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This survey aims at providing a comprehensive overview of the recent trends in the field of modeling and simulation (M&S) of interactions between users and recommender systems and applications of the M&S to the performance improvement of industrial recommender engines.
Multi-Agent Adversarial Inverse Reinforcement Learning
Yu, L., Song, J., Ermon, S., 2019 · 1907
Earlier work this paper cites.
RecSim: A Configurable Simulation Platform for Recommender Systems
Ie, E., Hsu, C.w., Mladenov, M., Jain, V., Narvekar, S., Wang, J., Wu, R., Boutilier, C., 2019 · 1909
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Learning to Recommend from Sparse Data via Generative User Feedback
Wang, W., Xu, H., Zhang, R., Wang, W., Rai, P., Carin, L., 2019 · 1910
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Bai, X., Guan, J., Wang, H., 2019 · 1911
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The matthew effect in science: The reward and communication systems of science are considered
Merton, R.K., 1968 · 1968
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Validation of Simulation Results
Van Horn, R.L., 1971 · 1971
Earlier work this paper cites.
Principles and techniques of simulation validation, verification, and testing, in: Proceedings of the 27th conference on Winter simulation - WSC ’95, ACM Press, New York, New York, USA. pp. 147–154
Balci, O., 1995 · 1995
Earlier work this paper cites.
Using content-based filtering for recommendation, in: Proceedings of the machine learning in the new information age: MLnet/ECML2000 workshop, pp. 47–56
Van Meteren, R., Van Someren, M., 2000 · 2000
Earlier work this paper cites.
New recommendation system using reinforcement learning
Rojanavasu, P., Srinil, P., Pinngern, O., 2005 · 2005
Earlier work this paper cites.
E-commerce intelligent agent: personalization travel support agent using q learning, in: Proceedings of the 7th international conference on Electronic commerce, pp. 287–292
Srivihok, A., Sukonmanee, P., 2005 · 2005
Earlier work this paper cites.
Improving recommendation lists through topic diversification
Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G., 2005 · 2005
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Using svd and demographic data for the enhancement of generalized collaborative filtering
Vozalis, M.G., Margaritis, K.G., 2007 · 2007
Earlier work this paper cites.
Novelty and diversity in information retrieval evaluation, in: SIGIR ’08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, ACM, New York, NY, USA. pp. 659–666
Clarke, C.L.A., Kolla, M., Cormack, G.V., Vechtomova, O., Ashkan, A., Büttcher, S., Mackinnon, I., 2008 · 2008
Earlier work this paper cites.
Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation
Saito, Y., Aihara, S., Matsutani, M., Narita, Y., 2020 · 2008
Earlier work this paper cites.
Partially synthetic data for recommender systems: Prediction performance and preference hiding
Slokom, M., Larson, M., Hanjalic, A., 2020 · 2008
Earlier work this paper cites.
Blockbuster culture’s next rise or fall: The impact of recommender systems on sales diversity
Fleder, D., Hosanagar, K., 2009 · 2009
Earlier work this paper cites.
Chen, J., Dong, H., Wang, X., Feng, F., Wang, M., He, X., 2020 · 2010
Earlier work this paper cites.
Modeling Micro-Macro Relationships: Problems and Solutions
Opp, K.D., 2011 · 2010
Earlier work this paper cites.
A framework for collaborative filtering recommender systems
Bobadilla, J., Hernando, A., Ortega, F., Bernal, J., 2011 · 2011
Earlier work this paper cites.
Do offline metrics predict online performance in recommender systems?
Krauth, K., Dean, S., Zhao, A., Guo, W., Curmei, M., Recht, B., Jordan, M.I., 2020 · 2011
Earlier work this paper cites.
Evaluating Recommendation Systems. Springer US, Boston, MA
Shani, G., Gunawardana, A., 2011 · 2011
Earlier work this paper cites.
Item popularity and recommendation accuracy, in: Proceedings of the Fifth ACM Conference on Recommender Systems, Association for Computing Machinery, New York, NY, USA. p. 125–132
Steck, H., 2011 · 2011
Earlier work this paper cites.
Incremental collaborative filtering recommender based on regularized matrix factorization
Luo, X., Xia, Y., Zhu, Q., 2012 · 2012
Earlier work this paper cites.
Mesoscopic level: A new representation level for large scale agent-based simulations, in: Proceedings of the 4th International Conference on Advances in System Simulation, pp. 68–73
Navarro, L., Corruble, V., Flacher, F., Zucker, J.D., 2012 · 2012
Earlier work this paper cites.
Ranking with non-random missing ratings: Influence of popularity and positivity on evaluation metrics, in: Proceedings of the Sixth ACM Conference on Recommender Systems, Association for Computing Machinery, New York, NY, USA. p. 147–154
Pradel, B., Usunier, N., Gallinari, P., 2012 · 2012
Earlier work this paper cites.
