Fetching the paper…
Reading the bibliography…
Ranking, recommendation, and retrieval systems are widely used in online platforms and other societal systems, including e-commerce, media-streaming, admissions, gig platforms, and hiring.
The probability ranking principle in ir
S. E. Robertson · 1977
Earlier work this paper cites.
First-mover advantage: A synthesis, conceptual framework, and research propositions
R. A. Kerin, P. R. Varadarajan, and R. A. Peterson · 1992
Earlier work this paper cites.
The trec-8 question answering track report
E. M. Voorhees · 1999
Earlier work this paper cites.
Variations in relevance judgments and the measurement of retrieval effectiveness
E. M. Voorhees · 2000
Earlier work this paper cites.
Travelling waves and spatial hierarchies in measles epidemics
B. T. Grenfell, O. N. Bjørnstad, and J. Kappey · 2001
Earlier work this paper cites.
Cumulated gain-based evaluation of ir techniques
K. Järvelin and J. Kekäläinen · 2002
Earlier work this paper cites.
Behavioural studies of strategic thinking in games
C. F. Camerer · 2003
Earlier work this paper cites.
Eye-tracking analysis of user behavior in www search
L. A. Granka, T. Joachims, and G. Gay · 2004
Earlier work this paper cites.
Shilling recommender systems for fun and profit
S. K. Lam and J. Riedl · 2004
Earlier work this paper cites.
Dynamic conversion behavior at e-commerce sites
W. W. Moe and P. S. Fader · 2004
Earlier work this paper cites.
Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions
G. Adomavicius and A. Tuzhilin · 2005
Earlier work this paper cites.
Topical interests and the mitigation of search engine bias
S. Fortunato, A. Flammini, F. Menczer, and A. Vespignani · 2006
Earlier work this paper cites.
The effectiveness of web search engines for retrieving relevant ecommerce links
B. J. Jansen and P. R. Molina · 2006
Earlier work this paper cites.
The netflix prize
J. Bennett, S. Lanning, et al · 2007
Earlier work this paper cites.
Content-based recommendation systems
M. J. Pazzani and D. Billsus · 2007
Earlier work this paper cites.
Predictably irrational
D. Ariely and S. Jones · 2008
Earlier work this paper cites.
Recommendation systems with purchase data
A. V. Bodapati · 2008
Earlier work this paper cites.
An experimental comparison of click position-bias models
N. Craswell, O. Zoeter, M. Taylor, and B. Ramsey · 2008
Earlier work this paper cites.
Leading the herd astray: An experimental study of self-fulfilling prophecies in an artificial cultural market
M. J. Salganik and D. J. Watts · 2008
Earlier work this paper cites.
Presentation bias is significant in determining user preference for search results—a user study
J. Bar-Ilan, K. Keenoy, M. Levene, and E. Yaari · 2009
Earlier work this paper cites.
Performance of recommender algorithms on top-n recommendation tasks
P. Cremonesi, Y. Koren, and R. Turrin · 2010
Earlier work this paper cites.
Modeling social networks from sampled data
M. S. Handcock and K. J. Gile · 2010
Earlier work this paper cites.
Discrete temporal models of social networks
S. Hanneke, W. Fu, and E. P. Xing · 2010
Earlier work this paper cites.
Improving ad relevance in sponsored search
D. Hillard, S. Schroedl, E. Manavoglu, H. Raghavan, and C. Leggetter · 2010
Earlier work this paper cites.
Can you measure the roi of your social media marketing?
D. L. Hoffman and M. Fodor · 2010
Earlier work this paper cites.
Beyond position bias: Examining result attractiveness as a source of presentation bias in clickthrough data
Y. Yue, R. Patel, and H. Roehrig · 2010
Earlier work this paper cites.
Learning to rank for information retrieval
T.-Y. Liu · 2011
Earlier work this paper cites.
A user-centric evaluation framework for recommender systems
P. Pu, L. Chen, and R. Hu · 2011
Earlier work this paper cites.
