research-article

Competitive Search

Authors:

Moshe TennenholtzAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 2838 - 2849

https://doi.org/10.1145/3477495.3532771

Published: 07 July 2022 Publication History

Abstract

The Web is a canonical example of a competitive search setting that includes document authors with ranking incentives: their goal is to promote their documents in rankings induced for queries. The incentives affect some of the corpus dynamics as the authors respond to rankings by applying strategic document manipulations. This well known reality has deep consequences that go well beyond the need to fight spam. As a case in point, researchers showed using game theoretic analysis that the probability ranking principle is not optimal in competitive retrieval settings; specifically, it leads to reduced topical diversity in the corpus. We provide a broad perspective on recent work on competitive retrieval settings, argue that this work is the tip of the iceberg, and pose a suite of novel research directions; for example, a general game theoretic framework for competitive search, methods of learning-to-rank that account for post-ranking effects, approaches to automatic document manipulation, addressing societal aspects and evaluation.

References

[1]

2005--2009. AIRWeb - International Workshop on Adversarial Information Retrieval on the Web.

[2]

Gianni Amati and C. J. van Rijsbergen. 2002. Probabilistic models of information retrieval based on measuring the divergence from randomness. ACM Trans. Inf. Syst. 20, 4 (2002), 357--389.

Digital Library

[3]

Simon P. Anderson, Andre de Palma, and Jacques-Francois Thisse. 1992. Discrete Choice Theory of Product Differentiation. MIT press.

[4]

Itai Ashlagi, Dov Monderer, and Moshe Tennenholtz. 2008. On the Value of Correlation. J. Artif. Intell. Res. (JAIR) 33 (2008), 575--613.

Digital Library

[5]

Itai Ashlagi, Dov Monderer, and Moshe Tennenholtz. 2009. Mediators in position auctions. Games and Economic Behavior 67, 1 (2009), 2--21. https://doi.org/10. 1016/j.geb.2008.11.005

[6]

Susan Athey and Glenn Ellison. 2011. Position Auctions with Consumer Search. The Quarterly Journal of Economics 126, 3 (2011).

[7]

Robert Aumann. 1974. Subjectivity and correlation in randomized strategies. Journal of Mathematical Economics 1 (1974), 67--96.

[8]

Banerjee. 1992. A simple model of herd behavior. The Quarterly Journal of Economics 107 (1992), 797--817.

[9]

Nicholas J. Belkin, Colleen Cool, Diane Kelly, S-J Lin, SY Park, J Perez-Carballo, and C Sikora. 2001. Iterative exploration, design and evaluation of support for query reformulation in interactive information retrieval. Information Processing & Management 37, 3 (2001), 403--434.

Digital Library

[10]

Ran Ben-Basat, Moshe Tennenholtz, and Oren Kurland. 2015. The Probability Ranking Principle is Not Optimal in Adversarial Retrieval Settings. In Proceedings of ICTIR. 51--60.

Digital Library

[11]

Ran Ben-Basat, Moshe Tennenholtz, and Oren Kurland. 2017. A Game Theoretic Analysis of the Adversarial Retrieval Setting. J. Artif. Intell. Res. 60 (2017), 1127--1164.

[12]

Omer Ben-Porat and Moshe Tennenholtz. 2018. A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providers. In Proc. of NIPS. 1110--1120.

[13]

Michael Bendersky, W. Bruce Croft, and Yanlei Diao. 2011. Quality-biased ranking of Web documents. In Proceedings of WSDM. 95--104.

Digital Library

[14]

Ron Berman and Zsolt Katona. 2013. The Role of Search Engine Optimization in Search Marketing. Mark. Sci. 32, 4 (2013), 644--651.

Digital Library

[15]

Asia J. Biega, Krishna P. Gummadi, and Gerhard Weikum. 2018. Equity of Attention: Amortizing Individual Fairness in Rankings. In Proceedings of SIGIR. 405--414.

Digital Library

[16]

S. Bikhchandani, D. Hirshleifer, and I. Welch. 1992. A theory of fads, fashion, custom and cultural change as information cascade. The Journal of Political Economy 100 (1992), 992--1026.

[17]

István Bíró, Jácint Szabó, and András A. Benczúr. 2008. Latent dirichlet allocation in web spam filtering. In Proceedings of AIRWeb 2008, Fourth International Workshop on Adversarial Information Retrieval on the Web. 29--32.

[18]

S. Brenner. 2010. Location (hotelling) games and applications. Wiley Encyclopedia of Operations Research and Management Science.