Verification and validation of simulation models
Sargent, R.G., 2013 · 2012
Earlier work this paper cites.
Multiscale modelling and simulation: a position paper
Hoekstra, A., Chopard, B., Coveney, P., 2014 · 2013
Earlier work this paper cites.
Dj-mc: A reinforcement-learning agent for music playlist recommendation
Liebman, E., Saar-Tsechansky, M., Stone, P., 2014 · 2014
Earlier work this paper cites.
Exploring the filter bubble: The effect of using recommender systems on content diversity, in: Proceedings of the 23rd International Conference on World Wide Web, Association for Computing Machinery, New York, NY, USA. p. 677–686
Nguyen, T.T., Hui, P.M., Harper, F.M., Terveen, L., Konstan, J.A., 2014 · 2014
Earlier work this paper cites.
The movielens datasets: History and context
Harper, F.M., Konstan, J.A., 2015 · 2015
Earlier work this paper cites.
Recommender system application developments: A survey
Lu, J., Wu, D., Mao, M., Wang, W., Zhang, G., 2015 · 2015
Cited alongside, same era.
Tensorflow: A system for large-scale machine learning, in: 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), pp. 265–283
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D.G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., Zheng, X., 2016 · 2016
Cited alongside, same era.
Datagencars: A generator of synthetic data for the evaluation of context-aware recommendation systems
del Carmen, M., Ilarri, S., Hermoso, R., Trillo-Lado, R., 2017 · 2016
Cited alongside, same era.
Generative Adversarial Imitation Learning
Ho, J., Ermon, S., 2016 · 2016
Cited alongside, same era.
Large-scale Validation of Counterfactual Learning Methods: A Test-Bed
Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems, in: RecSys 2020 - 14th ACM Conference on Recommender Systems, pp. 190–199
Huang, J., Oosterhuis, H., De Rijke, M., Van Hoof, H., 2020 · 2020
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Surprise: A Python library for recommender systems
Hug, N., 2020 · 2020
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Feedback loop and bias amplification in recommender systems, in: Proceedings of the 29th ACM International Conference on Information and Knowledge Management, Association for Computing Machinery, New York, NY, USA. p. 2145–2148
Mansoury, M., Abdollahpouri, H., Pechenizkiy, M., Mobasher, B., Burke, R., 2020 · 2020
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Demonstrating Principled Uncertainty Modeling for Recommender Ecosystems with RecSim NG, in: RecSys 2020 - 14th ACM Conference on Recommender Systems, pp. 591–593
Mladenov, M., Hsu, C.W., Jain, V., Ie, E., Colby, C., Mayoraz, N., Pham, H., Tran, D., Vendrov, I., Boutilier, C., 2020 · 2020
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Lefortier, D., Swaminathan, A., Gu, X., Joachims, T., de Rijke, M., 2016 · 2016
Cited alongside, same era.
The synthetic data vault, in: Proceedings - 3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016, pp. 399–410
Patki, N., Wedge, R., Veeramachaneni, K., 2016 · 2016
Cited alongside, same era.
Apache spark: A unified engine for big data processing
Zaharia, M., Xin, R.S., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M.J., Ghodsi, A., Gonzalez, J., Shenker, S., Stoica, I., 2016 · 2016
Cited alongside, same era.
Methods of recommender system: A review, in: 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), IEEE. pp. 1–4
Patel, B., Desai, P., Panchal, U., 2017 · 2017
Cited alongside, same era.
Case recommender, in: Proceedings of the 12th ACM Conference on Recommender Systems, ACM, New York, NY, USA. pp. 494–495
da Costa, A., Fressato, E., Neto, F., Manzato, M., Campello, R., 2018 · 2018
Cited alongside, same era.
Horizon: Facebook’s Open Source Applied Reinforcement Learning Platform
Gauci, J., Conti, E., Liang, Y., Virochsiri, K., He, Y., Kaden, Z., Narayanan, V., Ye, X., Chen, Z., Fujimoto, S., 2018 · 2018
Cited alongside, same era.
Generating inter-dependent data streams for recommender systems
Jakomin, M., Curk, T., Bosnić, Z., 2018 · 2018
Cited alongside, same era.
How Do Recommender Systems Affect Sales Diversity? A Cross-Category Investigation via Randomized Field Experiment
Lee, D., Hosanagar, K., 2019 · 2018
Cited alongside, same era.
