Collaborative Information Retrieval: Frameworks, Theoretical Models and Emerging Topics - Tutorial...

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 TaminePaul Sabatier UniversityIRIT, Toulouse - France

Laure SoulierPierre and Marie Curie UniversityLIP6, Paris - France

Monday 17th October, 2016

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GOAL OF THE TUTORIAL

• Introducing the notion of collaboration and the different forms of collaborativeinformation retrieval and seeking

I Positioning collaborative IR within the major theoretical approaches of IRI Identifying the Collaborative IR challenges

• Presenting state-of-the-art theoretical models for collaborative IRI Identifying the key factors affecting the design of collaborative IR modelsI Reviewing major research progress in the area

• Discussing promising research directionsI Bridging the gap between two (close) research branches: collaborative IR and social media IR

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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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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 IRWhat does collaboration refer to (in IR)?Collaborative information retrieval paradigmsCollaborative 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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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 exploretheir 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 informalnegotiation, jointly creating rules and structures governing their relationships and ways to act or decideon the issues that brought them together; it is a process involving shared norms and mutually beneficialinteractions.” [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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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

I What? Collaboration involves one user interacting with the system to solve an individualsearch goal. The collaboration is system-mediated.

I Why? Ensuring immediate or long-term search gains through one or multiple searchsessions respectively.

I How? Exploiting relevance feedback, user’s personal and evolving behavioral data.I 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

I What? Collaboration involves a group of users interacting intentionally or unintentionallywith each other and/or with the system to solve a shared/common search goal. Thecollaboration is user-mediated and/or system-mediated.

I Why? Ensuring long-term search gain and/or synergic effectI How? Exploiting relevance feedback, using the group members’ social interactions, personal

and evolving behavioral dataI 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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• User-system collaborationI What? Collaboration involves one user interacting with the system to solve an individual

search goal. The collaboration is system-mediated.

I Why? Ensuring immediate or long-term search gains through one or multiple searchsessions respectively.








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search goal. The collaboration is system-mediated.I Why? Ensuring immediate or long-term search gains through one or multiple search

sessions respectively.








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sessions respectively.I How? Exploiting relevance feedback, user’s personal and evolving behavioral data.

I Main IR research branches: Interactive IR [Jansen et al., 2008, Lavrenko and Croft, 2001],dynamic IR [Jin et al., 2013, Yang et al., 2016]






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sessions respectively.I How? Exploiting relevance feedback, user’s personal and evolving behavioral data.I Main IR research branches: Interactive IR [Jansen et al., 2008, Lavrenko and Croft, 2001],







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• User-user (and user-system) collaborationI 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. Thecollaboration is user-mediated and/or system-mediated.




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I Why? Ensuring long-term search gain and/or synergic effect

I How? Exploiting relevance feedback, using the group members’ social interactions, personaland evolving behavioral data

I 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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and evolving behavioral data

I 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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WHAT DOES COLLABORATION REFER TO (IN IR)?USER-SYSTEM COLLABORATION

• Conceptual models of IR:I Static IR: system-based IR, does not learn from users

eg. VSM [Salton, 1971], BM25 [Robertson et al., 1995] LM [Ponte and Croft, 1998], PageRankand Hits [Brin and Page, 1998]

I Interactive IR: exploiting feedback from userseg. Rocchio [Rocchio, 1971], Relevance-based LM [Lavrenko and Croft, 2001]

I Dynamic IR: learning dynamically from past user-system interactions and predicts futureeg. iPRP [Fuhr, 2008], interactive exploratory search [Jin et al., 2013]

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• Conceptual models of IR:

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• Conceptual models of IR:

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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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• Main IR research branches involving user-user collaboration

Collaborative IR Social media IR Collaborativefiltering

Intent Explicit Implicit ImplicitDepth of mediation Interface/Algorithms Algorithms AlgorithmsConcurrency Synchronous/ Asynchronous Asynchronous

AsynchronousLocation Co-located/ Re-

moteRemote Remote

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• Collaborative IR [Foster, 2006, Golovchinsky et al., 2009]I Optimizing the synergic effect of co-searchingI How?

I Applying collaboration paradigms: division of labor,sharing of knowledge, awareness

I Supporting mediation between users

• Collaborative filtering [Resnick et al., 1994, Ma et al., 2009]I Recommending search results using ratings/preferences

of other usersI How?

I Inferring user’s own preferences from other users’preferences

I Personalizing search results• Social Information Retrieval [Amer-Yahia et al., 2007, Pal and Counts, 2011]

I Exploiting social media platforms to retrievedocument/users...

I How?I Social network analysis (graph structure, information

diffusion, ...)I 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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I Supporting mediation between users• Collaborative filtering [Resnick et al., 1994, Ma et al., 2009]

I Recommending search results using ratings/preferencesof other users

I How?I Inferring user’s own preferences from other users’

preferencesI Personalizing search results

• Social Information Retrieval [Amer-Yahia et al., 2007, Pal and Counts, 2011]I Exploiting social media platforms to retrieve

document/users...I How?