[19]

Sergey Brin and Lawrence Page. 1998. The Anatomy of a Large-Scale Hypertextual Web Search Engine. In Proceedings of WWW. 107--117.

Digital Library

[20]

Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language Models are Few-Shot Learners. arXiv:2005.14165

[21]

Chris Buckley, Gerard Salton, James Allan, and Amit Singhal. 1994. Automatic query expansion using SMART: TREC3. In Proceedings of TREC. 69--80.

[22]

Christopher J. C. Burges. 2010. From RankNet to LambdaRank to LambdaMart: An overview. Technical Report. Microsoft.

[23]

James P. Callan. 1994. Passage-level Evidence in Document Retrieval. In Proceedings of SIGIR. 302--310.

[24]

David Carmel, Elad Haramaty, Arnon Lazerson, and Liane Lewin-Eytan. 2020. Multi-Objective Ranking Optimization for Product Search Using Stochastic Label Aggregation. In Proc. of the WebConf. 373--383.

Digital Library

[25]

Carlos Castillo. 2018. Fairness and Transparency in Ranking. SIGIR Forum 52, 2 (2018), 64--71.

Digital Library

[26]

Carlos Castillo, Claudio Corsi, Debora Donato, Paolo Ferragina, and Aristides Gionis. 2008. Query-log mining for detecting spam. In Proceedings of AIRWeb, Fourth International Workshop on Adversarial Information Retrieval on the Web. 17--20.

Digital Library

[27]

Carlos Castillo and Brian D. Davison. 2010. Adversarial Web Search. Foundations and Trends in Information Retrieval 4, 5 (2010), 377--486.

Digital Library

[28]

Carlos Castillo, Debora Donato, Luca Becchetti, Paolo Boldi, Stefano Leonardi, Massimo Santini, and Sebastiano Vigna. 2006. A reference collection for web spam. SIGIR Forum 40, 2 (2006), 11--24.

Digital Library

[29]

Carlos Castillo, Debora Donato, Aristides Gionis, Vanessa Murdock, and Fabrizio Silvestri. 2007. Know your neighbors: web spam detection using the web topology. In Proceedings of SIGIR. 423--430.

Digital Library

[30]

Gordon V. Cormack, Mark D. Smucker, and Charles L. A. Clarke. 2011. Efficient and effective spam filtering and re-ranking for large web datasets. Informaltiom Retrieval Journal 14, 5 (2011), 441--465.

Digital Library

[31]

W. Bruce Croft and John Lafferty (Eds.). 2003. Language Modeling for Information Retrieval. Number 13 in Information Retrieval Book Series. Kluwer.

[32]

Stephan Dempe. 2002. Foundations of Bilevel Programming. Springer.

[33]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. CoRR abs/1810.04805 (2018).

[34]

Fernando Diaz, Bhaskar Mitra, Michael D. Ekstrand, Asia J. Biega, and Ben Carterette. 2020. Evaluating Stochastic Rankings with Expected Exposure. In Proceedings of CIKM. 275--284.

Digital Library

[35]

Yinpeng Dong, Hang Su, Baoyuan Wu, Zhifeng Li, Wei Liu, Tong Zhang, and Jun Zhu. 2019. Efficient Decision-Based Black-Box Adversarial Attacks on Face Recognition. In Proceedings of CVPR. 7714--7722.

[36]

Kfir Eliaz and Ran Spiegler. 2011. A simple model of search engine pricing. The Economic Journal 121, 556 (2011), F329--F339.

[37]

Hui Fang, Tao Tao, and ChengXiang Zhai. 2011. Diagnostic Evaluation of Information Retrieval Models. ACM Trans. Inf. Syst. 29, 2 (2011), 7:1--7:42.

Digital Library

[38]

Hui Fang and ChengXiang Zhai. 2005. An exploration of axiomatic approaches to information retrieval. In Proceedings of SIGIR. 480--487.

Digital Library

[39]

Dennis Fetterly, Mark Manasse, and Marc Najork. 2004. Spam, Damn Spam, and Statistics: Using Statistical Analysis to Locate Spam Web Pages. In Proceedings of WebDB. 1--6.

Digital Library

[40]

Andrey Fradkin. 2017. Search, Matching, and the Role of Digital Marketplace Design in Enabling Trade: Evidence from Airbnb. Available at SSRN: https://ssrn.com/abstract=2939084 or http://dx.doi.org/10.2139/ssrn.2939084.

