Evaluation of Recommender Technology Using Multi-Agent Simulation<br />ZinaPetrushyna, Ralf Klamma<br />March 22nd, 2011<b...
Agenda<br />Motivation<br />TeLLNet<br />Game Theory<br />Network Formation Games<br />Multi-Agent Simulations<br />Future...
TeLLNet = TeachersLifelong Learning Network<br />Why do someteacherscollaboratewithothersandsome not?<br />163.330 registe...
Game Theory Basics<br />Every situationas a game [Borel38, NeMo44]<br />A player – makesdecisions in a game<br />Players c...
Game Theory<br />A gameis a tuple<br />                                                                     , where<br />N...
Socialnetworksareformedby individual decisions<br />Cost: write an e-mail<br />Utility: cooperatewithothers<br />Socialnet...
Set ofagentswhichareactorsof a network.    andaretypicalmembersof a set<br />A strategyof an agentis a vector<br />wherefo...
Nash Network : Win-Win Situation<br />Every agentchangesitsstrategyuntil all agentsaresatisfiedwiththeirstrategiesand will...
Network Formation Strategies<br />Homophily – loveofthe same [LaMe54, MSK01]<br />similarsocio-economicalstatus<br />think...
Epistemic Network Analysis: Assesmentof Learning<br />Learning in action [Gee2003]<br />Assessmentofisolatedskillsis not e...
Multi-Agent Simulation System<br />A multi-agentsystemis a collectionofheterogeneousand diverse intelligent agentsthatinte...
Examples / State ofthe Art<br />Recommendations<br />Yenta [Foner97] – lookingforuserswithsimilarinterests<br />based on d...
Agent Based Simulation<br />Heterogeneous, autonomous and pro-active actors, such as human-centered systems<br />Agents ar...
Inputs forsimulation model<br />Agent =Teacher<br />Teacherproperties: <br />Languages<br />Subjects<br />Country<br />Ins...
RecommendationTechniques<br />Collaborative filtering [Breese et al.1998]<br />Memory-based: user-based, item-based<br />M...
Simulation of Network Formation using Data Mining <br />Compareteacherprofiles:subjects ,institutionalroles, experiences i...
Network Formation Game Simulation <br />Payoffdefinition: payoffmatrixiscalculateddynamicallybased on Epistemic Frame vect...
Nash Equilibrium forNetwork Formation<br />Finding a Nash Equilibrium (NE) is NP-hard<br />Computer scientists deal withfi...
Future work<br />Runningsimulation model withmanyagents (>100)<br />Evaluation ofsimulationsresultscomparingnetworks<br />...
References<br />Luck, M., McBurney, P., Shehory, O., & Willmott, S. (2005). Agent technology: computing as interaction (a ...
Recommender Systems in TEL<br />TEL User Tasks supportedbyRecommender System [HKTR04, MDV*10] : Find peers!<br />Adaptive ...
What Do We Query in the Dataset?<br />How do teachers(agents) maketheirdecisions?<br />Whatpropertiesshouldthecollaborator...