I Social network analysis (graph structure, informationdiffusion, ...)

I Integrating social-based features within the documentrelevance scoring



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Collaborative Information Retrieval11 / 102



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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 interestsInsufficient knowledge

Mutual beneficial goalsDivision of labor

Who?

Groups vs. Communities

When?

Synchronous vs. Asynchronous

Where?

Colocated vs. Remote

How?

CrowdsourcingImplicit vs. Explicit intent

User mediationSystem mediation

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What?


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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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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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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 modelsUnderstanding Collaborative IROverviewSystem-mediated CIR modelsUser-Driven System-mediated CIR modelsRoadmap


4. Open ideas

5. Discussion

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EMPIRICAL UNDERSTANDING OF CIR

Objectives

1 Investigating user behavior and search patternsI Search processes [Shah and Gonzalez-Ibanez, 2010, Yue et al., 2014]I Search tactics and practices [Hansen and Jarvelin, 2005, Morris, 2013,

Amershi and Morris, 2008, Tao and Tombros, 2013, Capra, 2013]I Role assignment [Imazu et al., 2011, Tamine and Soulier, 2015]

2 Studying the impact of collaborative search settings on performanceI Impact of collaboration on search performance

[Shah and Gonzalez-Ibanez, 2011, Gonzalez-Ibanez et al., 2013]

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EMPIRICAL UNDERSTANDING OF CIRGOAL: EXPLORING COLLABORATIVE SEARCH PROCESSES

• Study objective: Testing the feasibility of the Kuhlthau’s model of the informationseeking process in a collaborative information seeking situation[Shah and Gonzalez-Ibanez, 2010]

Stage Feeling Thoughts Actions(Affective) (Cognitive)

Initiation Uncertainty General/Vague ActionsSelection OptimismExploration Confusion, Frustration, Doubt Seeking relevant informa-

tionFormulation Clarity Narrowed, ClearerCollection Sense of direction,

ConfidenceIncreased interest Seeking relevant or focused

informationPresentation Relief, Satisfaction or disap-

pointmentClearer or focused

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• Study objective: Testing the feasibility of the Kuhlthau’s model in collaborativeinformation seeking situations [Shah and Gonzalez-Ibanez, 2010]

I Participants: 42 dyads, students or university employees who already did a collaborative worktogether

I System: Coagmento 1

I 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

I 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 consequencesof the current economic recession in the US. As a part of your contract, you are required to collect all therelevant information from any available online sources that you can find. ... Your report on this topic shouldaddress 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, proposalexecution, and effects of the economy simulation plan, and people’s opinions and reactions on economy’sdownfall”

1http://www.coagmento.org/19 / 102



• (Main) Study results:I The Kuhlthau’s model stages map collaborative tasks

• Initiation: number of chatmessages at the stage andbetween stages

• Selection: number of chatmessages discussing thestrategy

• Exploration: number ofsearch queries

• Formulation: number ofvisited webpages

• Collection: number ofcollected webpages

• Presentation: number ofmoving actions fororganizing collectedsnippets

Figure: c©[Shah and Gonzalez-Ibanez, 2010]

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• (Main) Study results:I The Kuhlthau’s model stages map collaborative tasks

• Initiation: number of chatmessages at the stage andbetween stages

• Selection: number of chatmessages discussing thestrategy

• Exploration: number ofsearch queries

• Formulation: number ofvisited webpages

• Collection: number ofcollected webpages

• Presentation: number ofmoving actions fororganizing collectedsnippets


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EMPIRICAL UNDERSTANDING OF CIRGOAL: EXPLORING SEARCH TACTICS AND PRACTICES

• Study objective: Analyzing query (re)formulations and related term sources based onparticipants’ actions [Yue et al., 2014]

I Participants: 20 dyads, students who already knew each other in advanceI System: CollabsearchI 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

I Tasks: (T1) academic literature search, (T2) travel planning

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EMPIRICAL UNDERSTANDING OF CIRGOAL: EXPLORING SEARCH TACTICS AND PRACTICES

• (Main) Study results:I Individual action-based query reformulation (V, S, Q):

I No (significant) new findingsI Collaborative action-based query reformulation (SP, QP, C):

I Influence of communication (C) is task-dependent.I Influence of collaborators’ queries (QP) is significantly higher than previous own queries (Q).I Less influence of collaborators’ workspace (SP) than own workspace (S).

