evaluation of recommender technology using multi agent simulation

Lehrstuhl Informatik 5(Information Systems)

Prof. Dr. M. Jarke

Zina PetrushynaRalf Klamma

I5-P220311-1

TeLLNet

Evaluation of Recommender Technology Using Multi-Agent Simulation

Zina Petrushyna, Ralf Klamma

March 22nd, 2011

CELSTEC, Open University, Heerlen


Prof. Dr. M. Jarke


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TeLLNet

Agenda

MotivationTeLLNetGame TheoryNetwork Formation GamesMulti-Agent SimulationsFuture Work


Prof. Dr. M. Jarke


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TeLLNet

TeLLNet = Teachers Lifelong Learning NetworkWhy do some teachers collaborate with others and some not?

163.330 registered teachers only 29.119 teachers collaborate in 19.128 projects

How to create better support for teachers?

TeLLNet


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TeLLNet

Game Theory Basics

Every situation as a game [Borel38, NeMo44]A player – makes decisions in a gamePlayers choose best strategies based on payoff functionsPayoffs motivations of playersA strategy defines a set of moves or actions a player will follow in a given game (mixed strategy, pure strategy)


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TeLLNet

Game Theory

A game is a tuple , where N is a nonempty, finite set of playersEach player has

1. a set of actions (strategy space) 2. payoff functions 3. payoff matrix

NiiNii uANG )(,)(,

NiiARAui :

Player B chooses white Player B chooses black

Player A chooses white 1,1 1,0

Player A chooses black 0,1 0,0


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TeLLNet

Social networks are formed by individual decisionsCost: write an e-mailUtility: cooperate with others

Social networks between pupilsCost: make a jokeUtility: get appreciation from others

Lifelong learner networksCost: take a learning courseUtility: find learners with

similar way of reasoning

Network Formation Games


Prof. Dr. M. Jarke


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TeLLNet

Set of agents which are actors of a network. and are typical members of a setA strategy of an agent is a vector

where for each

Actor and are connected if

Network Formation

}...,1{ nN

i

i j

Ni),,,...,( ,1,1,1, niiiiiii aaaaa

}1,0{, jia }{\ iNj

j 1, jia


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TeLLNet

Nash Network : Win-Win Situation

Every agent changes its strategy until all agents are satisfied with their strategies and will not benefit if they change strategies (the network is stable) Nash equilibriumA network is a Nash network if each agent is in Nash equilibriumChosen strategies defeat others for the good of all players [Nash51, FuTi91]


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TeLLNet

Network Formation Strategies

Homophily – love of the same [LaMe54, MSK01]similar socio-economical status thinking in a similar way

Contagiositybeing influenced by others

How to represent strategies for a lifelong learner?


Prof. Dr. M. Jarke


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TeLLNetEpistemic Network Analysis: Assesment

of Learning

Learning in action [Gee2003]Assessment of isolated skills is not effective

Focus on performance in context (actions)Evidence of learning:

linking models of understandingobservable actionsevaluation

[SHS*09]


Prof. Dr. M. Jarke


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TeLLNet

Epistemic Frame for TeLLNet

• the way how members of a community see themselves in the community• institution role, country

Identity

• tasks, community members perform• languages, subjects, and tools from projects

Skills

• the understanding shared by members of a community• languages, subjects

Knowledge

• beliefs of members• experiences from projects (partners)

Values

• warrants that justify members’ actions as legitimate• quality labels, prizes, European quality labels

Epistemology


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TeLLNet

Multi-Agent Simulation System

A multi-agent system is a collection of heterogeneous and diverse intelligent agents that interact with each other and their environment [SiAi08]Simulation of a real-world domain [LMS*05]

Approximation of the real worldSimulation model consists of a set of rules that defines how the system changes over timePurposes of simulation system:

Better understanding of a systemPredictions


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TeLLNet

Examples / State of the Art

RecommendationsYenta [Foner97] – looking for users with similar interestsbased on data from Web media

Market-binding mechanisms Looking for the best item (a reward agent, set of items and users agents) [WMJe05]

Team formationForming teams for performing a task in dynamicenvironment [GaJa05]


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TeLLNet

Multi-Agent Simulation Questions

Which kind of behavior can be expected under arbitrarily given parameter combinations and initial conditions?Which kind of behavior will a given target system display in the future?Which state will the target system reach in the future?