[41]

Andrey Fradkin. 2019. A Simulation Approach to Designing Digital Matching Platforms. Available at SSRN: https://ssrn.com/abstract=3320080 or http://dx.doi.org/10.2139/ssrn.3320080.

[42]

Norbert Fuhr. 2008. A probability ranking principle for interactive information retrieval. Information Retrieval 11, 3 (2008), 251--265.

Digital Library

[43]

Jianfeng Gao, Chenyan Xiong, Paul Bennett, and Nick Craswell. 2022. Neural Approaches to Conversational Information Retrieval. CoRR abs/2201.05176 (2022).

[44]

Shlomo Geva, Jaap Kamps, and Ralf Schenkel (Eds.). 2012. Focused Retrieval of Content and Structure, 10th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX. Lecture Notes in Computer Science, Vol. 7424.

[45]

Arpita Ghosh and R. Preston McAfee. 2011. Incentivizing high-quality usergenerated content. In Proceedings of WWW. 137--146.

[46]

Arpita Ghosh and R. Preston McAfee. 2012. Crowdsourcing with endogenous entry. In Proceedings of WWW. 999--1008.

Digital Library

[47]

Yu Gong, Xusheng Luo, Kenny Q. Zhu, Wenwu Ou, Zhao Li, and Lu Duan. 2019. Automatic Generation of Chinese Short Product Titles for Mobile Display. In Proc. of AAAI.

Digital Library

[48]

Gregory Goren, Oren Kurland, Moshe Tennenholtz, and Fiana Raiber. 2018. Ranking Robustness Under Adversarial Document Manipulations. In Proceedings of SIGIR. 395--404.

Digital Library

[49]

Gregory Goren, Oren Kurland, Moshe Tennenholtz, and Fiana Raiber. 2020. Ranking-Incentivized Quality Preserving Content Modification. In Proceedings of SIGIR. 259--268.

Digital Library

[50]

Gregory Goren, Oren Kurland, Moshe Tennenholtz, and Fiana Raiber. 2021. Driving the Herd: Search Engines as Content Influencers. CoRR abs/2110.11166 (2021).

Digital Library

[51]

Jiafeng Guo, Yixing Fan, Qingyao Ai, and W. Bruce Croft. 2016. A Deep Relevance Matching Model for Ad-hoc Retrieval. In Proceedings of CIKM. 55--64.

[52]

Jiafeng Guo, Yixing Fan, Liang Pang, Liu Yang, Qingyao Ai, Hamed Zamani, Chen Wu, W. Bruce Croft, and Xueqi Cheng. 2020. A Deep Look into neural ranking models for information retrieval. Inf. Process. Manag. 57, 6 (2020).

[53]

Zoltán Gyöngyi and Hector Garcia-Molina. 2005. Web Spam Taxonomy. In Proc. of AIRWeb 2005. 39--47.

[54]

Zoltán Gyöngyi, Hector Garcia-Molina, and Jan O. Pedersen. 2004. Combating Web Spam with TrustRank. In Proceedgins of VLDB. 576--587.

[55]

Donna Harman. 1988. Towards interactive query expansion. In Proc. of SIGIR. 321--331.

Digital Library

[56]

John C. Harsanyi. 1967. Games with Incomplete Information Played by "Bayesian" Players, I--III Part I. The Basic Model. Management Science 14, 3 (1967).

[57]

Warren He, James Wei, Xinyun Chen, Nicholas Carlini, and Dawn Song. 2017. Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong. CoRR abs/1706.04701 (2017).

[58]

Sandy Huang, Nicolas Papernot, Ian J. Goodfellow, Yan Duan, and Pieter Abbeel. 2017. Adversarial Attacks on Neural Network Policies. In Proc. of ICLR.

[59]

Peter Izsak, Fiana Raiber, Oren Kurland, and Moshe Tennenholtz. 2014. The search duel: a response to a strong ranker. In Proceedings of SIGIR. 919--922.

Digital Library

[60]

Jacob, Olivier Chapelle, and Carlos Castillo. 2008. Web spam identification through content and hyperlinks. In Proceedings of AIRWeb 2008, Fourth International Workshop on Adversarial Information Retrieval on the Web. 41--44.

[61]

N. Jardine and C. J. van Rijsbergen. 1971. The use of hierarchic clustering in information retrieval. Information Storage and Retrieval 7, 5 (1971), 217--240.

[62]

Robin Jia, Aditi Raghunathan, Kerem Göksel, and Percy Liang. 2019. Certified Robustness to Adversarial Word Substitutions. In Proceedings of EMNLP-IJCNLP. 4127--4140.