• V: percentage of queries for whichparticipants viewed results, oneterm originated from at least onepage

• S: percentage of queries for whichparticipants saved results, one termoriginated from at least one page

• Q: percentage of queries with atleast one overlapping term withprevious queries

• SP: percentage of queries for whichat least one term originated fromcollaborators’ workspace

• QP: percentage of queries for whichat least one term originated fromcollaborators’ previous queries

• C: percentage of queries for whichat least one term originated fromcollaborators’ communication Figure: c©[Yue et al., 2014]

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EMPIRICAL UNDERSTANDING OF CIRGOAL: STUDYING ROLE ASSIGNMENT

• Study objective: Understanding differences in users’ behavior in role-oriented andnon-role- oriented collaborative search sessions

I Participants: 75 dyads, students who already knew each otherI Settings: 25 dyads without roles, 50 dyads with roles (25 PM roles, 25 GS roles)I System: open-source Coagmento pluginI 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

I Tasks: Three (3) exploratory search tasks, topics from Interactive TREC track2

Tamine, L. and Soulier, L. (2015). Understanding the impact of therole factor in collaborative information retrieval. In Proceedings ofthe ACM International on Conference on Information andKnowledge Management, CIKM 15, pages 4352.

2http://trec.nist.gov/data/t8i/t8i.html23 / 102



• (Main) Study resultsI Users with assigned roles significantly behave differently than users with roles

Mean(s.d.)npq dt nf qn ql qo nbm

W/RoleGS 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)

IGDiff p -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)IGDiff p 0.24*** 1.45*** -2.42*** -1.69*** 0.06*** 0-0.23*** 0.05***

W/oRoleGroup 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/Rolevs.W/oRole

ANOVA p-val. ** *** ** *

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• (Main) Study resultsI Early and high level of coordination of participants without roleI Role drift for participants with PM role

(a) GS (b) PM (c) W/oRole

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EMPIRICAL UNDERSTANDING OF CIRGOAL: EVALUATING THE IMPACT OF COLLABORATION ON SEARCH PERFORMANCE

• Study objective: Evaluating the synergic effect of collaboration in information seeking[Shah and Gonzalez-Ibanez, 2011]

I Participants: 70 participants, 10 as single users, 30 as dyadsI Settings: C1 (single users), C2 (artificial formed teams), C3 (co-located teams, different

computers), C4 (co-located teams, same computer), C5 remotely located teamsI System: CoagmentoI 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

I Tasks: One exploratory search task, topic ”gulf oil spill”

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EMPIRICAL UNDERSTANDING OF CIRGOAL: EVALUATING THE IMPACT OF COLLABORATION ON SEARCH PERFORMANCE

• (Main) Study resultsI Value of remote collaboration when the task has clear independent componentsI Remotely located teams able to leverage real interactions leading to synergic collaborationI Cognitive load in a collaborative setting not significantly higher than in an individual one


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Lessons learned

• Small-group (critical mass) collaborative search is a common practice despite the lack ofspecific 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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Lessons learned


• The whole is greater than the sum of all

• Collaborative search behavior differs from individual search behavior while somephases 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




tasks?

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Lessons learned



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




tasks?

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Lessons learned



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 incomparison to no roles



tasks?

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Lessons learned







tasks?

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Lessons learned







tasks?

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Lessons learned






• 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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Lessons learned






• 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 searchtasks?

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Lessons learned







tasks?

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OVERVIEW OF IR MODELS AND TECHNIQUESDESIGNING COLLABORATIVE IR MODELS: A YOUNG RESEARCH AREA

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OVERVIEW OF IR MODELS AND TECHNIQUESDESIGNING COLLABORATIVE IR MODELS: A YOUNG RESEARCH AREA

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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?

I Click-through data, queries, viewed results, result rankings, ...I User-user communication

• Mediation?I Rooting/suggesting/enhance the queriesI Building personalized document rankingsI Automatically set-up division of labor

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Collaborative IR models are based on algorithmic mediation:Systems re-use users’ search activity data to mediate the search• Data?

I Click-through data, queries, viewed results, result rankings, ...I User-user communication

• Mediation?I Rooting/suggesting/enhance the queriesI Building personalized document rankingsI Automatically set-up division of labor

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Notations

Notation Descriptiond Documentq Queryuj User jg Collaborative groupti term iRSV(d, q) Relevance Status Value given (d,q)N Document collection sizeni Number of documents in the collection in which term ti occursR Number of relevant documents in the collectionRuj Number of relevant documents in the collection for user uj

ruji Number of relevant documents of user uj in which term ti occurs

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SYSTEM-MEDIATED CIR MODELSUSER GROUP-BASED MEDIATION

• Enhancing collaborative search with users’ context[Morris et al., 2008, Foley and Smeaton, 2009a, Han et al., 2016]

I Division of labor: dividing the work by non-overlapping browsingI Sharing of knowledge: exploiting personal relevance judgments, user’s authority

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SYSTEM-MEDIATED CIR MODELSUSER/GROUP-BASED MEDIATION: GROUPIZATION, SMART SPLITTING, GROUP-HIGHLIGHTING [MORRIS ET AL., 2008]

• Hypothesis setting: one or a few synchronous search query(ies)• 3 approaches

I Smart splitting: splitting top ranked web results using a round-robin technique,personalized-splitting of remaining results (document ranking level)

I Groupization: reusing individual personalization techniques towards groups (document rankinglevel)

I Hit Highlighting: highlighting user’s keywords (document browsing level)

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SYSTEM-MEDIATED CIR MODELSUSER/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′ − n′i − Ruj + r

uji + 0.5)

(n′i − ruji + 0.5)(Ruj − r

uji + 0.5

(2)

N′ = (N + Ruj ) (3)

n′i = ni + ruji (4)

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SYSTEM-MEDIATED CIR MODELSUSER/GROUP-BASED MEDIATION: SMART-SPLITTING [MORRIS ET AL., 2008]

Example

Smart-splitting according to personalized scores.