Item popularity and recommendation accuracy
H. Steck · 2011
Earlier work this paper cites.
The role of social networks in information diffusion
E. Bakshy, I. Rosenn, C. Marlow, and L. Adamic · 2012
Earlier work this paper cites.
Fairness through awareness
C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel · 2012
Earlier work this paper cites.
Content Strategy for the Web: Content Strategy Web _p2
K. Halvorson and M. Rach · 2012
Earlier work this paper cites.
The play store
R. Holly · 2012
Earlier work this paper cites.
The million song dataset challenge
B. McFee, T. Bertin-Mahieux, D. P. Ellis, and G. R. Lanckriet · 2012
Earlier work this paper cites.
Crawling ajax-based web applications through dynamic analysis of user interface state changes
A. Mesbah, A. Van Deursen, and S. Lenselink · 2012
Earlier work this paper cites.
Social media marketing
M. Saravanakumar and T. SuganthaLakshmi · 2012
Earlier work this paper cites.
Streaming trend detection in twitter
J. Benhardus and J. Kalita · 2013
Earlier work this paper cites.
Iolaus: Securing online content rating systems
A. Molavi Kakhki, C. Kliman-Silver, and A. Mislove · 2013
Earlier work this paper cites.
Behavioral dynamics on the web: Learning, modeling, and prediction
K. Radinsky, K. M. Svore, S. T. Dumais, M. Shokouhi, J. Teevan, A. Bocharov, and E. Horvitz · 2013
Earlier work this paper cites.
Multi-Objective Pareto-Efficient Approaches for Recommender Systems
M. T. Ribeiro, A. Lacerda, E. Silva, D. E. Moura, E. Silva De Moura, I. Hata, A. Veloso, and N. Zi · 2013
Earlier work this paper cites.
Time-aware point-of-interest recommendation
Q. Yuan, G. Cong, Z. Ma, A. Sun, and N. M. Thalmann · 2013
Earlier work this paper cites.
Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols
P. G. Campos, F. Díez, and I. Cantador · 2014
Earlier work this paper cites.
On the dynamics of social media popularity: A youtube case study
F. Figueiredo, J. M. Almeida, M. A. Gonçalves, and F. Benevenuto · 2014
Earlier work this paper cites.
Online shoppers’ response to price comparison sites
K. Jung, Y. C. Cho, and S. Lee · 2014
Earlier work this paper cites.
Exploring the Filter Bubble: The Effect of Using Recommender Systems on Content Diversity
T. T. Nguyen, P.-M. Hui, F. M. Harper, L. Terveen, and J. A. Konstan · 2014
Earlier work this paper cites.
The doppelgänger bot attack: Exploring identity impersonation in online social networks
O. Goga, G. Venkatadri, and K. P. Gummadi · 2015
Earlier work this paper cites.
The netflix recommender system: Algorithms, business value, and innovation
C. A. Gomez-Uribe and N. Hunt · 2015
Earlier work this paper cites.
The movielens datasets: History and context
F. M. Harper and J. A. Konstan · 2015
Earlier work this paper cites.
Influence of vertical result in web search examination
Z. Liu, Y. Liu, K. Zhou, M. Zhang, and S. Ma · 2015
Earlier work this paper cites.
The international affiliation network of youtube trends
E. L. Platt, R. Bhargava, and E. Zuckerman · 2015
Earlier work this paper cites.
Estimating the causal impact of recommendation systems from observational data
A. Sharma, J. M. Hofman, and D. J. Watts · 2015
Earlier work this paper cites.
Economic recommendation systems
G. Bahar, R. Smorodinsky, and M. Tennenholtz · 2016
Earlier work this paper cites.
App installs on ios and android: Cross platform spillover
A. Farahat and T. Bhatia · 2016
Earlier work this paper cites.
Data poisoning attacks on factorization-based collaborative filtering
B. Li, Y. Wang, A. Singh, and Y. Vorobeychik · 2016
Earlier work this paper cites.
Reviews, reputation, and revenue: The case of yelp. com
M. Luca · 2016
Earlier work this paper cites.