Evaluation of recommender technology using multi agent simulation
Evaluation of recommender technology using multi agent simulation
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Evaluation of recommender technology using multi agent simulation

  1. 1. Evaluation of Recommender Technology Using Multi-Agent Simulation<br />ZinaPetrushyna, Ralf Klamma<br />March 22nd, 2011<br />CELSTEC, Open University, Heerlen<br />
  2. 2. Agenda<br />Motivation<br />TeLLNet<br />Game Theory<br />Network Formation Games<br />Multi-Agent Simulations<br />Future Work<br />
  3. 3. TeLLNet = TeachersLifelong Learning Network<br />Why do someteacherscollaboratewithothersandsome not?<br />163.330 registered teachers<br />only 29.119 teacherscollaborate in 19.128 projects<br />Howtocreatebettersupportforteachers?<br />TeLLNet<br />
  4. 4. Game Theory Basics<br />Every situationas a game [Borel38, NeMo44]<br />A player – makesdecisions in a game<br />Players choosebeststrategiesbased on payofffunctions<br />Payoffsmotivationsofplayers<br />A strategydefines a setofmovesoractions a player will follow in a givengame (mixedstrategy, pure strategy)<br />
  5. 5. Game Theory<br />A gameis a tuple<br /> , where<br />Nis a nonempty, finite setofplayers<br />Eachplayerhas<br />a setofactions (strategyspace) <br />payofffunctions<br />payoffmatrix<br />
  6. 6. Socialnetworksareformedby individual decisions<br />Cost: write an e-mail<br />Utility: cooperatewithothers<br />Socialnetworksbetweenpupils<br />Cost:make a joke<br />Utility:getappreciationfromothers<br />Lifelonglearnernetworks<br />Cost:take a learningcourse<br />Utility: find learnerswith<br />similarwayofreasoning<br />Network Formation Games<br />
  7. 7. Set ofagentswhichareactorsof a network. andaretypicalmembersof a set<br />A strategyof an agentis a vector<br />whereforeach<br />Actorandareconnectedif<br />Network Formation<br />
  8. 8. Nash Network : Win-Win Situation<br />Every agentchangesitsstrategyuntil all agentsaresatisfiedwiththeirstrategiesand will not benefitiftheychangestrategies (thenetworkisstable)  Nash equilibrium<br />A networkis a Nash networkifeachagentis in Nash equilibrium<br />Chosen strategiesdefeatothersforthegoodof all players [Nash51, FuTi91]<br />
  9. 9. Network Formation Strategies<br />Homophily – loveofthe same [LaMe54, MSK01]<br />similarsocio-economicalstatus<br />thinking in a similarway<br />Contagiosity<br />beinginfluenced<br />byothers<br />Howtorepresent<br />strategiesfor a<br />lifelonglearner?<br />
  10. 10. Epistemic Network Analysis: Assesmentof Learning<br />Learning in action [Gee2003]<br />Assessmentofisolatedskillsis not effective<br />Focus on performance in context (actions)<br />Evidence of learning: <br />linking models of understanding<br />observable actions<br />evaluation<br /> [SHS*09]<br />
  11. 11. Epistemic Frame forTeLLNet<br />
  12. 12. Multi-Agent Simulation System<br />A multi-agentsystemis a collectionofheterogeneousand diverse intelligent agentsthatinteractwitheachotherandtheirenvironment [SiAi08]<br />Simulation of a real-worlddomain [LMS*05]<br />Approximation ofthe real world<br />Simulation model consistsof a setofrulesthatdefineshowthesystemchangesover time<br />Purposesofsimulationsystem:<br />Betterunderstandingof a system<br />Predictions<br />
  13. 13. Examples / State ofthe Art<br />Recommendations<br />Yenta [Foner97] – lookingforuserswithsimilarinterests<br />based on datafrom Web media<br />Market-bindingmechanisms<br />Lookingforthebest item (a rewardagent, setofitemsand<br />usersagents) [WMJe05]<br />Team formation<br />Formingteamsforperforming a task in dynamic<br />environment [GaJa05]<br />
  14. 14. Multi-Agent Simulation Questions<br />Which kind of behavior can be expected under arbitrarily given parameter combinations andinitialconditions?<br />Which kind of behavior will a given target system display in the future?<br />Which state will the target system reach in the future?<br /> [Troitzsch2000]<br />2009<br />2010<br />2008<br />
  15. 15. Agent Based Simulation<br />Heterogeneous, autonomous and pro-active actors, such as human-centered systems<br />Agents are capable to act without human intervention<br />Agents possess goal-directed behavior<br />Each agent has its own incentives and motives<br />Suited for modeling organizations: most work is based on cooperation and communication<br /> [Gazendam, 1993]<br />
  16. 16. Inputs forsimulation model<br />Agent =Teacher<br />Teacherproperties: <br />Languages<br />Subjects<br />Country<br />Institution role<br />Any Awards? (European Quality Label orPrize)<br />Project properties:<br />Languages<br />Tools<br />Subjects<br />Numberofpupils in a project<br />Age ofpupils in a project<br />Any Award? (Quality Label)<br />