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SYSTEM-MEDIATED CIR MODELSUSER/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

I Aggregate the partial user relevance scoresI Compute the user’s authority weighting

Figure: c©[Foley et al., 2008, Foley and Smeaton, 2009b]

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• A: Combining inputs of the RF process

puwo(ti) =

U−1∑u=0

ruiwBM25(ti) (5)

wBM25(ti) = log(∑U−1

u=0 αuru

iRu

)(1−∑U−1

u=0 αuni − rui

N − Ru)

(∑U−1

u=0 αuni − rui

N − Ru)(1−

∑U−1u=0 αu

rui

Ru)

(6)

U−1∑u=0

αu = 1 (7)

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• B: Combining outputs of the RF process

crwo(ti) =

U−1∑u=0

αuwBM25(ti, u) (8)

wBM25(ti, u) = log(

rui

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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SYSTEM-MEDIATED CIR MODELSUSER/GROUP-BASED MEDIATION: CONTEXT-BASED COLLABORATIVE SEARCH [HAN ET AL., 2016]

• Exploit a 3-dimensional context:I Individual search history HQU : queries, results, bookmarks etc.)I Collaborative group HCL: collaborators’ search history (queries, results, bookmarks etc.)I Collaboration HCH : collaboration behavior chat (communication)

Figure: c©[Han et al., 2016]

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SYSTEM-MEDIATED CIR MODELSUSER/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) =1K

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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SYSTEM-MEDIATED CIR MODELSROLE-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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SYSTEM-MEDIATED CIR MODELSROLE-BASED MEDIATION: PROSPECTOR AND MINER [PICKENS ET AL., 2008]

• Prospector/Miner as functional roles supported by algorithms:I Prospector: ”..opens new fields for exploration into a data collection..”.→ Draws ideas from algorithmically suggested query terms

I Miner: ”..ensures that rich veins of information are explored...”.→ Refines the search by judging highly ranked (unseen) documents

• Collaborative system architecture:I Algorithmic layer: functions

combining users’ search activities toproduce fitted outcomes to roles(queries, document rankings).

I Regulator layer: captures inputs(search activities), calls theappropriate functions of thealgorithmic layer, roots the outputsof the algorithmic layer to theappropriate role (user).

Figure: c©[Pickens et al., 2008]

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SYSTEM-MEDIATED CIR MODELSROLE-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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SYSTEM-MEDIATED CIR MODELSROLE-BASED MEDIATION: GATHERER AND SURVEYOR [SHAH ET AL., 2010]

• Gatherer/Surveyor as functional roles supported by algorithms:I Gatherer: ”..scan results of joint search activity to discover most immediately relevant documents..”.I Surveyor: ”..browse a wider diversity of information to get a better understanding of the collection

being searched...”.

• Main functions:I Merging: merging (eg. CombSum) the

documents rankings of collaboratorsI Splitting: rooting the appropriate

documents according to roles (eg.k-means clustering). High precision forthe Gatherer, high diversity for theSurveyor

Figure: c©[Shah et al., 2010]

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SYSTEM-MEDIATED CIR MODELSROLE-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 ina research focus surrounding the query topic..”.

• Domain novice: ”..represent problems in terms of surface or superficial aspects and aregenerally interested in enhancing their learning about the general query topic..”.

Soulier, L., Tamine, L., and Bahsoun, W. (2014b). On domainexpertise-based roles in collaborative information retrieval.Information Processing & Management (IP&M), 50(5):752774.

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SYSTEM-MEDIATED CIR MODELSROLE-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,wkij)∈π(uj)

k [λkdjP(ti|θd) + (1− λk

dj)P(ti|θC)]wk

ij (21)

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1 Role-based document relevance scoring : parameter smoothing using evidence fromnovelty and specificity

λkdj =

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

I Novelty

Nov(d,D(uj)k) = mind′∈D(uj)

k d(d, d′) (23)

I Specificity

Spec(d) = avgti∈dspec(ti) = avgti∈d(−log(

fdtiN )

α) (24)

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1 Document allocation to collaboratorsI Classification-based on the Expectation Maximization algorithm (EM)

I E-step: Document probability of belonging to collaborator’s class

P(Rj = 1|xkdj) =

αkj · φ

kj (xk

dj)

αkj · φ

kj (xk

dj) + (1− αkj ) · ψ

kj (xk

dj)(25)

I M-step : Parameter updating and likelihood estimationI Document allocation to collaborators by comparison of document ranks within collaborators’

lists

rkjj′ (d, δk

j , δkj′ ) =

{1 if rank(d, δk

j ) < rank(d, δkj′ )