Fake it till you make it: Reputation, competition, and yelp review fraud
M. Luca and G. Zervas · 2016
Earlier work this paper cites.
Recommendations as treatments: Debiasing learning and evaluation
T. Schnabel, A. Swaminathan, A. Singh, N. Chandak, and T. Joachims · 2016
Earlier work this paper cites.
Beyond ranking: Optimizing whole-page presentation
Y. Wang, D. Yin, L. Jie, P. Wang, M. Yamada, Y. Chang, and Q. Mei · 2016
Cited alongside, same era.
Controlling popularity bias in learning-to-rank recommendation
H. Abdollahpouri, R. Burke, and B. Mobasher · 2017
Cited alongside, same era.
Multisided fairness for recommendation
R. Burke · 2017
Cited alongside, same era.
Optimizing the recency-relevancy trade-off in online news recommendations
A. Chakraborty, S. Ghosh, N. Ganguly, and K. P. Gummadi · 2017
Cited alongside, same era.
Racial discrimination in the sharing economy: Evidence from a field experiment
B. Edelman, M. Luca, and D. Svirsky · 2017
Cited alongside, same era.
Coevolve: A joint point process model for information diffusion and network evolution
Awareness in practice: tensions in access to sensitive attribute data for antidiscrimination
M. Bogen, A. Rieke, and S. Ahmed · 2020
Later among the works it cites.
Fair allocation through selective information acquisition
W. Cai, J. Gaebler, N. Garg, and S. Goel · 2020
Later among the works it cites.
Bias and debias in recommender system: A survey and future directions
J. Chen, H. Dong, X. Wang, F. Feng, M. Wang, and X. He · 2020
Later among the works it cites.
Guidelines on ranking transparency pursuant to regulation (eu) 2019/1150 of the european parliament and of the council, 2020
E. Commission · 2020
Later among the works it cites.
Fairness is not static: Deeper understanding of long term fairness via simulation studies
A. D’Amour, H. Srinivasan, J. Atwood, P. Baljekar, D. Sculley, and Y. Halpern · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
M. Farajtabar, Y. Wang, M. Gomez-Rodriguez, S. Li, H. Zha, and L. Song · 2017
Cited alongside, same era.
Ir evaluation methods for retrieving highly relevant documents
K. Järvelin and J. Kekäläinen · 2017
Cited alongside, same era.
Distributive Justice
J. Lamont and C. Favor · 2017
Cited alongside, same era.
Information retrieval meets game theory: The ranking competition between documents’ authors
N. Raifer, F. Raiber, M. Tennenholtz, and O. Kurland · 2017
Cited alongside, same era.
Fairness-aware group recommendation with pareto-efficiency
L. Xiao, Z. Min, Z. Yongfeng, G. Zhaoquan, L. Yiqun, and M. Shaoping · 2017
Cited alongside, same era.
Beyond parity: Fairness objectives for collaborative filtering
S. Yao and B. Huang · 2017
Cited alongside, same era.
Fa* ir: A fair top-k ranking algorithm
M. Zehlike, F. Bonchi, C. Castillo, S. Hajian, M. Megahed, and R. Baeza-Yates · 2017
Cited alongside, same era.
Evaluating stochastic rankings with expected exposure
F. Diaz, B. Mitra, M. D. Ekstrand, A. J. Biega, and B. Carterette · 2020
Later among the works it cites.
On fair selection in the presence of implicit variance
V. Emelianov, N. Gast, K. P. Gummadi, and P. Loiseau · 2020
Later among the works it cites.
Exploring Longitudinal Effects of Session-based Recommendations
A. Ferraro, D. Jannach, and X. Serra · 2020
Later among the works it cites.
The effect of content-equivalent near-duplicates on the evaluation of search engines
M. Fröbe, J. P. Bittner, M. Potthast, and M. Hagen · 2020
Later among the works it cites.
