2 citations · 3 across the 7 of their papers we have counts for
5 papers · 1 filter
RLRF: Competitive Search Agent Design via Reinforcement Learning from Ranker Feedback
Tommy Mordo, Sagie Dekel, Omer Madmon +2
Competitive search is a setting where document publishers modify them to improve their ranking in response to a query. Recently, publishers have increasingly leveraged LLMs to gene…
On the Merits of LLM-Based Corpus Enrichment
Gal Zur, Tommy Mordo, Moshe Tennenholtz +1
Generative AI (genAI) technologies -- specifically, large language models (LLMs) -- and search have evolving relations. We argue for a novel perspective: using genAI to enrich a do…
White Hat Search Engine Optimization using Large Language Models
Niv Bardas, Tommy Mordo, Oren Kurland +2
We present novel white-hat search engine optimization techniques based on genAI and demonstrate their empirical merits.
CSP: A Simulator For Multi-Agent Ranking Competitions
Tommy Mordo, Tomer Kordonsky, Haya Nachimovsky +2
In ranking competitions, document authors compete for the highest rankings by modifying their content in response to past rankings. Previous studies focused on human participants,…
Search results diversification in competitive search
Tommy Mordo, Itamar Reinman, Moshe Tennenholtz +1
In Web retrieval, there are many cases of competition between authors of Web documents: their incentive is to have their documents highly ranked for queries of interest. As such, t…