[63]

Thorsten Joachims. 2002. Optimizing Search Engines Using Clickthrough Data. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 133--142.

Digital Library

[64]

Thorsten Joachims, Laura Granka, Bing Pan, Helene Hembrooke, and Geri Gay. 2005. Accurately interpreting clickthrough data as implicit feedback. In Proc. of SIGIR. 154--161.

Digital Library

[65]

Timothy Jones, Ramesh S. Sankaranarayana, David Hawking, and Nick Craswell. 2009. Nullification test collections for web spam and SEO. In Proceedings of AIRWeb. 53--60.

Digital Library

[66]

Diane Kelly and Xin Fu. 2006. Elicitation of term relevance feedback: an investigation of term source and context. In Proc. of SIGIR. 453--460.

Digital Library

[67]

E. Koutsoupias and C. Papadimitriou. 1999. Worst-Case Equilibria. In Proc. of STACS.

[68]

Adam D. I. Kramer, Jamie Elizabeth Guillory, and Jeffrey T. Hancock. 2014. Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences of the United States of America 111 24 (2014), 8788--90.

[69]

Oren Kurland and Lillian Lee. 2004. Corpus structure, language models, and ad hoc information retrieval. In Proceedings of SIGIR. 194--201.

Digital Library

[70]

Victor Lavrenko and W. Bruce Croft. 2001. Relevance-Based Language Models. In Proceedings of SIGIR. 120--127.

[71]

Linyang Li, Ruotian Ma, Qipeng Guo, Xiangyang Xue, and Xipeng Qiu. 2020. BERT-ATTACK: Adversarial Attack Against BERT Using BERT. In Proc. of EMNLP. 6193--6202.

[72]

Jimmy Lin, Rodrigo Nogueira, and Andrew Yates. 2020. Pretrained Transformers for Text Ranking: BERT and Beyond. CoRR abs/2010.06467 (2020).

[73]

Tie-Yan Liu. 2011. Learning to Rank for Information Retrieval. Springer. I--XVII, 1--285 pages.

Digital Library

[74]

Xiaoyong Liu and W. Bruce Croft. 2002. Passage retrieval based on language models. In Proceedings of CIKM. 375--382.

[75]

Xiaoyong Liu and W. Bruce Croft. 2004. Cluster-Based Retrieval Using Language Models. In Proceedings of SIGIR. 186--193.

[76]

Rishabh Mehrotra and Benjamin A. Carterette. 2019. Recommendations in a marketplace. In Proceedings of RecSys, Toine Bogers, Alan Said, Peter Brusilovsky, and Domonkos Tikk (Eds.). 580--581.

[77]

Bhaskar Mitra and Nick Craswell. 2018. An Introduction to Neural Information Retrieval. Foundations and Trends in Information Retrieval 13, 1 (2018), 1--126.

Digital Library

[78]

Bhaskar Mitra, Fernando Diaz, and Nick Craswell. 2017. Learning to Match using Local and Distributed Representations of Text for Web Search. In Proceedings of WWW. 1291--1299.

Digital Library

[79]

Mandar Mitra, Amit Singhal, and Chris Buckley. 1998. Improving Automatic Query Expansion. In Proceedings of SIGIR. 206--214.

Digital Library

[80]

Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard S. Zemel, and Craig Boutilier. 2020. Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach. In Proceedings of ICML. 6987--6998.

[81]

Dov Monderer and Moshe Tennenholtz. 2004. K-Implementation. J. Artif. Intell. Res. (JAIR) 21 (2004), 37--62.

Digital Library

[82]

John F. Nash. 1950. Equilibrium Points in N-Person Games. Proc. of the National Academy of Sciences of the United States of America 36.1 (1950), 48--49.

[83]

Alexandros Ntoulas, Marc Najork, Mark Manasse, and Dennis Fetterly. 2006. Detecting spam web pages through content analysis. In Proceedings of WWW. 83--92.

Digital Library

[84]

Nicolas Papernot, Patrick D. McDaniel, Ian J. Goodfellow, Somesh Jha, Z. Berkay Celik, and Ananthram Swami. 2017. Practical Black-Box Attacks against Machine Learning. In Proc. of AsiaCCS. 506--519.

Digital Library

[85]

Kira Radinsky and Paul N. Bennett. 2013. Predicting content change on the web. In Proceedings of WSDM. 415--424.