A survey of learning causality with data: Problems and methods
R. Guo, L. Cheng, J. Li, P. R. Hahn, and H. Liu · 2020
Later among the works it cites.
Simulating the Impact of Recommender Systems on the Evolution of Collective Users’ Choices
N. Hazrati, M. Elahi, and F. Ricci · 2020
Later among the works it cites.
Escaping the McNamara Fallacy: Toward More Impactful Recommender Systems Research
D. Jannach and C. Bauer · 2020
Later among the works it cites.
Do offline metrics predict online performance in recommender systems?
K. Krauth, S. Dean, A. Zhao, W. Guo, M. Curmei, B. Recht, and M. I. Jordan · 2020
Later among the works it cites.
Cross-platform spillover effects in consumption of viral content: A quasi-experimental analysis using synthetic controls
H. Krijestorac, R. Garg, and V. Mahajan · 2020
Later among the works it cites.
Fairness without demographics through adversarially reweighted learning
P. Lahoti, A. Beutel, J. Chen, K. Lee, F. Prost, N. Thain, X. Wang, and E. Chi · 2020
Later among the works it cites.
Feedback Loop and Bias Amplification in Recommender Systems
M. Mansoury, H. Abdollahpouri, M. Pechenizkiy, B. Mobasher, and R. Burke · 2020
Later among the works it cites.
Optimizing long-term social welfare in recommender systems: A constrained matching approach
M. Mladenov, E. Creager, O. Ben-Porat, K. Swersky, R. Zemel, and C. Boutilier · 2020
Later among the works it cites.
Controlling fairness and bias in dynamic learning-to-rank
M. Morik, A. Singh, J. Hong, and T. Joachims · 2020
Later among the works it cites.
Fairrec: Two-sided fairness for personalized recommendations in two-sided platforms
G. K. Patro, A. Biswas, N. Ganguly, K. P. Gummadi, and A. Chakraborty · 2020
Later among the works it cites.
Mars-gym: A gym framework to model, train, and evaluate recommender systems for marketplaces
M. R. O. Santana, L. C. Melo, F. H. F. Camargo, B. Brandão, A. Soares, R. M. Oliveira, and S. Caetano · 2020
Later among the works it cites.
T. Sühr, S. Hilgard, and H. Lakkaraju · 2020
Later among the works it cites.
Action we take, 2020
TrustPilot · 2020
Later among the works it cites.
Causal inference for recommender systems
Y. Wang, D. Liang, L. Charlin, and D. M. Blei · 2020
Later among the works it cites.
Measuring Recommender System Effects with Simulated Users
S. Yao, Y. Halpern, N. Thain, X. Wang, K. Lee, F. Prost, E. H. Chi, J. Chen, and A. Beutel · 2020
Later among the works it cites.
Reducing disparate exposure in ranking: A learning to rank approach
M. Zehlike and C. Castillo · 2020
Later among the works it cites.
Practical data poisoning attack against next-item recommendation
H. Zhang, Y. Li, B. Ding, and J. Gao · 2020
Later among the works it cites.
What we can’t measure, we can’t understand: Challenges to demographic data procurement in the pursuit of fairness
M. Andrus, E. Spitzer, J. Brown, and A. Xiang · 2021
Later among the works it cites.
On the dangers of stochastic parrots: can language models be too big?
E. M. Bender, A. McMillan-Major, T. Gebru, and S. Shmitchell · 2021
Later among the works it cites.
Individually fair ranking
A. Bower, H. Eftekhari, M. Yurochkin, and Y. Sun · 2021
Later among the works it cites.
Proposal for a regulation laying down harmonised rules on artificial intelligence, 2021
E. Commission · 2021
Later among the works it cites.
When the umpire is also a player: Bias in private label product recommendations on e-commerce marketplaces
A. Dash, A. Chakraborty, S. Ghosh, A. Mukherjee, and K. P. Gummadi · 2021
Later among the works it cites.
Bridging machine learning and mechanism design towards algorithmic fairness
J. Finocchiaro, R. Maio, F. Monachou, G. K. Patro, M. Raghavan, A.-A. Stoica, and S. Tsirtsis · 2021
Later among the works it cites.