[Troitzsch2000]

2008 2009 2010


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TeLLNet

Agent Based Simulation

Heterogeneous, autonomous and pro-active actors, such as human-centered systems

Agents are capable to act without human interventionAgents possess goal-directed behaviorEach agent has its own incentives and motives

Suited for modeling organizations: most work is based on cooperation and communication

[Gazendam, 1993]


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TeLLNet

Inputs for simulation model

Agent =TeacherTeacher properties:

LanguagesSubjectsCountryInstitution roleAny Awards? (European Quality Label or Prize)

Project properties:LanguagesToolsSubjectsNumber of pupils in a project Age of pupils in a projectAny Award? (Quality Label)


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TeLLNet

Recommendation Techniques

Collaborative filtering [Breese et al.1998]Memory-based: user-based, item-basedModel-based: Bayesian, pLSA, Clustering, etc.

Content-based Recommendation [Sarwar et al.2001]Items featuresUsers‘ profile based on features of rated items

Hybrid Techniques [Burke2002]Partner?


Prof. Dr. M. Jarke


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TeLLNetSimulation of Network Formation using

Data Mining

Compare teacher profiles:subjects ,institutional roles, experiences in projectsFind teachers that suit to each other

Cosine similarityBelief NetworksDecision trees

The relationship concerns only 2 teachers and omits teachers in a network!


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TeLLNet

Network Formation Game Simulation

Payoff definition: payoff matrix is calculated dynamically based on Epistemic Frame vector:

teachers‘ subjects, subjects of projects (experiences)teachers‘ languages, languages of projects (experiences)tools used in projects (experiences)countries past collaborators are coming from (beliefs)...

Strategy definition: homophily or contagiosityLooking for a suitable network for a teacher and not for a suitable partner!


Prof. Dr. M. Jarke


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TeLLNetNash Equilibrium for Network Formation

Finding a Nash Equilibrium (NE) is NP-hardComputer scientists deal with finding appropriate techniques for calculating NE with a lot of agentsWe are not interested in the best solution but in a better solution


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TeLLNet

Future work

Running simulation model with many agents (>100)Evaluation of simulations results comparing networksEvaluation of teachers satisfaction of proposed networksTools/techniques for computing Nash equilibrium