Cornac: A comparative framework for multimodal recommender systems
Salah, A., Truong, Q.T., Lauw, H.W., 2020 · 2020
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MARS-Gym: A Gym framework to model, train, and evaluate Recommender Systems for Marketplaces, in: 2020 International Conference on Data Mining Workshops (ICDMW), IEEE. pp. 189–197
Santana, M.R.O., Melo, L.C., Camargo, F.H.F., Brandao, B., Soares, A., Oliveira, R.M., Caetano, S., 2020 · 2020
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Complex Social and Behavioral Systems
Sotomayor, M., Pérez-Castrillo, D., Castiglione, F. (Eds.), 2020 · 2020
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Understanding Longitudinal Dynamics of Recommender Systems with Agent-Based Modeling and Simulation
Adomavicius, G., Jannach, D., Leitner, S., Zhang, J., 2021 · 2021
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Report on the 1st simulation for information retrieval workshop (sim4ir 2021) at sigir 2021
Balog, K., Maxwell, D., Thomas, P., Zhang, S., 2022 · 2021
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Simulations in Recommender Systems: An industry perspective
Bernardi, L., Batra, S., Bruscantini, C.A., 2021 · 2021
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Recommendation system simulations: A discussion of two key challenges
Chaney, A.J.B., 2021 · 2021
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Bias Issues and Solutions in Recommender System: Tutorial on the RecSys 2021. Association for Computing Machinery, New York, NY, USA
Chen, J., Wang, X., Feng, F., He, X., 2021a · 2021
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A troubling analysis of reproducibility and progress in recommender systems research
Dacrema, M.F., Boglio, S., Cremonesi, P., Jannach, D., 2021 · 2021
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Fake it till you make it: Guidelines for effective synthetic data generation
Dankar, F.K., Ibrahim, M., 2021 · 2021
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SimuRec: Workshop on Synthetic Data and Simulation Methods for Recommender Systems Research, in: Fifteenth ACM Conference on Recommender Systems, ACM, New York, NY, USA. pp. 803–805
Ekstrand, M.D., Chaney, A., Castells, P., Burke, R., Rohde, D., Slokom, M., 2021 · 2021
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Imitate TheWorld: A Search Engine Simulation Platform
Gao, Y., Huzhang, G., Shen, W., Liu, Y., Zhou, W.J., Da, Q., Yu, Y., 2021 · 2021
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AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online
Huzhang, G., Pang, Z., Gao, Y., Liu, Y., Shen, W., Zhou, W.J., Da, Q., Zeng, A., Yu, H., Yu, Y., Zhou, Z.H., 2021 · 2021
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Kiyohara, H., Kawakami, K., Saito, Y., 2021 · 2021
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T-RECS: A Simulation Tool to Study the Societal Impact of Recommender Systems
Lucherini, E., Sun, M., Winecoff, A., Narayanan, A., 2021 · 2021
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Accordion: A trainable simulator forlong-term interactive systems, in: RecSys 2021 - 15th ACM Conference on Recommender Systems, pp. 102–113
McInerney, J., Elahi, E., Basilico, J., Raimond, Y., Jebara, T., 2021 · 2021
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Towards Unified Metrics for Accuracy and Diversity for Recommender Systems. Association for Computing Machinery, New York, NY, USA
Parapar, J., Radlinski, F., 2021 · 2021
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Synevarec: A framework for evaluating recommender systems on synthetic data classes, in: 2021 International Conference on Data Mining Workshops (ICDMW), pp. 55–64
Provalov, V., Stavinova, E., Chunaev, P., 2021 · 2021
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Quality Metrics in Recommender Systems: Do We Calculate Metrics Consistently?. Association for Computing Machinery, New York, NY, USA
Tamm, Y.M., Damdinov, R., Vasilev, A., 2021 · 2021
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Simulation as experiment: An empirical critique of simulation research on recommender systems
Winecoff, A.A., Sun, M., Lucherini, E., Narayanan, A., 2021 · 2021
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Measuring Recommender System Effects with Simulated Users
Yao, S., Halpern, Y., Thain, N., Wang, X., Lee, K., Prost, F., Chi, E.H., Chen, J., Beutel, A., 2021 · 2021
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UserSim: User simulation via supervised generative adversarial network, in: The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021, pp. 3582–3589
Zhao, X., Xia, L., Zou, L., Liu, H., Yin, D., Tang, J., 2021 · 2021
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Longitudinal Impact of Preference Biases on Recommender Systems’ Performance
Zhou, M., Zhang, J., Adomavicius, G., 2021 · 2021
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A Survey on Session-based Recommender Systems
Wang, S., Cao, L., Wang, Y., Sheng, Q.Z., Orgun, M.A., Lian, D., 2022 · 2022
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Recommender System Based on Temporal Models: A Systematic Review
Rabiu, I., Salim, N., Da’u, A., Osman, A., 2020 · 2076
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