i + 0.5)(Ruj − r
uj
i + 0.5
(2)
N = (N + Ruj ) (3)
ni = ni + r
uj
i (4)
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67. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
SYSTEM-MEDIATED CIR MODELS
USER/GROUP-BASED MEDIATION: SMART-SPLITTING [MORRIS ET AL., 2008]
Example
Smart-splitting according to personalized scores.
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68. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
SYSTEM-MEDIATED CIR MODELS
USER/GROUP-BASED MEDIATION: COLLABORATIVE RELEVANCE FEEDBACK [FOLEY ET AL., 2008, FOLEY AND SMEATON, 2009B]
• Hypothesis setting: multiple independent synchronous search queries
• Collaborative relevance feedback: sharing collaborator’s explicit relevance judgments
Aggregate the partial user relevance scores
Compute the user’s authority weighting
Figure: c [Foley et al., 2008, Foley and Smeaton, 2009b]
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69. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
SYSTEM-MEDIATED CIR MODELS
USER/GROUP-BASED MEDIATION: COLLABORATIVE RELEVANCE FEEDBACK [FOLEY ET AL., 2008, FOLEY AND SMEATON, 2009B]
• A: Combining inputs of the RF process
puwo(ti) =
U−1
u=0
ruiwBM25(ti) (5)
wBM25(ti) = log
( U−1
u=0 αu
ru
i
Ru
)(1 − U−1
u=0 αu
ni − rui
N − Ru
)
( U−1
u=0 αu
ni − rui
N − Ru
)(1 − U−1
u=0 αu
rui
Ru
)
(6)
U−1
u=0
αu = 1 (7)
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70. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
SYSTEM-MEDIATED CIR MODELS
USER/GROUP-BASED MEDIATION: COLLABORATIVE RELEVANCE FEEDBACK [FOLEY ET AL., 2008, FOLEY AND SMEATON, 2009B]
• B: Combining outputs of the RF process
crwo(ti) =
U−1
u=0
αuwBM25(ti, u) (8)
wBM25(ti, u) = log
(
ru
i
Ru
)(1 −
ni − rui
N − Ru
)
(
ni − rui
N − Ru
)(1 −
rui
Ru
)
(9)
• C: Combining outputs of the ranking process
RSV(d, q) =
U−1
u=0
αuRSV(d, q, u) (10)
RSV(d, q, u) =
ti∈d∩q
wBM25(ti, u) (11)
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71. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
SYSTEM-MEDIATED CIR MODELS
USER/GROUP-BASED MEDIATION: CONTEXT-BASED COLLABORATIVE SEARCH [HAN ET AL., 2016]
• Exploit a 3-dimensional context:
Individual search history HQU: queries, results, bookmarks etc.)
Collaborative group HCL: collaborators’ search history (queries, results, bookmarks etc.)
Collaboration HCH: collaboration behavior chat (communication)
Figure: c [Han et al., 2016]
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72. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
SYSTEM-MEDIATED CIR MODELS
USER/GROUP-BASED MEDIATION: CONTEXT-BASED COLLABORATIVE SEARCH [HAN ET AL., 2016]
1 Building a document ranking RSV(q, d) and generating Rank(d)
2 Building the document language model θd
3 Building the context language model θHx
p(ti|Hx) =
1
K
K
k=1
p(ti|Xk) (12)
p(ti|Xk) =
nk
Xk
(13)
4 Computing the KL-divergence between θHx and θd
D(θd, θHx ) = −
ti
p(ti|θd) log p(ti|Hx) (14)
5 Learning to rank using pairwise features (Rank(d), D(θd, θHx))
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73. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
SYSTEM-MEDIATED CIR MODELS
ROLE-BASED MEDIATION
Enhancing collaborative search with user’s role
[Pickens et al., 2008, Shah et al., 2010, Soulier et al., 2014b]
• Division of labour: dividing the work based on users’ role peculiarities
• Sharing of knowledge: splitting the search results
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74. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
SYSTEM-MEDIATED CIR MODELS
ROLE-BASED MEDIATION: PROSPECTOR AND MINER [PICKENS ET AL., 2008]
• Prospector/Miner as functional roles supported by algorithms:
Prospector: ”..opens new fields for exploration into a data collection..”.
→ Draws ideas from algorithmically suggested query terms
Miner: ”..ensures that rich veins of information are explored...”.
→ Refines the search by judging highly ranked (unseen) documents
• Collaborative system architecture:
Algorithmic layer: functions
combining users’ search activities to
produce fitted outcomes to roles
(queries, document rankings).
Regulator layer: captures inputs
(search activities), calls the
appropriate functions of the
algorithmic layer, roots the outputs
of the algorithmic layer to the
appropriate role (user).
Figure: c [Pickens et al., 2008]
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75. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
SYSTEM-MEDIATED CIR MODELS
ROLE-BASED MEDIATION: PROSPECTOR AND MINER [PICKENS ET AL., 2008]
• Prospector function: The highly-relevant terms are suggested based on:
Score(ti) =
Lq∈L
wr(Lq)wf (Lq)rlf(ti; Lq) (15)
rlf(ti; Lq): number of documents in Lq in which ti occurs.
• Miner function: The unseen documents are queued according to
RSV(q, d) =
Lq∈L
wr(Lk)wf (Lq)borda(d; Lq) (16)
wr(Lq) =
|seen ∈ Lq|
|seen ∈ Lq|
(17)
wf (Lq) =
|rel ∈ Lq|
|seen ∈ Lq|
(18)
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76. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
SYSTEM-MEDIATED CIR MODELS
ROLE-BASED MEDIATION: GATHERER AND SURVEYOR [SHAH ET AL., 2010]
• Gatherer/Surveyor as functional roles supported by algorithms:
Gatherer: ”..scan results of joint search activity to discover most immediately relevant documents..”.
Surveyor: ”..browse a wider diversity of information to get a better understanding of the collection
being searched...”.
• Main functions:
Merging: merging (eg. CombSum) the
documents rankings of collaborators
Splitting: rooting the appropriate
documents according to roles (eg.
k-means clustering). High precision for
the Gatherer, high diversity for the
Surveyor