  17. 17. RecommendationTechniques<br />Collaborative filtering [Breese et al.1998]<br />Memory-based: user-based, item-based<br />Model-based: Bayesian, pLSA, Clustering, etc.<br />Content-based Recommendation [Sarwar et al.2001]<br />Items features<br />Users‘ profilebased on featuresofrateditems<br />Hybrid Techniques [Burke2002]<br />Partner?<br />
  18. 18. Simulation of Network Formation using Data Mining <br />Compareteacherprofiles:subjects ,institutionalroles, experiences in projects<br />Find teachersthatsuittoeachother<br />Cosinesimilarity<br />Belief Networks<br />Decisiontrees<br /> The relationshipconcernsonly 2 teachersandomitsteachers in a network!<br />
  19. 19. Network Formation Game Simulation <br />Payoffdefinition: payoffmatrixiscalculateddynamicallybased on Epistemic Frame vector:<br />teachers‘ subjects, subjectsofprojects (experiences)<br />teachers‘ languages, languagesofprojects (experiences)<br />toolsused in projects (experiences)<br />countries pastcollaboratorsarecomingfrom (beliefs)<br />...<br />Strategydefinition: homophilyorcontagiosity<br />Lookingfor a suitablenetworkfor a teacherand not for a suitablepartner!<br />
  20. 20. Nash Equilibrium forNetwork Formation<br />Finding a Nash Equilibrium (NE) is NP-hard<br />Computer scientists deal withfindingappropriatetechniquesforcalculating NE with a lotofagents<br />Weare not interested<br /> in thebestsolution<br /> but in a bettersolution<br />
  21. 21. Future work<br />Runningsimulation model withmanyagents (>100)<br />Evaluation ofsimulationsresultscomparingnetworks<br />Evaluation ofteacherssatisfactionofproposednetworks<br />Tools/techniquesforcomputing Nash equilibrium<br />
  22. 22. References<br />Luck, M., McBurney, P., Shehory, O., & Willmott, S. (2005). Agent technology: computing as interaction (a roadmap for agent based computing). Liverpool, UK: AgentLink.<br />Troitzsch, K.G. Approaching agent-based simulation: FIRMA meeting 2000, Available via http://www.uni-koblenz.de/~moeh/publik/ABM.pdf<br />Gazendam, H.W.M. (1993). Theories about architectures and performance of multi-agent systems. In: III European Congress of Psychology. Tampere, Finnland.<br />Burke, R. Hybrid recommender systems: Survey and experiments, User Modeling and User-Adapted Interaction12 (2002), pp. 331–370<br />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.<br />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.<br />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.<br />Brusilovsky P., Nejdl W., (2004) “Adaptive Hypermedia and Adaptive Web”, Practical Handbook<br /> of Internet Computing, CRC Press LLC<br />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.<br />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.<br />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<br />Foner, L. 1999. Political artifacts and personal privacy: The Yenta multi-agent distributed matchmaking system. Ph.D. thesis, Massachusetts Institute of Technology.<br />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<br />Anderson, C. The Long Tail: why the future of business is selling less of more. New York: Hyperion, 2006<br />Siebers, P.-O. and Aickelin, U. Introduction to multi-agent simulation. Computing research repository, 2008<br />von Neumann, J. and Morgenstern, O. (1944), Theory of games and economic behavior, Princeton University Press<br />Borel E. (1938) Applications aux Jeux de Hasard<br />McPherson, M., L. Smith-Lovin, and J. Cook. (2001). Birds of a Feather: Homophily in Social Networks. Annual Review of Sociology. 27:415-44. <br />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.<br />Gee, J.P. 2003 What video games have to teach about learning and literacy. New York: Palgrave Macmillian<br />
  23. 23. Recommender Systems in TEL<br />TEL User Tasks supportedbyRecommender System [HKTR04, MDV*10] : Find peers!<br />Adaptive systems (educationalhypermedia) [BrNe04] – contentselection, navigationsupport, presentation<br />Altered Vista System [WRL*04]<br />3A Contextual Ranking System [ESS*09]<br />Recommenderalgorithmssimulations [NBB*09]<br />ReMashed - tags andratingsof Web media [DPA*09] <br />
  24. 24. What Do We Query in the Dataset?<br />How do teachers(agents) maketheirdecisions?<br />Whatpropertiesshouldthecollaboratorpossess?<br />Whatpreferencesdoes a teacherhasaccordinghisfuture/currentpartners?<br />How do teachers form theirfuturebehaviours?<br />Whatpreferenciesmaybechanged in thefuture in defining<br />theircollaborationpartnersandwhy?<br />How do theyremember he past? <br />How do theylearnandreflect in theirbehaviour?<br />

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