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ReferencesLuck, M., McBurney, P., Shehory, O., & Willmott, S. (2005). Agent technology: computing as interaction (a roadmap for agent based computing). Liverpool, UK: AgentLink.Troitzsch, K.G. Approaching agent-based simulation: FIRMA meeting 2000, Available via http://www.uni-koblenz.de/~moeh/publik/ABM.pdfGazendam, H.W.M. (1993). Theories about architectures and performance of multi-agent systems. In: III European Congress of Psychology. Tampere, Finnland.Burke, R. Hybrid recommender systems: Survey and experiments, User Modeling and User-Adapted Interaction 12 (2002), pp. 331–370Helou, S. El, Salzmann C.,Sire S., Gillet, D. The 3A Contextual Ranking System: Simultaneously Recommending Actors, Assets, and Group Activities, in: Proc. of the ACM Conference On Recommender Systems, ACM, New York, 2009, 373–376.Herlocker J.L., Konstan J.A., Terveen L.G., Riedl J.T. (2004). Evaluating Collaborative Filtering Recommender Systems, ACM Transactions on Information Systems, Vol. 22, No. 1, January 2004, pp. 5–53.Manouselis, N. , Drachsler, H., Vuorikari, R., Hummel, H., Koper, R. (2010) Recommender Systems in Technology Enhanced Learning, in Kantor P., Ricci F., Rokach L., Shapira, B. (Eds.), Recommender Systems Handbook: A Complete Guide for Research Scientists & Practitioners.Brusilovsky P., Nejdl W., (2004) “Adaptive Hypermedia and Adaptive Web”, Practical Handbookof Internet Computing, CRC Press LLCWalker, A., Recker, M., Lawless, K., Wiley, D., “Collaborative information filtering: A review and an educational application”, International Journal of Artificial Intelligence and Education,14, 1-26, 2004.Nadolski, R., Van den Berg, B., Berlanga, A., Drachsler, H., Hummel, H., Koper, R.,& Sloep, P. (2009). Simulating light-weight Personalised Recommender Systems in learning networks: A case for Pedagogy-Oriented and Rating based Hybrid Recommendation Strategies. Journal of Artificial Societies and Social Simulation (JASSS), vol. 12, no 14, http://jasss.soc.surrey.ac.uk/12/1/4.html, Accessed 17 November, 2009.Drachsler, H., Pecceu, D., Arts, T., Hutten, E., Rutledge, L., Van Rosmalen, P., Hummel, H.G.K., Koper, R.: ReMashed - Recommendations for Mash-Up Personal Learning Environments. In: Cress, U., Dimitrova, V., Specht, M. (eds.): Learning in the Synergy of Multiple Disciplines, EC-TEL 2009, LNCS 5794, Berlin; Heidelberg; New York: Springer, pp 788-793, 2009aFoner, L. 1999. Political artifacts and personal privacy: The Yenta multi-agent distributed matchmaking system. Ph.D. thesis, Massachusetts Institute of Technology.Gaston, M.E. and des Jardins, M. Agent-organized networks for dynamic network formation. In ACM AAMAS’05, pp. 230-237, New York, USA, 2005Anderson, C. The Long Tail: why the future of business is selling less of more. New York: Hyperion, 2006Siebers, P.-O. and Aickelin, U. Introduction to multi-agent simulation. Computing research repository, 2008von Neumann, J. and Morgenstern, O. (1944), Theory of games and economic behavior, Princeton University PressBorel E. (1938) Applications aux Jeux de HasardMcPherson, M., L. Smith-Lovin, and J. Cook. (2001). Birds of a Feather: Homophily in Social Networks. Annual Review of Sociology. 27:415-44. Lazarsfeld, P., and R. K. Merton. (1954). Friendship as a Social Process: A Substantive and Methodological Analysis. In Freedom and Control in Modern Society, Morroe Berger, Theodore Abel, and Charles H. Page, eds. New York: Van Nostrand, 18-66.Gee, J.P. 2003 What video games have to teach about learning and literacy. New York: Palgrave Macmillian

http://www.uni-koblenz.de/~moeh/publik/ABM.pdf


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TeLLNet

Recommender Systems in TEL

TEL User Tasks supported by Recommender System [HKTR04, MDV*10] : Find peers!Adaptive systems (educational hypermedia) [BrNe04] – content selection, navigation support, presentationAltered Vista System [WRL*04]3A Contextual Ranking System [ESS*09]Recommender algorithms simulations [NBB*09]ReMashed - tags and ratings of Web media [DPA*09]


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TeLLNet

What Do We Query in the Dataset?

How do teachers(agents) make their decisions?What properties should the collaborator possess?What preferences does a teacher has according his future/current partners?

How do teachers form their future behaviours?What preferencies may be changed in the future in defining their collaboration partners and why?

How do they remember he past? How do they learn and reflect in their behaviour?

evaluation of recommender technology using multi agent simulation

Education

nash networkifeachagentis

nash equilibrium ne

epistemic network analysis

agent simulationzinapetrushyna

behavioreach agent

game borel38

game theorya gameis

epistemic frame vector