Figure: c [Shah et al., 2010]
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77. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
SYSTEM-MEDIATED CIR MODELS
ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE
Domain expert/Domain novice as knowledge-based roles supported by algorithms:
• Domain expert: ”..represent problems at deep structural levels and are generally interested in
discovering new associations among different aspects of items, or in delineating the advances in
a research focus surrounding the query topic..”.
• Domain novice: ”..represent problems in terms of surface or superficial aspects and are
generally interested in enhancing their learning about the general query topic..”.
Soulier, L., Tamine, L., and Bahsoun, W. (2014b). On domain
expertise-based roles in collaborative information retrieval.
Information Processing & Management (IP&M), 50(5):752774.
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78. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
SYSTEM-MEDIATED CIR MODELS
ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE [SOULIER ET AL., IP&M 2014B]
A two step algorithm:
1 Role-based document relevance scoring
Pk
(d|uj, q) ∝ Pk(uj|d) · Pk(d|q) (19)
P(q|θd) ∝ (ti,wiq)∈q[λP(ti|θd) + (1 − λ)P(ti|θC)]wiq (20)
Pk
(uj|d) ∝ P(π(uj)k|θd)
∝ (ti,wk
ij
)∈π(uj)k [λk
dj
P(ti|θd) + (1 − λk
dj
)P(ti|θC)]
wk
ij (21)
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79. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
SYSTEM-MEDIATED CIR MODELS
ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE [SOULIER ET AL., IP&M 2014B]
A two step algorithm:
1 Role-based document relevance scoring : parameter smoothing using evidence from
novelty and specificity
λk
dj =
Nov(d, D(uj)k) · Spec(d)β
maxd ∈D Nov(d, D(uj)k) · Spec(d )β
(22)
with β
1 if uj is an expert
−1 if uj is a novice
Novelty
Nov(d, D(uj)
k
) = mind ∈D(uj)k d(d, d ) (23)
Specificity
Spec(d) = avgti∈dspec(ti) = avgti∈d(
−log(
fdti
N )
α
) (24)
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80. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
SYSTEM-MEDIATED CIR MODELS
ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE [SOULIER ET AL., IP&M 2014B]
A two step algorithm:
1 Document allocation to collaborators
Classification-based on the Expectation Maximization algorithm (EM)
E-step: Document probability of belonging to collaborator’s class
P(Rj = 1|x
k
dj) =
αk
j · φk
j (xk
dj)
αk
j
· φk
j
(xk
dj
) + (1 − αk
j
) · ψk
j
(xk
dj
)
(25)
M-step : Parameter updating and likelihood estimation
Document allocation to collaborators by comparison of document ranks within collaborators’
lists
r
k
jj (d, δ
k
j , δ
k
j ) =
1 if rank(d, δk
j ) < rank(d, δk
j
)
0 otherwise
(26)
Division of labor: displaying distinct document lists between collaborators
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81. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
SYSTEM-MEDIATED CIR MODELS
ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE [SOULIER ET AL., IP&M 2014B]
Example
Applying the Expert/Novice CIR model
Let’s consider:
• A collaborative search session with two users u1 (expert) and u2 (novice).
• A shared information need I modeled through a query q.
• A collection of 10 documents and their associated relevance score with respect to the
shared information need I.
t1 t2 t3 t4
q 1 0 1 0
d1 2 3 1 1
d2 0 0 5 3
d3 2 1 7 6
d4 4 1 0 0
d5 2 0 0 0
d6 3 0 0 0
d7 7 1 1 1
d8 3 3 3 3
d9 1 4 5 0
d10 0 0 4 0
Weighting vectors of documents and query:
q = (0.5, 0, 0.5, 0) ;
d1 = (0.29, 0.43, 0.14, 0.14)
d2 = (0, 0, 0.63, 0.37)
d3 = (0.12, 0.06, 0.44, 0.28)
d4 = (0.8, 0.2, 0, 0)
d5 = (1, 0, 0, 0)
d6 = (0.3, 0, 0, 0.7)
d7 = (0.7, 0.1, 0.1, 0.1)
d8 = (0.25, 0.25, 0.25, 0.25)
d9 = (0.1, 0.4, 0.5, 0)
d10 = (0, 0, 1, 0).
Users profile: π(u1)0 = π(u2)0 = q
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82. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
SYSTEM-MEDIATED CIR MODELS
ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE [SOULIER ET AL., IP&M 2014B]
Example
Applying the Expert/Novice CIR model
RSV(q, d) rank(d) Spec(d)
d1 0.24 2 0.19
d2 0.02 7 0.23
d3 0.17 3 0.19
d4 0.03 6 0.15
d5 0.01 9 0.1
d6 0.02 8 0.1
d7 0.10 4 0.19
d8 0.31 1 0.19
d9 0.09 5 0.16
d10 0.01 10 0.15
• Iteration 0: Distributing top (6) documents to users: 3 most specific to the expert and
the 3 less specific to the novice.
Expert u1: l0
(u1, D0
ns) = {d8, d1, d3}
Novice u2: l0
(u2, D0
ns) = {d7, d9, d4}
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83. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
SYSTEM-MEDIATED CIR MODELS
ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE [SOULIER ET AL., IP&M 2014B]
Example
Applying the Expert/Novice CIR model
• Iteration 1. Let’s consider that user u2 selected document d4 (D(u1)1 = {d4}).
Building the user’s profile.
π(u1)1
= (0.5, 0, 0.5, 0)
π(u2)1
= ( 0.5+0.8
2 , 0.2
2 , 0.5
2 , 0) = (0.65, 0.1, 0.25, 0).
Estimating the document relevance with respect to collaborators.
For user u1 : P1
(d1|u1) = P1
(d1|q) ∗ P1
(u1|d1) = 0.24 ∗ 0.22 = 0.05.
P1
(d1|q) = 0.24.
P1
(u1|d1) = (0.85 ∗ 2
7
+ 0.15 ∗ 24
84
)0.05
+ (0.85 ∗ 3
7
+ 0.15 ∗ 13
84
)0
+ (0.85 ∗ 1
7
+ 0.15 ∗ 26
84
)0.05
+
(0.85 ∗ 1
7
+ 0.15 ∗ 21
84
)0
= 0.22
λ1
11 = 1∗0.19
0.23
= 0.85 where 0.19 expresses the specificity of document d1 and 1 is the document
novelty score, and 0.23 the normalization score.
The normalized
document scores
for each
collaborators are
the following:
P1
(d|u1) P2
(d|u2)
d1 0.23 0.28
d2 0 0.03
d3 0.16 0.11
d5 0.01 0.01
d6 0.03 0.02
d7 0.12 0.14
d8 0.34 0.34
d9 0.10 0.06
d10 0.01 0.01 51 / 102

84. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
SYSTEM-MEDIATED CIR MODELS
ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE [SOULIER ET AL., IP&M 2014B]
Example
Applying the Expert/Novice CIR model
• Iteration 1. Let’s consider that user u2 selected document d4 (D(u1)1 = {d4, d5}).
Building the user’s profile.
π(u1)1
= (0.5, 0, 0.5, 0)
π(u2)1
= ( 0.5+0.8
2 , 0.2
2 , 0.5
2 , 0) = (0.65, 0.1, 0.25, 0).
Estimating the document relevance with respect to collaborators.
For user u1 : P1
(d1|u1) = P1
(d1|q) ∗ P1
(u1|d1) = 0.24 ∗ 0.22 = 0.05. P1
(d1|q) = 0.24 since that the
user’s profile has not evolve.
λ1
11 = 1∗0.19
0.23
= 0.85 where 0.19 expresses the specificity of document d1 and 1 is the document
novelty score, and 0.23 the normalization score.
P1
(u1|d1) = (0.85 ∗ 2
7
+ 0.15 ∗ 24
84
)0.05
+ (0.85 ∗ 3
7
+ 0.15 ∗ 13
84
)0
+ (0.85 ∗ 1
7
+ 0.15 ∗ 26
84
)0.05
+
(0.85 ∗ 1
7
+ 0.15 ∗ 21
84
)0
= 0.22
The normalized
document scores
for each
collaborators are
the following:
P1
(d|u1) P2
(d|u2)
d1 0.23 0.28
d2 0 0.03
d3 0.16 0.11
d5 0.01 0.01
d6 0.03 0.02
d7 0.12 0.14
d8 0.34 0.34
d9 0.10 0.06
d10 0.01 0.01 52 / 102

85. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
USER-DRIVEN SYSTEM-MEDIATED CIR MODELS
MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH
Soulier, L., Shah, C., and Tamine, L. (2014a). User-driven
System-mediated Collaborative Information Retrieval. In
Proceedings of the Annual International SIGIR Conference on
Research and Development in Information Retrieval, SIGIR 14,
pages 485494. ACM.
53 / 102

86. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
USER-DRIVEN SYSTEM-MEDIATED CIR MODELS
MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH [SOULIER ET AL., SIGIR 2014A]
• Identifying users’ search behavior differences: estimating significance of differences
using the Kolmogrov-Smirnov test
• Characterizing users’ role
54 / 102

87. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
USER-DRIVEN SYSTEM-MEDIATED CIR MODELS
MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH [SOULIER ET AL., SIGIR 2014A]
• User’s roles modeled through patterns
Intuition
Number of visited documents
Number of submitted queries
Negative correlation
Role pattern PR1,2
Search feature kernel KR1,2
Search feature-based correlation matrix FR1,2
F
R1,2
=



1 if positively correlated
−1 if negatively correlated
0 otherwise
55 / 102

88. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
USER-DRIVEN SYSTEM-MEDIATED CIR MODELS
MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH [SOULIER ET AL., SIGIR 2014A]
• Categorizing users’ roles Ru
argmin R1,2
||FR1,2 C
(tl)
u1,u2
|| (27)
subject to :
∀
(fj,fk)∈K
R1,2 FR1,2 (fj, fk) − C
(tl)
u1,u2
(fj, fk)) > −1
where defined as:
FR1,2 (fj, fk) C
(tl)
u1,u2
(fj, fk) =
FR1,2 (fj, fk) − C
(tl)
u1,u2
(fj, fk) if FR1,2 (fj, fk) ∈ {−1; 1}
0 otherwise
• Personalizing the search: [Pickens et al., 2008, Shah, 2011].
56 / 102

89. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
USER-DRIVEN SYSTEM-MEDIATED CIR MODELS
MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH [SOULIER ET AL., SIGIR 2014A]
Example
Mining role of collaborators
A collaborative
search session
implies two users
u1 and u2 aiming
at identifying
information
dealing with
“global warming”.
We present search
actions of
collaborators for
the 5 first minutes
of the session.
u t actions additional information
u2 0 submitted query “global warming”
u1 1 submitted query “global warming”
u2 8 document d1: visited comment: “interesting”
u2 12 document d2: visited
u2 17 document d3: visited rated: 4/5
u2 19 document d4: visited
u1 30 submitted query “greenhouse effect”
u1 60 submitted query “global warming definition”
u1 63 document d20: visited rated: 3/5
u1 70 submitted query “global warming protection”
u1 75 document d21: visited
u2 100 document d5: visited rated: 5/5
u2 110 document d6: visited rated: 4/5
u2 120 document d7: visited
u1 130 submitted query “gas emission”
u1 132 document d22: visited rated: 4/5
u2 150 document d8: visited
u2 160 document d9: visited
u2 170 document d10: visited
u2 200 document d11: visited comment: “great”
u2 220 document d12: visited
u2 240 document d13: visited
u1 245 submitted query “global warming world protection”
u1 250 submitted query “causes temperature changes”
u1 298 submitted query “global warming world politics” 57 / 102

90. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
USER-DRIVEN SYSTEM-MEDIATED CIR MODELS
MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH [SOULIER ET AL., SIGIR 2014A]
Example
Mining role of collaborators: matching with role patterns
• Role patterns
Roles of reader-querier
F
Rread,querier =
1 −1
−1 1
, K
Rread,querier = {(Nq, Np)}
Role : (S
(tl)
u1
, S
(tl)
u2
, Rread,querier) → {(reader, querier), (querier, reader)}
(S
(tl)
u1
, S
(tl)
u2
, Rread,querier) →
(reader, querier) if S
(tl)
u1
(tl, Np) > S
(tl)
u2
(tl, Np)
(querier, reader) otherwise
Role of judge-querier
F
Rjudge,querier =
1 −1
−1 1
, K
Rjudge,querier = {(Nq, Nc)}
Role : (S
(tl)
u1
, S
(tl)
u2
, Rjudge,querier → {(judge, querier), (querier, judge)}
(S
(tl)
u1
, S
(tl)
u2
, Rjudge,querier) →
(judge, querier) if S
(tl)
u1
(tl, Nc) > S
(tl)
u2
(tl, Nc)
(querier, judge) otherwise
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91. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
USER-DRIVEN SYSTEM-MEDIATED CIR MODELS
MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH [SOULIER ET AL., SIGIR 2014A]
Example
Mining role of collaborators
• Track users’ behavior each 60 seconds
• F = {Nq, Nd, Nc, Nr}, respectively, number of queries, documents, comments, ratings.
• Users’ search behavior
S
(300)
u1
=





3 0 0 0
4 2 0 1
5 3 0 2
5 3 0 2
8 3 0 2





S
(300)
u2
=





1 4 1 1
1 7 1 3
1 10 1 3
1 13 2 3
1 13 2 3





• Collaborators’ search differences (matrix and Kolmogorov-Smirnov test)
∆
(300)
u1,u2
=





2 −4 −1 −1
3 −5 −1 −2
4 −7 −1 −1
4 −10 −2 −1
7 −10 −2 −1





- Number of queries : p
(tl)
u1,u2
(Nq) = 0.01348
- Number of pages : p
(tl)
u1,u2
(Nd) = 0.01348
- Number of comments : p
(tl)
u1,u2
(Nc) = 0.01348
- Number of ratings : p
(tl)
u1,u2
(Nr) = 0.08152
59 / 102

92. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
USER-DRIVEN SYSTEM-MEDIATED CIR MODELS
MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH [SOULIER ET AL., SIGIR 2014A]
Example
Mining role of collaborators: matching with role patterns
• Collaborators’ search action complementarity: correlation matrix between search
differences
C
(300)
u1,u2
=



1 −0.8186713 −0.731925 0
−0.8186713 1 0.9211324 0
−0.731925 0.9211324 1 0
0 0 0 0



• Role mining: comparing the role pattern with the sub-matrix of collaborators’
behaviors
Role of reader-querier
||F
Rread,querier C
(300)
u1,u2
|| =
0 −1 − (−0.8186713)
−1 − (−0.8186713) 0
=
0 0.183287
0.183287 0
The Frobenius norm is equals to:
√
0.1832872 = 0.183287.
Role of judge-querier
||F
Rjudge,querier C
(300)
u1,u2
|| =
0 −1 − (−0.731925)
−1 − (−0.731925) 0
=
0 0.268174
0.268174 0
The Frobenius norm is equals to:
√
0.2681742 = 0.268174.
→ Collaborators acts as reader/querier with u1 labeled as querier and u2 as reader (highest
Np).
60 / 102

