Towards Disentangling Relevance and Bias in Unbiased Learning to Rank
Topics: AI (Deep Learning), Learning-to-rank, Marc Najork, Ranking, Scoring, User Signals
The paper is titled “Towards Disentangling Relevance and Bias in Unbiased Learning to Rank” by authors affiliated with various institutions including the University of Illinois and Google Research. The study addresses the challenge of separating relevance and bias within the context of unbiased learning to rank (ULTR). This research is particularly focused on mitigating various biases that arise from implicit user feedback data, such as clicks, which have been a significant area of interest in recent times. The study employs a two-tower architecture approach, which is popular in real-world applications, where click modeling is divided into a relevance tower with regular input features and a bias tower with bias-relevant inputs like the position of a document.
Your advantages:
+ Full analysis of hundreds of well researched active Microsoft and Google patents and research paper.
+ Save a lot of time and get insights in just a few minutes, without having to spend hours analyzing the documents.
+ Get quick exclusive insights about how search engines and Google could work with easy to understand summaries and analysis.
+ All patents classified by topic for targeted research.
+ New patent summaries and analysis every week. Weekly notification via E-Mail
+ Use all 4 AI Research Tools to gain insights from all documents in the database, the Google API Leak, Antitrust trial documents, the whole google support documents, fundamental knowledge from worlds best SEO experts, Bill Slawskis fundamental research work for the industry, books about semantic search and more in seconds.
+ Gain fundamental insights for your SEO work and become a real thought leader.
Get access to the SEO Research Suite and become a SEO thought leader now!
