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The content that a recommender system (RS) shows to users influences them.
Degenerate Feedback Loops in Recommender Systems
Jiang, R., Chiappa, S., Lattimore, T., György, A., and Kohli, P · 1902
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BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., and Jiang, P · 1904
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Ie, E., Jain, V., Wang, J., Narvekar, S., Agarwal, R., Wu, R., Cheng, H.-T., Lustman, M., Gatto, V., Covington, P., McFadden, J., Chandra, T., and Boutilier, C · 1905
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Advantage Amplification in Slowly Evolving Latent-State Environments
Mladenov, M., Meshi, O., Ooi, J., Schuurmans, D., and Boutilier, C · 1905
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Toward Simulating Environments in Reinforcement Learning Based Recommendations
Zhao, X., Xia, L., Zou, L., Yin, D., and Tang, J · 1906
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Auditing Radicalization Pathways on YouTube
Ribeiro, M. H., Ottoni, R., West, R., Almeida, V. A. F., and Meira, W · 1908
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RecSim: A Configurable Simulation Platform for Recommender Systems
Ie, E., Hsu, C.-w., Mladenov, M., Jain, V., Narvekar, S., Wang, J., Wu, R., and Boutilier, C · 1909
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Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation
Bai, X., Guan, J., and Wang, H · 1911
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Memory accessibility and probability judgments: An experimental evaluation of the availability heuristic
MacLeod, C. and Campbell, L · 1939
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An Introduction to Hidden Markov Models
Rabiner, L. R · 1986
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A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models
Bilmes, J. A · 1998
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Planning and acting in partially observable stochastic domains
Kaelbling, L. P., Littman, M. L., and Cassandra, A. R · 1998
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Reinforcement learning: an introduction
Sutton, R. S. and Barto, A. G · 1998
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A Non-Homogeneous Hidden Markov Model for Precipitation Occurrence
Hughes, J. P., Guttorp, P., and Charles, S. P · 1999
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Artificial intelligence: a modern approach
Russell, S. and Norvig, P · 2002
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Is seeing believing? how recommender system interfaces affect users’ opinions
Cosley, D., Lam, S. K., Albert, I., Konstan, J. A., and Riedl, J · 2003
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The Engagement-Diversity Connection: Evidence from a Field Experiment on Spotify
Holtz, D., Carterette, B., Chandar, P., Nazari, Z., Cramer, H., and Aral, S · 2003
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Preference Construction and Persistence in Digital Marketplaces: The Role of Electronic Recommendation Agents
Häubl, G. and Murray, K. B · 2003
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Persuasion in Recommender Systems
Gretzel, U. and Fesenmaier, D · 2006
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Feedback Loop and Bias Amplification in Recommender Systems
Mansoury, M., Abdollahpouri, H., Pechenizkiy, M., Mobasher, B., and Burke, R · 2007
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Hidden Incentives for Auto-Induced Distributional Shift
Krueger, D., Maharaj, T., and Leike, J · 2009
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REALab: An Embedded Perspective on Tampering
Kumar, R., Uesato, J., Ngo, R., Everitt, T., Krakovna, V., and Legg, S · 2011
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Do Recommender Systems Manipulate Consumer Preferences? A Study of Anchoring Effects
Adomavicius, G., Bockstedt, J. C., Curley, S. P., and Zhang, J · 2013
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How actions create–not just reveal–preferences
Ariely, D. and Norton, M. I · 2013
Cited alongside, same era.
Modeling and broadening temporal user interest in personalized news recommendation
Li, L., Zheng, L., Yang, F., and Li, T · 2013
Cited alongside, same era.
Experimental evidence of massive-scale emotional contagion through social networks
Kramer, A. D. I., Guillory, J. E., and Hancock, J. T · 2014
Cited alongside, same era.
Exploring the filter bubble: the effect of using recommender systems on content diversity
Nguyen, T. T., Hui, P.-M., Harper, F. M., Terveen, L., and Konstan, J. A · 2014
Cited alongside, same era.
Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks
Zhang, Y., Dai, H., Xu, C., Feng, J., Wang, T., Bian, J., Wang, B., and Liu, T.-Y · 2014
Cited alongside, same era.