0 otherwise(26)

I Division of labor: displaying distinct document lists between collaborators

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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 t4q 1 0 1 0d1 2 3 1 1d2 0 0 5 3d3 2 1 7 6d4 4 1 0 0d5 2 0 0 0d6 3 0 0 0d7 7 1 1 1d8 3 3 3 3d9 1 4 5 0d10 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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Example


RSV(q, d) rank(d) Spec(d)d1 0.24 2 0.19d2 0.02 7 0.23d3 0.17 3 0.19d4 0.03 6 0.15d5 0.01 9 0.1d6 0.02 8 0.1d7 0.10 4 0.19d8 0.31 1 0.19d9 0.09 5 0.16d10 0.01 10 0.15

• Iteration 0: Distributing top (6) documents to users: 3 most specific to the expert andthe 3 less specific to the novice.

I Expert u1: l0(u1,D0ns) = {d8, d1, d3}

I Novice u2: l0(u2,D0ns) = {d7, d9, d4}

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Example


• Iteration 1. Let’s consider that user u2 selected document d4 (D(u1)1 = {d4}).I Building the user’s profile.π(u1)

1 = (0.5, 0, 0.5, 0)π(u2)

1 = ( 0.5+0.82 , 0.2

2 ,0.52 , 0) = (0.65, 0.1, 0.25, 0).

I Estimating the document relevance with respect to collaborators.I 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 ∗ 2484 )

0.05 + (0.85 ∗ 37 + 0.15 ∗ 13

84 )0 + (0.85 ∗ 1

7 + 0.15 ∗ 2684 )

0.05 +

(0.85 ∗ 17 + 0.15 ∗ 21

84 )0 = 0.22

λ111 = 1∗0.19

0.23 = 0.85 where 0.19 expresses the specificity of document d1 and 1 is the documentnovelty score, and 0.23 the normalization score.

The normalizeddocument scoresfor eachcollaborators arethe following:

P1(d|u1) P2(d|u2)d1 0.23 0.28d2 0 0.03d3 0.16 0.11d5 0.01 0.01d6 0.03 0.02d7 0.12 0.14d8 0.34 0.34d9 0.10 0.06d10 0.01 0.01 51 / 102



Example


• Iteration 1. Let’s consider that user u2 selected document d4 (D(u1)1 = {d4, d5}).I Building the user’s profile.π(u1)

1 = (0.5, 0, 0.5, 0)π(u2)

1 = ( 0.5+0.82 , 0.2

2 ,0.52 , 0) = (0.65, 0.1, 0.25, 0).

I Estimating the document relevance with respect to collaborators.I 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∗0.190.23 = 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 ∗ 2484 )

0.05 + (0.85 ∗ 37 + 0.15 ∗ 13

84 )0 + (0.85 ∗ 1

7 + 0.15 ∗ 2684 )

0.05 +

(0.85 ∗ 17 + 0.15 ∗ 21

84 )0 = 0.22

The normalizeddocument scoresfor eachcollaborators arethe following:

P1(d|u1) P2(d|u2)d1 0.23 0.28d2 0 0.03d3 0.16 0.11d5 0.01 0.01d6 0.03 0.02d7 0.12 0.14d8 0.34 0.34d9 0.10 0.06d10 0.01 0.01 52 / 102


USER-DRIVEN SYSTEM-MEDIATED CIR MODELSMINE USERS’ ROLES THEN PERSONALIZE THE SEARCH

Soulier, L., Shah, C., and Tamine, L. (2014a). User-drivenSystem-mediated Collaborative Information Retrieval. InProceedings of the Annual International SIGIR Conference onResearch and Development in Information Retrieval, SIGIR 14,pages 485494. ACM.

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USER-DRIVEN SYSTEM-MEDIATED CIR MODELSMINE USERS’ ROLES THEN PERSONALIZE THE SEARCH [SOULIER ET AL., SIGIR 2014A]

• Identifying users’ search behavior differences: estimating significance of differencesusing the Kolmogrov-Smirnov test

• Characterizing users’ role

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• User’s roles modeled through patternsI Intuition

Number of visited documents

Number of submitted queries

Negative correlation

I Role pattern PR1,2

I Search feature kernel KR1,2

I Search feature-based correlation matrix FR1,2

FR1,2=

1 if positively correlated−1 if negatively correlated0 otherwise

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• 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].