[86]

Kira Radinsky, Fernando Diaz, Susan T. Dumais, Milad Shokouhi, Anlei Dong, and Yi Chang. 2013. Temporal web dynamics and its application to information retrieval. In Proceedings of WSDM. 781--782.

Digital Library

[87]

Nimrod Raifer, Fiana Raiber, Moshe Tennenholtz, and Oren Kurland. 2017. Information Retrieval Meets Game Theory: The Ranking Competition Between Documents' Authors. In Proceedings of SIGIR. 465--474.

Digital Library

[88]

Stephen E. Robertson. 1977. The Probability Ranking Principle in IR. Journal of Documentation (1977), 294--304. Reprinted in K. Sparck Jones and P. Willett (eds), Readings in Information Retrieval, pp. 281--286, 1997.

Digital Library

[89]

Stephen E. Robertson and Steve Walker. 1994. Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval. In Proceedings of SIGIR. 232--241.

Digital Library

[90]

Stephen E. Robertson, Steve Walker, Susan Jones, Micheline Hancock-Beaulieu, and Mike Gatford. 1994. Okapi at TREC-3. In Proceedings of TREC.

[91]

Joseph John Rocchio. 1971. Relevance Feedback in Information Retrieval. In The SMART Retrieval System: Experiments in Automatic Document Processing, Gerard Salton (Ed.). Prentice Hall, 313--323.

[92]

T. Roughgarden and E. Tardos. 2002. How bad is selfish routing? J. ACM 49, 2 (April 2002), 236--259.

Digital Library

[93]

Gerard Salton and Chris Buckley. 1990. Improving retrieval performance by relevance feedback. Journal of the American Society for Information Science (JASIS) 41, 4 (1990), 288--297.

[94]

Jerard Salton, Anita Wong, and Chung Shu Yang. 1975. A vector space model for automatic indexing. Commun. ACM 18, 11 (1975), 613--620.

Digital Library

[95]

Aécio S. R. Santos, Bruno Pasini, and Juliana Freire. 2016. A First Study on Temporal Dynamics of Topics on the Web. In Proceedings of WWW. 849--854.

Digital Library

[96]

Rodrygo L. T. Santos, Craig MacDonald, and Iadh Ounis. 2015. Search Result Diversification. Foundations and Trends in Information Retrieval 9, 1 (2015), 1--90.

Digital Library

[97]

Y. Shoham and M. Tennenholtz. 1995. Social Laws for Artificial Agent Societies: Off-line Design. Artificial Intelligence 73 (1995).

[98]

Ashudeep Singh and Thorsten Joachims. 2018. Fairness of Exposure in Rankings. In Proceedings of SIGKDD. 2219--2228.

Digital Library

[99]

L. Smith and P. Sorensen. 2000. Pathalogical outcomes of observational learning. Econometrica 68 (2000), 371--398.

[100]

Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus. 2014. Intriguing properties of neural networks. In Proc. of ICLR.

[101]

Bin Tan, Atulya Velivelli, Hui Fang, and ChengXiang Zhai. 2007. Term feedback for information retrieval with language models. In Proc. of SIGIR. 263--270.

Digital Library

[102]

Moshe Tennenholtz and Oren Kurland. 2019. Rethinking search engines and recommendation systems: a game theoretic perspective. Commun. ACM 62, 12 (2019), 66--75.

Digital Library

[103]

Florian Tramér, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel. 2020. Ensemble Adversarial Training: Attacks and Defenses. arXiv:1705.07204 [stat.ML]

[104]

Ziv Vasilisky, Moshe Tennenholtz, and Oren Kurland. 2020. Studying RankingIncentivized Web Dynamics. In Proceedings of SIGIR. 2093--2096.

[105]

Ellen M. Voorhees. 1985. The cluster hypothesis revisited. In Proceedings of SIGIR. 188--196.

Digital Library

[106]

Chen Wu, Ruqing Zhang, Jiafeng Guo, Yixing Fan, and Xueqi Cheng. 2021. Are Neural Ranking Models Robust?

[107]

Cihang Xie, Jianyu Wang, Zhishuai Zhang, Yuyin Zhou, Lingxi Xie, and Alan L. Yuille. 2017. Adversarial Examples for Semantic Segmentation and Object Detection. In Proc. of ICCV 2017. 1378--1387.

[108]

Chenyan Xiong, Zhuyun Dai, Jamie Callan, Zhiyuan Liu, and Russell Power. 2017. End-to-End Neural Ad-hoc Ranking with Kernel Pooling. In Proc. of SIGIR. 55--64.