Practical Diversified Recommendations on YouTube with Determinantal Point Processes
Wilhelm, M., Ramanathan, A., Bonomo, A., Jain, S., Chi, E. H., and Gillenwater, J · 2018
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SIREN: A Simulation Framework for Understanding the Effects of Recommender Systems in Online News Environments
Bountouridis, D., Harambam, J., Makhortykh, M., Marrero, M., Tintarev, N., and Hauff, C · 2019
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Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
Chen, X., Li, S., Li, H., Jiang, S., Qi, Y., and Song, L · 2019
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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., and Fujimoto, S · 2019
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Penalizing side effects using stepwise relative reachability
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Online Social Network Site Addiction: A Comprehensive Review
Andreassen, C. S · 2015
Cited alongside, same era.
The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections
Epstein, R. and Robertson, R. E · 2015
Cited alongside, same era.
Focusing on the Long-term: It’s Good for Users and Business
Hohnhold, H., O’Brien, D., and Tang, D · 2015
Cited alongside, same era.
Sunehag, P., Evans, R., Dulac-Arnold, G., Zwols, Y., Visentin, D., and Coppin, B · 2015
Cited alongside, same era.
Digital Nudging
Weinmann, M., Schneider, C., and vom Brocke, J · 2015
Cited alongside, same era.
Wide & Deep Learning for Recommender Systems
Cheng, H.-T., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., Anderson, G., Corrado, G., Chai, W., Ispir, M., Anil, R., Haque, Z., Hong, L., Jain, V., Liu, X., and Shah, H · 2016
Cited alongside, same era.
Deep Neural Networks for YouTube Recommendations
Covington, P., Adams, J., and Sargin, E · 2016
Cited alongside, same era.
Krakovna, V., Orseau, L., Kumar, R., Martic, M., and Legg, S · 2019
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
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Deep Learning based Recommender System: A Survey and New Perspectives
Zhang, S., Yao, L., Sun, A., and Tay, Y · 2019
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The Welfare Effects of Social Media
Allcott, H., Braghieri, L., Eichmeyer, S., and Gentzkow, M · 2020
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Top-K Off-Policy Correction for a REINFORCE Recommender System
Chen, M., Beutel, A., Covington, P., Jain, S., Belletti, F., and Chi, E · 2020
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Array programming with NumPy
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., Gérard-Marchant, P., Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant, T. E · 2020
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Digital nudging with recommender systems: Survey and future directions
Jesse, M. and Jannach, D · 2020
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Reinforcement learning based recommender systems: A survey
Afsar, M. M., Crump, T., and Far, B · 2021
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A theory of chosen preferences
Bernheim, B. D., Braghieri, L., Martínez-Marquina, A., and Zuckerman, D · 2021
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User Tampering in Reinforcement Learning Recommender Systems
Evans, C. and Kasirzadeh, A · 2021
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Recommender systems effect on the evolution of users’ choices distribution
Hazrati, N. and Ricci, F · 2021
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Auditing E-Commerce Platforms for Algorithmically Curated Vaccine Misinformation
Juneja, P. and Mitra, T · 2021
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High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models
Mudigere, D., Hao, Y., Huang, J., Tulloch, A., Sridharan, S., Liu, X., Ozdal, M., Nie, J., Park, J., Luo, L., Yang, J. A., Gao, L., Ivchenko, D., Basant, A., Hu, Y., Yang, J., Ardestani, E. K., Wang, X., Komuravelli, R., Chu, C.-H., Yilmaz, S., Li, H., Qian, J., Feng, Z., Ma, Y., Yang, J., Wen, E., Li, H., Yang, L., Sun, C., Zhao, W., Melts, D., Dhulipala, K., Kishore, K. R., Graf, T., Eisenman, A., Matam, K. K., Gangidi, A., Chen, G. J., Krishnan, M., Nayak, A., Nair, K., Muthiah, B., khorashadi, M., Bhattacharya, P., Lapukhov, P., Naumov, M., Qiao, L., Smelyanskiy, M., Jia, B., and Rao, V · 2021
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Designing Recommender Systems to Depolarize
Stray, J · 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., and Beutel, A · 2021
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Constrained Reinforcement Learning for Short Video Recommendation
Cai, Q., Zhan, R., Zhang, C., Zheng, J., Ding, G., Gong, P., Zheng, D., and Jiang, P · 2022
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Path-Specific Objectives for Safer Agent Incentives
Farquhar, S., Carey, R., and Everitt, T · 2022
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Franklin, M., Ashton, H., Gorman, R., and Armstrong, S · 2022
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