93. 1. Collaboration in IS and IR Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas 5. Discussion
OVERVIEW OF IR MODELS AND TECHNIQUES
[FoleyandSmeaton,2009a]
[Morrisetal.,2008]“smart-splitting”
[Morrisetal.,2008]“groupization”
[Pickensetal.,2008]
[Shahetal.,2010]
[Soulieretal.,IP&M2014b]
[Soulieretal.,SIGIR2014a]
Relevance
collective
individual
Evidence source
feedback
interest
expertise
behavior
role
Paradigm
division of labor
sharing of knowledge
61 / 102

94. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion
PLAN
1. Collaboration in IS and IR
2. Collaborative IR techniques and models
3. Emerging topics around collaboration
Research fields and key critical questions
Social media-based collaborative information access
Crowdsourcing
4. Open ideas
5. Discussion
62 / 102

95. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion
COLLABORATION AND SOCIAL MEDIA-BASED IR
TWO SIDES OF THE SAME COIN?
• Quiz Time!
What are these red
points?
63 / 102

96. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion
COLLABORATION AND SOCIAL MEDIA-BASED IR
TWO SIDES OF THE SAME COIN?
• Quiz Time!
What are these red
points?
63 / 102

97. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion
COLLABORATION AND SOCIAL MEDIA-BASED IR
TWO SIDES OF THE SAME COIN?
• Quiz Time!
What are these red
points?
Who are the winners?
How much times?
63 / 102

98. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion
COLLABORATION AND SOCIAL MEDIA-BASED IR
TWO SIDES OF THE SAME COIN?
• Quiz Time!
What are these red
points?
Who are the winners?
How much times?
How do they win?
63 / 102

99. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion
COLLABORATION AND SOCIAL MEDIA-BASED IR
TWO SIDES OF THE SAME COIN?
• Quiz Time!
What are these red
points?
Who are the winners?
How much times?
How do they win?
63 / 102

100. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion
RESEARCH FIELDS AND KEY CRITICAL QUESTIONS
SOCIAL MEDIA-BASED INFORMATION ACCESS AND CROWDSOURCING
• Social media-based collaborative information access
Seeking, answering, sharing, bookmarking, and spreading information
Implicit or explicit intents (sharing, questioning, and/or answering)
→ Improving the search outcomes through social interactions
• Crowdsourcing
Solving a task according to constraints (budget, time, ...)
Defining, budgeting, and allocating the task
→ Identifying the right group of workers
Emerging issue
How to leverage from the wisdom of crowds?
64 / 102

101. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion
SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS
CONTEXT AND MOTIVATIONS
• Collaboration
Identifying and solving a shared complex problem
Creating and sharing knowledge within a work team
• Social media-based collaboration
Leveraging from the ”wisdom of the crowd”
Tasks: social question-answering, social search, real-time search
Emerging needs
• Understanding the cognitive behaviors of social users sharing, assessing and disseminating
information within social medias in order to achieve shared tasks leading to collective and
productive outcomes.
• Designing of a theoretical framework for collaborative IR within social environments
65 / 102

102. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion
SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS
CONTEXT AND MOTIVATIONS
• Communication during a natural disaster
People sent more than 20 million Tweets about the storm between Oct 27
& Nov 1. Terms tracked: ”sandy”, ”hurricane”, #sandy, #hurricane.
66 / 102

103. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion
SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS
CONTEXT AND MOTIVATIONS
67 / 102

104. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion
SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS
CONTEXT AND MOTIVATIONS
67 / 102

105. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion
SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS
CONTEXT AND MOTIVATIONS
• Analyzing seekers’ behavior on social media platforms
[Morris, 2013, Oeldorf-Hirsch et al., 2014, Teevan et al., 2011, Fuchs and Groh, 2015]
Investigating the motivation of using social media for search tasks
Analyzing information needs
Studying the scope of social interactions
Analyzing users’ satisfaction
68 / 102

106. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion
SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS
CONTEXT AND MOTIVATIONS
• Analyzing seekers’ behavior on social media platforms
[Morris, 2013, Oeldorf-Hirsch et al., 2014, Teevan et al., 2011, Fuchs and Groh, 2015]
Investigating the motivation of using social media for search tasks
Analyzing information needs
Studying the scope of social interactions
Analyzing users’ satisfaction
• Main results
Large audience and wide range of topics
[Harper et al., 2008, Jeong et al., 2013, Tamine et al., 2016]
Specific audience, expertise → trust, personalisation and contextualisation
[Morris et al., 2010]
Friendsourcing through people addressing (”@”, forward)
[Liu and Jansen, 2013, Teevan et al., 2011, Fuchs and Groh, 2015]
Communication, exchange, sensemaking
[Morris, 2013, Evans and Chi, 2010, Tamine et al., 2016]
68 / 102

107. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion
SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS
CONTEXT AND MOTIVATIONS
• Analyzing the potential of collaboration in social media platforms
Research questions
What are the structural and semantic patterns of explicit collaboration?
How groups of users with similar or complementary interests may be more likely to explicitly
collaborate with each other?
1 Hurricane #Sandy
(October 2012)
2 #Ebola virus epidemic
(2013-2014)
Lynda Tamine, Laure Soulier, Lamjed Ben Jabeur, Frdric Amblard,
Chihab Hanachi, Gilles Hubert, and Camille Roth. Social media-based
collaborative information access: Analysis of online crisis-related twitter
conversations. ACM conference on HyperText and hypermedia, 2016.
69 / 102

108. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion
SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS
CONTEXT AND MOTIVATIONS
• Analyzing the potential of collaboration in social media platforms [Tamine et al., 2016]
Building the conversation tree
70 / 102

109. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion
SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS
CONTEXT AND MOTIVATIONS
• Analyzing the potential of collaboration in social media platforms [Tamine et al., 2016]
Building the conversation tree
Analyzing the patterns of collaboration networks
70 / 102

110. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion
SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS
CONTEXT AND MOTIVATIONS
• Analyzing the potential of collaboration in social media platforms [Tamine et al., 2016]
Building the conversation tree
Analyzing the patterns of collaboration networks
Extracting collaboration topics through the LDA algorithm
Sandy: Insults; Prayers; Negative thoughts; Thanks
Ebola: Prevention; Victims and quarantine; Actions/Thoughts to people
70 / 102

111. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion
SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS
CONTEXT AND MOTIVATIONS
• Analyzing the potential of collaboration in social media platforms [Tamine et al., 2016]
Building the conversation tree
Analyzing the patterns of collaboration networks
Extracting collaboration topics through the LDA algorithm
Sandy: Insults; Prayers; Negative thoughts; Thanks
Ebola: Prevention; Victims and quarantine; Actions/Thoughts to people
Building the social-collaboration network over the whole graph
70 / 102

112. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion
SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS
CONTEXT AND MOTIVATIONS
• Analyzing seekers’ behavior on social media platforms
[Morris, 2013, Oeldorf-Hirsch et al., 2014, Teevan et al., 2011, Fuchs and Groh, 2015]
• Main results
Large audience and wide range of topics
[Harper et al., 2008, Jeong et al., 2013, Tamine et al., 2016]
Specific audience, expertise → trust, personalisation and contextualisation
[Morris et al., 2010]
Friendsourcing through people addressing (”@”, forward)
[Liu and Jansen, 2013, Teevan et al., 2011, Fuchs and Groh, 2015]
Communication, exchange, sensemaking
[Morris, 2013, Evans and Chi, 2010, Tamine et al., 2016]
71 / 102

113. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion
SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS
CONTEXT AND MOTIVATIONS
• Analyzing seekers’ behavior on social media platforms
[Morris, 2013, Oeldorf-Hirsch et al., 2014, Teevan et al., 2011, Fuchs and Groh, 2015]
• Main results
Large audience and wide range of topics
[Harper et al., 2008, Jeong et al., 2013, Tamine et al., 2016]
Specific audience, expertise → trust, personalisation and contextualisation
[Morris et al., 2010]
Friendsourcing through people addressing (”@”, forward)
[Liu and Jansen, 2013, Teevan et al., 2011, Fuchs and Groh, 2015]
Communication, exchange, sensemaking
[Morris, 2013, Evans and Chi, 2010, Tamine et al., 2016]
Limitations of social media information access
Majority of questions without response [Jeong et al., 2013, Paul et al., 2011]
Answers mostly provided by members of the immediate follower network
[Morris et al., 2010, Rzeszotarski et al., 2014]
Social and cognitive cost of friendsourcing (e.g., spent time and deployed effort)
[Horowitz and Kamvar, 2010, Morris, 2013].
71 / 102

114. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion
SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS
CONTEXT AND MOTIVATIONS
• Analyzing seekers’ behavior on social media platforms
[Morris, 2013, Oeldorf-Hirsch et al., 2014, Teevan et al., 2011, Fuchs and Groh, 2015]
• Main results
Large audience and wide range of topics
[Harper et al., 2008, Jeong et al., 2013, Tamine et al., 2016]
Specific audience, expertise → trust, personalisation and contextualisation
[Morris et al., 2010]
Friendsourcing through people addressing (”@”, forward)
[Liu and Jansen, 2013, Teevan et al., 2011, Fuchs and Groh, 2015]
Communication, exchange, sensemaking
[Morris, 2013, Evans and Chi, 2010, Tamine et al., 2016]
Limitations of social media information access
Majority of questions without response [Jeong et al., 2013, Paul et al., 2011]
Answers mostly provided by members of the immediate follower network
[Morris et al., 2010, Rzeszotarski et al., 2014]
Social and cognitive cost of friendsourcing (e.g., spent time and deployed effort)
[Horowitz and Kamvar, 2010, Morris, 2013].
Design implications
• Recommendation of collaborators (asking questions to crowd instead of followers)
• Enhancement of social awareness (creating social ties to active users)
71 / 102

115. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion
SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS
THE APPROACHES: MEDIATION AT THE USER LEVEL
• Recommending users
Expertise and interests
[Pal and Counts, 2011, Balog et al., 2012, Ghosh, 2012, Bozzon et al., 2012, Hecht et al., 2012,
Wang et al., 2013, Gong et al., 2015, Ranganath et al., 2015]
Social availability/Responsiveness
[Horowitz and Kamvar, 2010, Sung et al., 2013, Ranganath et al., 2015]
Social activity [Horowitz and Kamvar, 2010, Wang et al., 2013, Ranganath et al., 2015]
Users’ connectedness [Horowitz and Kamvar, 2010]
• Identifying the right group of collaborators
Expertise and interests
[Chang and Pal, 2013, Nushi et al., 2015, Ranganath et al., 2015, Soulier et al., 2016]
Social availability/Responsiveness
[Chang and Pal, 2013, Mahmud et al., 2013, Nushi et al., 2015]
Social activity [Chang and Pal, 2013, Mahmud et al., 2013, Nushi et al., 2015,
Ranganath et al., 2015, Soulier et al., 2016]
Users’ connectedness [Ranganath et al., 2015]
Personality/Compatibility [Chang and Pal, 2013, Mahmud et al., 2013]
Optimization of the overall response [Mahmud et al., 2013, Soulier et al., 2016]
Complementarity of users’ skills [Nushi et al., 2015, Soulier et al., 2016]
72 / 102

116. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion
SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS
THE APPROACHES: MEDIATION AT THE USER LEVEL
• Recommending users
Expertise and interests [Pal and Counts, 2011, Balog et al., 2012, Ghosh, 2012,
Bozzon et al., 2012, Hecht et al., 2012, Wang et al., 2013, Gong et al., 2015]
Social availability/Responsiveness [Horowitz and Kamvar, 2010, Sung et al., 2013]
Social activity [Horowitz and Kamvar, 2010, Wang et al., 2013]
Users’ connectedness [Horowitz and Kamvar, 2010]
73 / 102

117. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion
SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS
RECOMMENDING USERS: AARDVARK [HOROWITZ AND KAMVAR, 2010]
Aardvark [Horowitz and Kamvar, 2010]
• The village paradigm: towards a social dissemination of knowledge
Information is passed from person to person
Finding the right person rather than the right document
s(ui, uj, q) = p(ui, uj) · p(ui, q) (28)
= p(ui|uj)
t∈T
p(ui|t)(t|q) (29)
74 / 102

118. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion
SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS
RECOMMENDING USERS: SEARCHBUDDIES [HECHT ET AL., 2012]
SearchBuddies [Hecht et al., 2012]
• A crowd-powered socially embedded search engine
• Leveraging users’ personal network to reach the good people/information
• Soshul Butterflie: Recommending people
Named entity extractors (Wikipedia,
openNLP, Yahoo! Placemaker)
Matching with the expertise of asker’s
friends (place and interests)
Answers built using predefined
templates
Figure: c [Hecht et al., 2012]
• Investigaetore: Recommending urls
Filtering using a whitelist of domains
Retrieving the top results
Figure: c [Hecht et al., 2012]
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SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS
RECOMMENDING USERS: WHOM TO MENTION [WANG ET AL., 2013]
Whom to mention? [Wang et al., 2013]
• Identifying potential information spreaders
• Improving tweet visibility and creating social interactions
• Overpassing the local network (followers) to further cascade diffusion
• Learning-to-rank algorithm (Support Vector Regression):
User interest (user profiling with recent tweets and score based on TF-IDF)
User social tie (strength and topicality of the retweet relationship between two users)
User influence (number of followers, number of received retweets/replies, and coverage of
posted tweets)
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SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS
THE APPROACHES: MEDIATION AT THE USER LEVEL
• Identifying the right group of collaborators
Expertise and interests [Nushi et al., 2015, Soulier et al., 2016, Chang and Pal, 2013]
Social availability/Responsiveness
[Chang and Pal, 2013, Mahmud et al., 2013, Nushi et al., 2015]
Social activity
[Chang and Pal, 2013, Mahmud et al., 2013, Nushi et al., 2015, Soulier et al., 2016]
Personality/Compatibility [Chang and Pal, 2013, Mahmud et al., 2013]
Optimization of the overall response [Mahmud et al., 2013, Soulier et al., 2016]
Complementarity of users’ skills [Nushi et al., 2015, Soulier et al., 2016]
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SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS
RECOMMENDING THE RIGHT GROUP OF COLLABORATORS: CROWDSTAR [NUSHI ET AL., 2015]
CrowdSTAR: A social Task Routing Framework for Online Communities [Nushi et al., 2015]
• Identifying a group of users (a crowd)
• Budgeted model (number of users) modeled through a crowd skyline
• Use case: peer-to-peer routing or answer provider
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SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS
RECOMMENDING THE RIGHT GROUP OF COLLABORATORS: CROWDSTAR [NUSHI ET AL., 2015]
CrowdSTAR: A social Task Routing Framework for Online Communities [Nushi et al., 2015]
• Identifying a group of users (a crowd)
• Budgeted model (number of users) modeled through a crowd skyline
• Use case: peer-to-peer routing or answer provider
• User utility model
Topic-dependent
Dynamic with users’ actions (answers, posts) and time (last actions)
User’s social network dependent
Figure: c [Nushi et al., 2015]
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SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS
RECOMMENDING THE RIGHT GROUP OF COLLABORATORS: CROWDSTAR [NUSHI ET AL., 2015]
• Routing questions within a crowd
Trade-off between users’ utility model and ”dominating” users (crowd skyline)
Pruning algorithm discarding the search space of the best user not yet included
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SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS
RECOMMENDING THE RIGHT GROUP OF COLLABORATORS: CROWDSTAR [NUSHI ET AL., 2015]
• Routing questions within a crowd
Trade-off between users’ utility model and ”dominating” users (crowd skyline)
Pruning algorithm discarding the search space of the best user not yet included
• Routing questions to multipe crowds
Crowd summary
Summary(c, t, f) =
u∈skyline(c,t) f(c, t, u)
|skyline(c, t)|
Crowd ranking
Score(c, t) =
f∈F
(wf · Summary(c, t, f))
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SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS
BUILDING THE RIGHT GROUP OF COLLABORATORS: ANSWERING TWITTER QUESTIONS [SOULIER ET AL., 2016]
Anwsering Twitter Question [Soulier et al., 2016]
• Identifying a group of users willing to overpass the local social network
• Gathering diverse pieces of information
• Maximization of the group entropy
Soulier, L., Tamine, L., and
Nguyen, G-H. (2016).
Answering Twitter
Questions: a Model for
Recommending Answerers
through Social Collaboration,
ACM CIKM 2016.
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126. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion
SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS
BUILDING THE RIGHT GROUP OF COLLABORATORS: ANSWERING TWITTER QUESTIONS [SOULIER ET AL., 2016]
• Learning the collaboration likelihood
Hypotheses:
On Twitter, collaboration between users is noted by the @ symbol
[Ehrlich and Shami, 2010, Honey and Herring, 2009]
Trust and authority enable to improve the effectiveness of the collaboration [McNally et al., 2013]
Collaboration is a structured search process in which users might or might not be complementary
[Sonnenwald et al., 2004, Soulier et al., 2014a]
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SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESS
BUILDING THE RIGHT GROUP OF COLLABORATORS: ANSWERING TWITTER QUESTIONS [SOULIER ET AL., 2016]
• Learning the collaboration likelihood
Hypotheses:
On Twitter, collaboration between users is noted by the @ symbol
[Ehrlich and Shami, 2010, Honey and Herring, 2009]
Trust and authority enable to improve the effectiveness of the collaboration [McNally et al., 2013]
Collaboration is a structured search process in which users might or might not be complementary
[Sonnenwald et al., 2004, Soulier et al., 2014a]
• Recommending a collaborative group
Identifying candidate collaborators through a temporal ranking model
[Berberich and Bedathur, 2013]
Extracting the collaborator group
Recursive decrementation of candidate collaborators through the information gain metric
Maximizing entropy equivalent to minimizing the information gain [Quinlan, 1986]
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128. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion
RESEARCH FIELDS AND KEY CRITICAL QUESTIONS
SOCIAL MEDIA-BASED INFORMATION ACCESS AND CROWDSOURCING
• Social media-based collaborative information access
Seeking, answering, sharing, bookmarking, and spreading information
Implicit or explicit intents (sharing, questioning, and/or answering)
→ Improving the search outcomes through social interactions
• Crowdsourcing
Solving a task according to constraints (budget, time, ...)
Defining, budgeting, and allocating the task
→ Identifying the right group of workers
Emerging issue
How to leverage from the wisdom of crowds?
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CROWDSOURCING
CONTEXT AND MOTIVATIONS
• Crowdsourcing platforms
Leveraging from the ”wisdom of the crowd” to perform a task [Li et al., 2014]
A step forward for improving the quality of search engines for specific tasks requiring high
quality data, assessments or labels [Abraham et al., 2016]
Large-scale experimental evaluation reducing the cost of running and analyzing experiments
[Abraham et al., 2016]
Cheap, fast, reliable mechanism to gather labels [Snow et al., 2008]
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CROWDSOURCING
CONTEXT AND MOTIVATIONS
Main issues
• Optimizing the search task
How to agregate answers over workers? → voting functions, stopping rules
[Abraham et al., 2016]
How to optimize the work between users? → number of workers [Abraham et al., 2016], task
allocation [Basu Roy et al., 2015, Karger et al., 2011], group recommendation
[Li et al., 2014, Rahman et al., 2015]
• Evaluating the quality of answers [Oleson et al., 2011, Blanco et al., 2011, Abraham et al., 2016]
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131. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion
CROWDSOURCING
RECOMMENDING THE RIGHT GROUP OF WORKERS
The wisdom of minority [Li et al., 2014]
• Leveraging from the ”minority of the crowd” to optimize the task
Figure: c [Li et al., 2014]
• Group discovery algorithm based on effect of features on users’ information gain
Intuition Information gain metric
wu: probability that user u provides the right
response
1−wu
L−1
: probability that user u does not provide
the right response
IG(u, L) = lnL + wulnwu + (1 − wu)ln
1 − wu
L − 1
(30)
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132. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration Open ideas 5. Discussion
PLAN
1. Collaboration in IS and IR
2. Collaborative IR techniques and models
3. Emerging topics around collaboration
4. Open ideas
5. Discussion
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133. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration Open ideas 5. Discussion
OPEN IDEAS
OPEN IDEAS
• Towards a novel probabilistic framework of relevance for CIR
What is a ”good ranking” with regard to the expected synergic effect of collaboration?
• Towards an axiomatic approach of relevance for CIR
Are IR heuristics similar to CIR heuristics?
Can relevance towards a group be modeled by a set of formally defined constraints on a
retrieval function?
• Dynamic IR models for CIR
How to optimize long-term gains over multiple users, user-user interactions, user-system
interactions and multi-search sessions?
How to formalize the division of labor through the evolving of users’ information needs over
time?
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134. 1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas Discussion
PLAN
1. Collaboration in IS and IR
2. Collaborative IR techniques and models
3. Emerging topics around collaboration
4. Open ideas
5. Discussion
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DISCUSSION
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REFERENCES XII
Tamine, L., Soulier, L., Jabeur, L. B., Amblard, F., Hanachi, C., Hubert, G., and Roth, C. (2016).
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