1. Evaluation of Recommender Technology Using Multi-Agent Simulation ZinaPetrushyna, Ralf Klamma March 22nd, 2011 CELSTEC, Open University, Heerlen

2. Agenda Motivation TeLLNet Game Theory Network Formation Games Multi-Agent Simulations Future Work

3. TeLLNet = TeachersLifelong Learning Network Why do someteacherscollaboratewithothersandsome not? 163.330 registered teachers only 29.119 teacherscollaborate in 19.128 projects Howtocreatebettersupportforteachers? TeLLNet

4. Game Theory Basics Every situationas a game [Borel38, NeMo44] A player – makesdecisions in a game Players choosebeststrategiesbased on payofffunctions Payoffsmotivationsofplayers A strategydefines a setofmovesoractions a player will follow in a givengame (mixedstrategy, pure strategy)

5. Game Theory A gameis a tuple , where Nis a nonempty, finite setofplayers Eachplayerhas a setofactions (strategyspace) payofffunctions payoffmatrix

6. Socialnetworksareformedby individual decisions Cost: write an e-mail Utility: cooperatewithothers Socialnetworksbetweenpupils Cost:make a joke Utility:getappreciationfromothers Lifelonglearnernetworks Cost:take a learningcourse Utility: find learnerswith similarwayofreasoning Network Formation Games

7. Set ofagentswhichareactorsof a network. andaretypicalmembersof a set A strategyof an agentis a vector whereforeach Actorandareconnectedif Network Formation

8. Nash Network : Win-Win Situation Every agentchangesitsstrategyuntil all agentsaresatisfiedwiththeirstrategiesand will not benefitiftheychangestrategies (thenetworkisstable)  Nash equilibrium A networkis a Nash networkifeachagentis in Nash equilibrium Chosen strategiesdefeatothersforthegoodof all players [Nash51, FuTi91]

9. Network Formation Strategies Homophily – loveofthe same [LaMe54, MSK01] similarsocio-economicalstatus thinking in a similarway Contagiosity beinginfluenced byothers Howtorepresent strategiesfor a lifelonglearner?

10. Epistemic Network Analysis: Assesmentof Learning Learning in action [Gee2003] Assessmentofisolatedskillsis not effective Focus on performance in context (actions) Evidence of learning: linking models of understanding observable actions evaluation [SHS*09]

11. Epistemic Frame forTeLLNet

12. Multi-Agent Simulation System A multi-agentsystemis a collectionofheterogeneousand diverse intelligent agentsthatinteractwitheachotherandtheirenvironment [SiAi08] Simulation of a real-worlddomain [LMS*05] Approximation ofthe real world Simulation model consistsof a setofrulesthatdefineshowthesystemchangesover time Purposesofsimulationsystem: Betterunderstandingof a system Predictions

13. Examples / State ofthe Art Recommendations Yenta [Foner97] – lookingforuserswithsimilarinterests based on datafrom Web media Market-bindingmechanisms Lookingforthebest item (a rewardagent, setofitemsand usersagents) [WMJe05] Team formation Formingteamsforperforming a task in dynamic environment [GaJa05]

14. Multi-Agent Simulation Questions Which kind of behavior can be expected under arbitrarily given parameter combinations andinitialconditions? Which kind of behavior will a given target system display in the future? Which state will the target system reach in the future? [Troitzsch2000] 2009 2010 2008

15. Agent Based Simulation Heterogeneous, autonomous and pro-active actors, such as human-centered systems Agents are capable to act without human intervention Agents possess goal-directed behavior Each agent has its own incentives and motives Suited for modeling organizations: most work is based on cooperation and communication [Gazendam, 1993]

16. Inputs forsimulation model Agent =Teacher Teacherproperties: Languages Subjects Country Institution role Any Awards? (European Quality Label orPrize) Project properties: Languages Tools Subjects Numberofpupils in a project Age ofpupils in a project Any Award? (Quality Label)

17. RecommendationTechniques Collaborative filtering [Breese et al.1998] Memory-based: user-based, item-based Model-based: Bayesian, pLSA, Clustering, etc. Content-based Recommendation [Sarwar et al.2001] Items features Users‘ profilebased on featuresofrateditems Hybrid Techniques [Burke2002] Partner?

18. Simulation of Network Formation using Data Mining Compareteacherprofiles:subjects ,institutionalroles, experiences in projects Find teachersthatsuittoeachother Cosinesimilarity Belief Networks Decisiontrees  The relationshipconcernsonly 2 teachersandomitsteachers in a network!

19. Network Formation Game Simulation Payoffdefinition: payoffmatrixiscalculateddynamicallybased on Epistemic Frame vector: teachers‘ subjects, subjectsofprojects (experiences) teachers‘ languages, languagesofprojects (experiences) toolsused in projects (experiences) countries pastcollaboratorsarecomingfrom (beliefs) ... Strategydefinition: homophilyorcontagiosity Lookingfor a suitablenetworkfor a teacherand not for a suitablepartner!

20. Nash Equilibrium forNetwork Formation Finding a Nash Equilibrium (NE) is NP-hard Computer scientists deal withfindingappropriatetechniquesforcalculating NE with a lotofagents Weare not interested in thebestsolution but in a bettersolution

21. Future work Runningsimulation model withmanyagents (>100) Evaluation ofsimulationsresultscomparingnetworks Evaluation ofteacherssatisfactionofproposednetworks Tools/techniquesforcomputing Nash equilibrium

22. References Luck, 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.pdf Gazendam, 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 Interaction12 (2002), pp. 331–370 Helou, 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 FilteringRecommender 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 Handbook of Internet Computing, CRC Press LLC Walker, 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, 2009a Foner, 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, 2005 Anderson, C. The Long Tail: why the future of business is selling less of more. New York: Hyperion, 2006 Siebers, P.-O. and Aickelin, U. Introduction to multi-agent simulation. Computing research repository, 2008 von Neumann, J. and Morgenstern, O. (1944), Theory of games and economic behavior, Princeton University Press Borel E. (1938) Applications aux Jeux de Hasard McPherson, 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

23. Recommender Systems in TEL TEL User Tasks supportedbyRecommender System [HKTR04, MDV*10] : Find peers! Adaptive systems (educationalhypermedia) [BrNe04] – contentselection, navigationsupport, presentation Altered Vista System [WRL*04] 3A Contextual Ranking System [ESS*09] Recommenderalgorithmssimulations [NBB*09] ReMashed - tags andratingsof Web media [DPA*09]

24. What Do We Query in the Dataset? How do teachers(agents) maketheirdecisions? Whatpropertiesshouldthecollaboratorpossess? Whatpreferencesdoes a teacherhasaccordinghisfuture/currentpartners? How do teachers form theirfuturebehaviours? Whatpreferenciesmaybechanged in thefuture in defining theircollaborationpartnersandwhy? How do theyremember he past? How do theylearnandreflect in theirbehaviour?