a multidimensional expertise recommender tool

A multidimensional expertise recommender tool

German Sanchez-Hernandez, Jennifer Nguyen, Nuria Agell, Cecilio Angulo

June 24th, 2015

IntroductionState of the art

ArquitectureUser interface

Conclusions

Outline

1 Introduction

2 State of the art

3 Arquitecture

4 User interface

5 Conclusions

2 / 24 German Sanchez-Hernandez JARCA 2015: Expertise Recommender



Conclusions

Motivation and framework

Outline

1 Introduction

2 State of the art

3 Arquitecture

4 User interface

5 Conclusions




Conclusions



www.projectcollage.eu

Creative learning is social, collaborative and peer based.

Expand and foster interaction among users with differentbackgrounds, opinions and levels of expertise→ improvement of creativity.

Find people with expertise in some areas(“right level of expertise” vs. “right person”)


www.projectcollage.eu



Conclusions

Related work

Outline

1 Introduction

2 State of the art

3 Arquitecture

4 User interface

5 Conclusions




Conclusions

Related work

Related work of ERS

MITRE’s Expert Finder [Mattox et al., 1999]: experts intopics. Associations author-term in internal documents.Search by keywords.

NASA POPS [Grove and Schain, 2008]: filtering experts byorganisation, project or competency. Use of RDF, multipledatabases, semantic web.

IBM SmaillBlue [Lin et al., 2008; 2009]: expertise in emails andchat, mapping search strings to keywords.

INDURE FacFinder [Fang et al., 2008]: information fromfaculty profiles and homepages, all internal documentsindexed. Proximity of terms. Considers order, source and rankof source.

StrangersRS [Guy et al., 2011]: recommendation of people withsimilar interests but unfamiliar.




Conclusions

Related work

Lacks

Just expertise

Information treatment:

Filtering candidatesParameters required (weights, thresholds)

→ Premature discards




Conclusions

ArquitectureCER

Outline

1 Introduction

2 State of the art

3 Arquitecture

4 User interface

5 Conclusions




Conclusions

ArquitectureCER

Arquitecture




Conclusions

ArquitectureCER

ArquitectureProfiling

Profiling module

Candidates’ profiles

Access to Collage UserProfile Service

Offline massive updateOnline selective update




Conclusions

ArquitectureCER

Profiles

Skill Profiling CER

Expertise Areas of knowledgeFour qualitative levels(none, high, medium, low)

Qualities/Subskills Other knowledge Specific tools

Proximity Ease to contactPhysical distanceHigh or low

Availability Current availabilityManual updateFour levels




Conclusions

ArquitectureCER

ArquitectureInteraction

Interaction module

Interaction with the user

Translates preferences torequirements

ExplicitImplicit (user profile)

Controls for selecting finalexperts




Conclusions

ArquitectureCER

ArquitectureIdentification

Identification module

Initial list of candidates

Access to profilesFeasible candidates




Conclusions

ArquitectureCER

ArquitectureSelection

Selection module

Assessing each candidate

Aggregating assessments

Ranking and selection ofcandidates




Conclusions

ArquitectureCER

Selection of candidatesAssessment

One assessment per requirement. Fuzzy distance between therequirement and the fulfillment.

Expertise: Ae(ps , l) = min(ps ,l)l .

Subskill: Aq(ps) = ps .

Proximity: distance between user and candidate.

Ap(pd) =

{1− pd−md

Md−mdif high proximity is required,

pd−mdMd−md

otherwise.

Availability: distance to (high) required availability

Aa(pa) = pa. (1)

(ps , pd and pa are related to the profile of the candidate; l is the requiredlevel of expertise; md and Md are the minimum and maximum distancesbetween departments.)




Conclusions

ArquitectureCER

Selection of candidatesAggregation and Ranking

Operator: OWA.Weights: linguistic quantifier

wi = Q

(i

n

)− Q

(i − 1

n

), i = 1, . . . , n.

“most of”: Q(r) = r12 .




Conclusions

Outline

1 Introduction

2 State of the art

3 Arquitecture

4 User interface

5 Conclusions




Conclusions

Current UIInput form




Conclusions

New UIInput form




Conclusions

Current UIResults




Conclusions

New UIResults




Conclusions

Outline

1 Introduction

2 State of the art

3 Arquitecture

4 User interface

5 Conclusions




Conclusions

Conclusions and Next Steps

Expertise recommender to find the right expert.

Right level of expertise vs. right person.

Additional information used: qualities, proximity, availability.

Integration into an existing platform (end users’ affinity space,Moodle).

Information available in end users’ systems.

Improving prototype.

Team forming.


Thank you!

German [email protected]

a multidimensional expertise recommender tool

Data & Analytics