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Example

Mining role of collaborators

A collaborativesearch sessionimplies two usersu1 and u2 aimingat identifyinginformationdealing with“global warming”.We present searchactions ofcollaborators forthe 5 first minutesof the session.

u t actions additional informationu2 0 submitted query “global warming”u1 1 submitted query “global warming”u2 8 document d1 : visited comment: “interesting”u2 12 document d2 : visitedu2 17 document d3 : visited rated: 4/5u2 19 document d4 : visitedu1 30 submitted query “greenhouse effect”u1 60 submitted query “global warming definition”u1 63 document d20 : visited rated: 3/5u1 70 submitted query “global warming protection”u1 75 document d21 : visitedu2 100 document d5 : visited rated: 5/5u2 110 document d6 : visited rated: 4/5u2 120 document d7 : visitedu1 130 submitted query “gas emission”u1 132 document d22 : visited rated: 4/5u2 150 document d8 : visitedu2 160 document d9 : visitedu2 170 document d10 : visitedu2 200 document d11 : visited comment: “great”u2 220 document d12 : visitedu2 240 document d13 : visitedu1 245 submitted query “global warming world protection”u1 250 submitted query “causes temperature changes”u1 298 submitted query “global warming world politics” 57 / 102



Example

Mining role of collaborators: matching with role patterns

• Role patternsI Roles of reader-querier

FRread,querier =

(1 −1−1 1

),KRread,querier = {(Nq,Np)}

Role : (S(tl)u1, S

(tl)u2 ,Rread,querier) → {(reader, querier), (querier, reader)}

(S(tl)u1, S

(tl)u2 ,Rread,querier) 7→

{(reader, querier) if S

(tl)u1

(tl,Np) > S(tl)u2 (tl,Np)

(querier, reader) otherwise

I Role of judge-querier

FRjudge,querier =

(1 −1−1 1

),KRjudge,querier = {(Nq,Nc)}

Role : (S(tl)u1, S

(tl)u2 ,Rjudge,querier → {(judge, querier), (querier, judge)}

(S(tl)u1, S

(tl)u2 ,Rjudge,querier) 7→

{(judge, querier) if S

(tl)u1

(tl,Nc) > S(tl)u2 (tl,Nc)

(querier, judge) otherwise

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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 04 2 0 15 3 0 25 3 0 28 3 0 2

S(300)u2 =

1 4 1 11 7 1 31 10 1 31 13 2 31 13 2 3

• Collaborators’ search differences (matrix and Kolmogorov-Smirnov test)

∆(300)u1,u2 =

2 −4 −1 −13 −5 −1 −24 −7 −1 −14 −10 −2 −17 −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

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Example

Mining role of collaborators: matching with role patterns

• Collaborators’ search action complementarity: correlation matrix between searchdifferences

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’

behaviorsI Role of reader-querier

||FRread,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.

I Role of judge-querier

||FRjudge,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 (highestNp).

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Paradigm division of labor � � � � � � �sharing of knowledge � � � � � � �

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1. Collaboration in IS and IR 2. Collaborative IR techniques and models Emerging topics around collaboration 4. Open ideas 5. Discussion

PLAN



3. Emerging topics around collaborationResearch fields and key critical questionsSocial media-based collaborative information accessCrowdsourcing

4. Open ideas

5. Discussion

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COLLABORATION AND SOCIAL MEDIA-BASED IRTWO SIDES OF THE SAME COIN?

• Quiz Time!

I What are these redpoints?

I Who are the winners?I How much times?I How do they win?

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• Quiz Time!



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• Quiz Time!


I Who are the winners?I How much times?

I How do they win?

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• Quiz Time!



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• Quiz Time!



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RESEARCH FIELDS AND KEY CRITICAL QUESTIONSSOCIAL MEDIA-BASED INFORMATION ACCESS AND CROWDSOURCING

• Social media-based collaborative information access

I Seeking, answering, sharing, bookmarking, and spreading informationI Implicit or explicit intents (sharing, questioning, and/or answering)

→ Improving the search outcomes through social interactions

• CrowdsourcingI Solving a task according to constraints (budget, time, ...)I 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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SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESSCONTEXT AND MOTIVATIONS

• CollaborationI Identifying and solving a shared complex problemI Creating and sharing knowledge within a work team

• Social media-based collaborationI Leveraging from the ”wisdom of the crowd”I Tasks: social question-answering, social search, real-time search

Emerging needs

• Understanding the cognitive behaviors of social users sharing, assessing and disseminatinginformation within social medias in order to achieve shared tasks leading to collective andproductive outcomes.

• Designing of a theoretical framework for collaborative IR within social environments

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• 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.

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• Analyzing seekers’ behavior on social media platforms[Morris, 2013, Oeldorf-Hirsch et al., 2014, Teevan et al., 2011, Fuchs and Groh, 2015]

I Investigating the motivation of using social media for search tasksI Analyzing information needsI Studying the scope of social interactionsI Analyzing users’ satisfaction

• Main resultsI Large audience and wide range of topics

[Harper et al., 2008, Jeong et al., 2013, Tamine et al., 2016]I Specific audience, expertise→ trust, personalisation and contextualisation

[Morris et al., 2010]I Friendsourcing through people addressing (”@”, forward)

[Liu and Jansen, 2013, Teevan et al., 2011, Fuchs and Groh, 2015]I Communication, exchange, sensemaking

[Morris, 2013, Evans and Chi, 2010, Tamine et al., 2016]

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I Investigating the motivation of using social media for search tasksI Analyzing information needsI Studying the scope of social interactionsI Analyzing users’ satisfaction






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• Analyzing the potential of collaboration in social media platformsI Research questions

I What are the structural and semantic patterns of explicit collaboration?I 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-basedcollaborative information access: Analysis of online crisis-related twitterconversations. ACM conference on HyperText and hypermedia, 2016.

