On the Power of Strategic Corpus Enrichment in Content Creation Games
Haya Nachimovsky, Moshe Tennenholtz

TL;DR
This paper demonstrates that strategic corpus enrichment with dummy documents can ensure stability and convergence to high-welfare equilibria in content ranking games, extending classical ranking algorithms.
Contribution
It introduces corpus enrichment techniques that guarantee equilibrium existence and convergence in content creation games, a novel approach extending Borel's Colonel Blotto game.
Findings
Enrichment can lead to pure Nash equilibrium.
Enrichment ensures convergence of best-response dynamics.
Tight bounds on document numbers needed.
Abstract
Search and recommendation ecosystems exhibit competition among content creators. This competition has been tackled in a variety of game-theoretic frameworks. Content creators generate documents with the aim of being recommended by a content ranker for various information needs. In order for the ecosystem, modeled as a content ranking game, to be effective and maximize user welfare, it should guarantee stability, where stability is associated with the existence of pure Nash equilibrium in the corresponding game. Moreover, if the contents' ranking algorithm possesses a game in which any best-response learning dynamics of the content creators converge to equilibrium of high welfare, the system is considered highly attractive. However, as classical content ranking algorithms, employed by search and recommendation systems, rank documents by their distance to information needs, it has been…
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Taxonomy
TopicsNatural Language Processing Techniques · Topic Modeling · Semantic Web and Ontologies
