6 research outputs found
Convergence of Learning Dynamics in Information Retrieval Games
We consider a game-theoretic model of information retrieval with strategic
authors. We examine two different utility schemes: authors who aim at
maximizing exposure and authors who want to maximize active selection of their
content (i.e. the number of clicks). We introduce the study of author learning
dynamics in such contexts. We prove that under the probability ranking
principle (PRP), which forms the basis of the current state of the art ranking
methods, any better-response learning dynamics converges to a pure Nash
equilibrium. We also show that other ranking methods induce a strategic
environment under which such a convergence may not occur
Ranking-Incentivized Quality Preserving Content Modification
The Web is a canonical example of a competitive retrieval setting where many
documents' authors consistently modify their documents to promote them in
rankings. We present an automatic method for quality-preserving modification of
document content -- i.e., maintaining content quality -- so that the document
is ranked higher for a query by a non-disclosed ranking function whose rankings
can be observed. The method replaces a passage in the document with some other
passage. To select the two passages, we use a learning-to-rank approach with a
bi-objective optimization criterion: rank promotion and content-quality
maintenance. We used the approach as a bot in content-based ranking
competitions. Analysis of the competitions demonstrates the merits of our
approach with respect to human content modifications in terms of rank
promotion, content-quality maintenance and relevance.Comment: 10 pages. 8 figures. 3 table
Matching of Users and Creators in Two-Sided Markets with Departures
Many online platforms of today, including social media sites, are two-sided
markets bridging content creators and users. Most of the existing literature on
platform recommendation algorithms largely focuses on user preferences and
decisions, and does not simultaneously address creator incentives. We propose a
model of content recommendation that explicitly focuses on the dynamics of
user-content matching, with the novel property that both users and creators may
leave the platform permanently if they do not experience sufficient engagement.
In our model, each player decides to participate at each time step based on
utilities derived from the current match: users based on alignment of the
recommended content with their preferences, and creators based on their
audience size. We show that a user-centric greedy algorithm that does not
consider creator departures can result in arbitrarily poor total engagement,
relative to an algorithm that maximizes total engagement while accounting for
two-sided departures. Moreover, in stark contrast to the case where only users
or only creators leave the platform, we prove that with two-sided departures,
approximating maximum total engagement within any constant factor is NP-hard.
We present two practical algorithms, one with performance guarantees under mild
assumptions on user preferences, and another that tends to outperform
algorithms that ignore two-sided departures in practice