Search Results for author:
Found 6 papers, 2 papers with code
Competitive Retrieval: Going Beyond the Single Query
Previous work on the competitive retrieval setting focused on a single-query setting: document authors manipulate their documents so as to improve their future ranking for a given query.
A Dataset for Sentence Retrieval for Open-Ended Dialogues
We report the performance of several retrieval baselines, including neural retrieval models, over the dataset.
Driving the Herd: Search Engines as Content Influencers
We present a first study of the ability of search engines to drive pre-defined, targeted, content effects in the corpus using simple techniques.
Studying Ranking-Incentivized Web Dynamics
The ranking incentives of many authors of Web pages play an important role in the Web dynamics.
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.
A Passage-Based Approach to Learning to Rank Documents
Specifically, we devise a suite of learning-to-rank-based document retrieval methods that utilize an effective ranking of passages produced in response to the query; the passage ranking is also induced using a learning-to-rank approach.