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• Analyzing the potential of collaboration in social media platforms [Tamine et al., 2016]I Building the conversation tree

I Analyzing the patterns of collaboration networksI Extracting collaboration topics through the LDA algorithm

I Sandy: Insults; Prayers; Negative thoughts; ThanksI Ebola: Prevention; Victims and quarantine; Actions/Thoughts to people

I Building the social-collaboration network over the whole graph

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• Analyzing the potential of collaboration in social media platforms [Tamine et al., 2016]I Building the conversation treeI Analyzing the patterns of collaboration networks

I Extracting collaboration topics through the LDA algorithmI Sandy: Insults; Prayers; Negative thoughts; ThanksI Ebola: Prevention; Victims and quarantine; Actions/Thoughts to people


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• Analyzing the potential of collaboration in social media platforms [Tamine et al., 2016]I Building the conversation treeI Analyzing the patterns of collaboration networksI Extracting collaboration topics through the LDA algorithm



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• Analyzing the potential of collaboration in social media platforms [Tamine et al., 2016]I Building the conversation treeI Analyzing the patterns of collaboration networksI Extracting collaboration topics through the LDA algorithm



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I Limitations of social media information accessI Majority of questions without response [Jeong et al., 2013, Paul et al., 2011]I Answers mostly provided by members of the immediate follower network

[Morris et al., 2010, Rzeszotarski et al., 2014]I 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)

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Design implications



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Design implications



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SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESSTHE APPROACHES: MEDIATION AT THE USER LEVEL

• Recommending usersI 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]

I Social availability/Responsiveness[Horowitz and Kamvar, 2010, Sung et al., 2013, Ranganath et al., 2015]

I Social activity [Horowitz and Kamvar, 2010, Wang et al., 2013, Ranganath et al., 2015]I Users’ connectedness [Horowitz and Kamvar, 2010]

• Identifying the right group of collaboratorsI Expertise and interests

[Chang and Pal, 2013, Nushi et al., 2015, Ranganath et al., 2015, Soulier et al., 2016]I Social availability/Responsiveness

[Chang and Pal, 2013, Mahmud et al., 2013, Nushi et al., 2015]I Social activity [Chang and Pal, 2013, Mahmud et al., 2013, Nushi et al., 2015,

Ranganath et al., 2015, Soulier et al., 2016]I Users’ connectedness [Ranganath et al., 2015]I Personality/Compatibility [Chang and Pal, 2013, Mahmud et al., 2013]I Optimization of the overall response [Mahmud et al., 2013, Soulier et al., 2016]I Complementarity of users’ skills [Nushi et al., 2015, Soulier et al., 2016]

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• Recommending usersI 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]I Social availability/Responsiveness [Horowitz and Kamvar, 2010, Sung et al., 2013]I Social activity [Horowitz and Kamvar, 2010, Wang et al., 2013]I Users’ connectedness [Horowitz and Kamvar, 2010]

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SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESSRECOMMENDING USERS: AARDVARK [HOROWITZ AND KAMVAR, 2010]

Aardvark [Horowitz and Kamvar, 2010]

• The village paradigm: towards a social dissemination of knowledgeI Information is passed from person to personI 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)

Figure: c©[Horowitz and Kamvar, 2010]74 / 102


SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESSRECOMMENDING 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

I Named entity extractors (Wikipedia,openNLP, Yahoo! Placemaker)

I Matching with the expertise of asker’sfriends (place and interests)

I Answers built using predefinedtemplates

Figure: c©[Hecht et al., 2012]

• Investigaetore: Recommending urlsI Filtering using a whitelist of domainsI Retrieving the top results

Figure: c©[Hecht et al., 2012]

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SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESSRECOMMENDING 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):I User interest (user profiling with recent tweets and score based on TF-IDF)I User social tie (strength and topicality of the retweet relationship between two users)I User influence (number of followers, number of received retweets/replies, and coverage of

posted tweets)

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• Identifying the right group of collaboratorsI Expertise and interests [Nushi et al., 2015, Soulier et al., 2016, Chang and Pal, 2013]I Social availability/Responsiveness

[Chang and Pal, 2013, Mahmud et al., 2013, Nushi et al., 2015]I Social activity

[Chang and Pal, 2013, Mahmud et al., 2013, Nushi et al., 2015, Soulier et al., 2016]I Personality/Compatibility [Chang and Pal, 2013, Mahmud et al., 2013]I Optimization of the overall response [Mahmud et al., 2013, Soulier et al., 2016]I Complementarity of users’ skills [Nushi et al., 2015, Soulier et al., 2016]