The (Im)possibility of Fairness: Different Value Systems Require Different Mechanisms For Fair Decision Making
S. A. Friedler, C. Scheidegger, and S. Venkatasubramanian · 2021
Later among the works it cites.
Imitate theworld: A search engine simulation platform
Y. Gao, G. Huzhang, W. Shen, Y. Liu, W.-J. Zhou, Q. Da, and Y. Yu · 2021
Later among the works it cites.
Standardized tests and affirmative action: The role of bias and variance
N. Garg, H. Li, and F. Monachou · 2021
Later among the works it cites.
Towards long-term fairness in recommendation
Y. Ge, S. Liu, R. Gao, Y. Xian, Y. Li, X. Zhao, C. Pei, F. Sun, J. Ge, W. Ou, et al · 2021
Later among the works it cites.
When fair ranking meets uncertain inference
A. Ghosh, R. Dutt, and C. Wilson · 2021
Later among the works it cites.
The stereotyping problem in collaboratively filtered recommender systems
W. Guo, K. Krauth, M. Jordan, and N. Garg · 2021
Later among the works it cites.
The market for fake reviews
S. He, B. Hollenbeck, and D. Proserpio · 2021
Later among the works it cites.
Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research
B. Koch, A. Hanna, E. Denton, and J. Foster · 2021
Later among the works it cites.
Understanding the attenuation of the accommodation recommendation spillover effect in view of spatial distance
S. Lai and N. Fan · 2021
Later among the works it cites.
L. T. Liu, N. Garg, and C. Borgs · 2021
Later among the works it cites.
Test-optional policies: Overcoming strategic behavior and informational gaps
Z. Liu and N. Garg · 2021
Later among the works it cites.
T-recs: A simulation tool to study the societal impact of recommender systems
E. Lucherini, M. Sun, A. Winecoff, and A. Narayanan · 2021
Later among the works it cites.
Accordion: A trainable simulator forlong-term interactive systems
J. McInerney, E. Elahi, J. Basilico, Y. Raimond, and T. Jebara · 2021
Later among the works it cites.
Algorithmic Impact Assessments and Accountability: The Co-construction of Impacts
J. Metcalf, E. Moss, E. A. Watkins, R. Singh, and M. C. Elish · 2021
Later among the works it cites.
Documenting computer vision datasets: An invitation to reflexive data practices
M. Miceli, T. Yang, L. Naudts, M. Schuessler, D. Serbanescu, and A. Hanna · 2021
Later among the works it cites.
Algorithmic fairness: Choices, assumptions, and definitions
S. Mitchell, E. Potash, S. Barocas, A. D’Amour, and K. Lum · 2021
Later among the works it cites.
RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems
M. Mladenov, C.-W. Hsu, V. Jain, E. Ie, C. Colby, N. Mayoraz, H. Pham, D. Tran, I. Vendrov, and C. Boutilier · 2021
Later among the works it cites.
Friends in high places: Demand spillovers and competition on digital platforms
M. Raj · 2021
Later among the works it cites.
Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development
M. K. Scheuerman, A. Hanna, and E. Denton · 2021
Later among the works it cites.
Algorithmic Auditing and Social Justice: Lessons from the History of Audit Studies
B. Vecchione, S. Barocas, and K. Levy · 2021
Later among the works it cites.
Maximizing marginal fairness for dynamic learning to rank
T. Yang and Q. Ai · 2021
Later among the works it cites.
A survey on causal inference
L. Yao, Z. Chu, S. Li, Y. Li, J. Gao, and A. Zhang · 2021
Later among the works it cites.
Fairness in Ranking: A Survey
M. Zehlike, K. Yang, and J. Stoyanovich · 2021
Later among the works it cites.
Fair top-k ranking with multiple protected groups
M. Zehlike, T. Sühr, R. Baeza-Yates, F. Bonchi, C. Castillo, and S. Hajian · 2022
Closest in time.