Digital Library

[109]

Jinxi Xu and W. Bruce Croft. 1996. Query Expansion using Local and Global Document Analysis. In Proceedings of SIGIR. 4--11.

[110]

Grace Hui Yang, Marc Sloan, and Jun Wang. 2016. Dynamic Information Retrieval Modeling. Morgan & Claypool Publishers.

[111]

Haixuan Yang, Irwin King, and Michael R. Lyu. 2007. DiffusionRank: a possible penicillin for web spamming. In Proceedings of SIGIR. 431--438.

[112]

Zhilin Yang, Zihang Dai, Yiming Yang, Jaime G. Carbonell, Ruslan Salakhutdinov, and Quoc V. Le. 2019. XLNet: Generalized Autoregressive Pretraining for Language Understanding. In Proc. of NeurIPS. 5754--5764.

[113]

Hamed Zamani, Johanne R. Trippas, Jeff Dalton, and Filip Radlinski. 2022. Conversational Information Seeking. CoRR abs/2201.08808 (2022).

[114]

Meike Zehlike, Francesco Bonchi, Carlos Castillo, Sara Hajian, Mohamed Megahed, and Ricardo A. Baeza-Yates. 2017. FA*IR: A Fair Top-k Ranking Algorithm. In Proceedings of CIKM. 1569--1578.

[115]

Chengxiang Zhai. 2021. Interactive Information Retrieval: Models, Algorithms, and Evaluation. In Proceedings of SIGIR. 2662--2665.

Digital Library

[116]

Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, and Michael I. Jordan. 2019. Theoretically Principled Trade-off between Robustness and Accuracy. In Proceedings of ICML. 7472--7482.

[117]

Jianguo Zhang, Pengcheng Zou, Zhao Li, Yao Wan, Xiuming Pan, Yu Gong, and Philip S. Yu. 2019. Multi-Modal Generative Adversarial Network for Short Product Title Generation in Mobile E-Commerce. In Proc. of NAACL-HLT. 64--72.

[118]

Guido Zuccon and Leif Azzopardi. 2010. Using the Quantum Probability Ranking Principle to Rank Interdependent Documents. In Proceedings of ECIR. 357--369.

Digital Library

Cited By

Liu YZhang RGuo Jde Rijke MHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Robust Information RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3661380(3009-3012)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3661380
Liu YZhang RGuo Jde Rijke MFan YCheng XHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Multi-granular Adversarial Attacks against Black-box Neural Ranking ModelsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657704(1391-1400)Online publication date: 11-Jul-2024
https://doi.org/10.1145/3626772.3657704
Phillips JChow YRogers HBlaszczynski A(2024)The search term ‘suicide’ is being used to lead web browsers to online casinosBehaviour & Information Technology10.1080/0144929X.2023.2298307(1-12)Online publication date: 5-Jan-2024
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Index Terms

Competitive Search
1. Information systems
  1. Information retrieval
    1. Search engine architectures and scalability
      1. Adversarial retrieval

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cover image ACM Conferences

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

3569 pages

ISBN:9781450387323

DOI:10.1145/3477495

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Julio Gonzalo
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RMIT University
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Gabriella Kazai
Waseda University

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Liu YZhang RGuo Jde Rijke MHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Robust Information RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3661380(3009-3012)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3661380
Liu YZhang RGuo Jde Rijke MFan YCheng XHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Multi-granular Adversarial Attacks against Black-box Neural Ranking ModelsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657704(1391-1400)Online publication date: 11-Jul-2024
https://doi.org/10.1145/3626772.3657704
Phillips JChow YRogers HBlaszczynski A(2024)The search term ‘suicide’ is being used to lead web browsers to online casinosBehaviour & Information Technology10.1080/0144929X.2023.2298307(1-12)Online publication date: 5-Jan-2024
https://doi.org/10.1080/0144929X.2023.2298307
Bevendorff JWiegmann MPotthast MStein B(2024)Is Google Getting Worse? A Longitudinal Investigation of SEO Spam in Search EnginesAdvances in Information Retrieval10.1007/978-3-031-56063-7_4(56-71)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56063-7_4
Wu CZhang RGuo JDe Rijke MFan YCheng X(2023)PRADA: Practical Black-box Adversarial Attacks against Neural Ranking ModelsACM Transactions on Information Systems10.1145/357692341:4(1-27)Online publication date: 8-Apr-2023
https://dl.acm.org/doi/10.1145/3576923

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