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SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESSRECOMMENDING 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 modelI Topic-dependentI Dynamic with users’ actions (answers, posts) and time (last actions)I User’s social network dependent

Figure: c©[Nushi et al., 2015]

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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 modelI Topic-dependentI Dynamic with users’ actions (answers, posts) and time (last actions)I User’s social network dependent

Figure: c©[Nushi et al., 2015]

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• Routing questions within a crowdI Trade-off between users’ utility model and ”dominating” users (crowd skyline)I Pruning algorithm discarding the search space of the best user not yet included

• Routing questions to multipe crowds

I Crowd summary

Summary(c, t, f ) =

∑u∈skyline(c,t) f (c, t, u)

|skyline(c, t)|

I Crowd ranking

Score(c, t) =∑f∈F

(wf · Summary(c, t, f ))

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• Routing questions within a crowdI Trade-off between users’ utility model and ”dominating” users (crowd skyline)I Pruning algorithm discarding the search space of the best user not yet included

• Routing questions to multipe crowds

I Crowd summary

Summary(c, t, f ) =

∑u∈skyline(c,t) f (c, t, u)

|skyline(c, t)|

I Crowd ranking

Score(c, t) =∑f∈F

(wf · Summary(c, t, f ))

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SOCIAL MEDIA-BASED COLLABORATIVE INFORMATION ACCESSBUILDING 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., andNguyen, G-H. (2016).Answering TwitterQuestions: a Model forRecommending Answerersthrough Social Collaboration,ACM CIKM 2016.

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• Learning the collaboration likelihoodI Hypotheses:

I On Twitter, collaboration between users is noted by the @ symbol[Ehrlich and Shami, 2010, Honey and Herring, 2009]

I Trust and authority enable to improve the effectiveness of the collaboration [McNally et al., 2013]I 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 groupI Identifying candidate collaborators through a temporal ranking model

[Berberich and Bedathur, 2013]I Extracting the collaborator group

I Recursive decrementation of candidate collaborators through the information gain metricI Maximizing entropy equivalent to minimizing the information gain [Quinlan, 1986]

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• Learning the collaboration likelihoodI Hypotheses:

I On Twitter, collaboration between users is noted by the @ symbol[Ehrlich and Shami, 2010, Honey and Herring, 2009]

I Trust and authority enable to improve the effectiveness of the collaboration [McNally et al., 2013]I 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 groupI Identifying candidate collaborators through a temporal ranking model

[Berberich and Bedathur, 2013]I Extracting the collaborator group

I Recursive decrementation of candidate collaborators through the information gain metricI Maximizing entropy equivalent to minimizing the information gain [Quinlan, 1986]

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RESEARCH FIELDS AND KEY CRITICAL QUESTIONSSOCIAL MEDIA-BASED INFORMATION ACCESS AND CROWDSOURCING

• Social media-based collaborative information access

I Seeking, answering, sharing, bookmarking, and spreading informationI Implicit or explicit intents (sharing, questioning, and/or answering)

→ Improving the search outcomes through social interactions

• CrowdsourcingI Solving a task according to constraints (budget, time, ...)I 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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CROWDSOURCINGCONTEXT AND MOTIVATIONS

• Crowdsourcing platforms

I Leveraging from the ”wisdom of the crowd” to perform a task [Li et al., 2014]I A step forward for improving the quality of search engines for specific tasks requiring high

quality data, assessments or labels [Abraham et al., 2016]I Large-scale experimental evaluation reducing the cost of running and analyzing experiments

[Abraham et al., 2016]I Cheap, fast, reliable mechanism to gather labels [Snow et al., 2008]

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CROWDSOURCINGCONTEXT AND MOTIVATIONS

Main issues

• Optimizing the search taskI How to agregate answers over workers?→ voting functions, stopping rules

[Abraham et al., 2016]I 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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CROWDSOURCINGRECOMMENDING 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

I Intuition I Information gain metricI wu : probability that user u provides the right

responseI 1−wu

L−1 : probability that user u does not providethe right response

IG(u, L) = lnL + wulnwu + (1− wu)ln1− wu

L− 1(30)

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1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration Open ideas 5. Discussion

PLAN




4. Open ideas

5. Discussion

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1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration Open ideas 5. Discussion

OPEN IDEASOPEN IDEAS

• Towards a novel probabilistic framework of relevance for CIRI What is a ”good ranking” with regard to the expected synergic effect of collaboration?

• Towards an axiomatic approach of relevance for CIRI Are IR heuristics similar to CIR heuristics?I Can relevance towards a group be modeled by a set of formally defined constraints on a

retrieval function?• Dynamic IR models for CIR

I How to optimize long-term gains over multiple users, user-user interactions, user-systeminteractions and multi-search sessions?

I How to formalize the division of labor through the evolving of users’ information needs overtime?

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1. Collaboration in IS and IR 2. Collaborative IR techniques and models 3. Emerging topics around collaboration 4. Open ideas Discussion

PLAN




4. Open ideas

5. Discussion

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DISCUSSION

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