Fair ranking: a critical review, challenges, and future directions
Ranking, recommendation, and retrieval systems are widely used in online platforms and
other societal systems, including e-commerce, media-streaming, admissions, gig platforms …
other societal systems, including e-commerce, media-streaming, admissions, gig platforms …
How Bad is Top- Recommendation under Competing Content Creators?
This study explores the impact of content creators' competition on user welfare in
recommendation platforms, as well as the long-term dynamics of relevance-driven …
recommendation platforms, as well as the long-term dynamics of relevance-driven …
Modeling content creator incentives on algorithm-curated platforms
Content creators compete for user attention. Their reach crucially depends on algorithmic
choices made by developers on online platforms. To maximize exposure, many creators …
choices made by developers on online platforms. To maximize exposure, many creators …
Rethinking search engines and recommendation systems: a game theoretic perspective
M Tennenholtz, O Kurland - Communications of the ACM, 2019 - dl.acm.org
Rethinking search engines and recommendation systems: a game theoretic perspective
Page 1 66 COMMUNICATIONS OF THE ACM | DECEMBER 2019 | VOL. 62 | NO. 12 review …
Page 1 66 COMMUNICATIONS OF THE ACM | DECEMBER 2019 | VOL. 62 | NO. 12 review …
Are neural ranking models robust?
Recently, we have witnessed the bloom of neural ranking models in the information retrieval
(IR) field. So far, much effort has been devoted to developing effective neural ranking …
(IR) field. So far, much effort has been devoted to developing effective neural ranking …
A game-theoretic approach to recommendation systems with strategic content providers
O Ben-Porat, M Tennenholtz - Advances in Neural …, 2018 - proceedings.neurips.cc
We introduce a game-theoretic approach to the study of recommendation systems with
strategic content providers. Such systems should be fair and stable. Showing that traditional …
strategic content providers. Such systems should be fair and stable. Showing that traditional …
Competitive search
O Kurland, M Tennenholtz - Proceedings of the 45th International ACM …, 2022 - dl.acm.org
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 …
authors with ranking incentives: their goal is to promote their documents in rankings induced …
Robust neural information retrieval: An adversarial and out-of-distribution perspective
Recent advances in neural information retrieval (IR) models have significantly enhanced
their effectiveness over various IR tasks. The robustness of these models, essential for …
their effectiveness over various IR tasks. The robustness of these models, essential for …
Towards Imperceptible Document Manipulations against Neural Ranking Models
Adversarial attacks have gained traction in order to identify potential vulnerabilities in neural
ranking models (NRMs), but current attack methods often introduce grammatical errors …
ranking models (NRMs), but current attack methods often introduce grammatical errors …
Dealing with textual noise for robust and effective BERT re-ranking
The pre-trained language models (PLMs), such as BERT, have been successfully employed
in two-phases ranking pipeline for information retrieval (IR). Meanwhile, recent studies have …
in two-phases ranking pipeline for information retrieval (IR). Meanwhile, recent studies have …