The SimIIR 2.0 Framework
Saber Zerhoudi, Sebastian Günther, Kim Plassmeier, Timo Borst, Christin Seifert, Matthias Hagen, Michael Granitzer
2022Proceedings of the 31st ACM International Conference on Information & Knowledge Management21 citationsDOI
Abstract
Simulated user retrieval system interactions enable studies with controlled user behavior. To this end, the SimIIR framework offers static, rule-based methods. We present an extended SimIIR 2.0 version with new components for dynamic user type-specific Markov model-based interactions and more realistic query generation. A flexible modularization ensures that the SimIIR 2.0 framework can serve as a platform to implement, combine, and run the growing number of proposed search behavior and query simulation ideas.
Topics & Concepts
Computer scienceModular programmingMarkov chainDistributed computingTheoretical computer scienceInformation retrievalData miningProgramming languageMachine learningInformation Retrieval and Search BehaviorRecommender Systems and TechniquesAdvanced Image and Video Retrieval Techniques