Learning-To-Rank
174 papers with code • 0 benchmarks • 9 datasets
Learning to rank is the application of machine learning to build ranking models. Some common use cases for ranking models are information retrieval (e.g., web search) and news feeds application (think Twitter, Facebook, Instagram).
Benchmarks
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Libraries
Use these libraries to find Learning-To-Rank models and implementationsDatasets
Most implemented papers
SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval
In learning-to-rank for information retrieval, a ranking model is automatically learned from the data and then utilized to rank the sets of retrieved documents.
That is a Known Lie: Detecting Previously Fact-Checked Claims
Interestingly, despite the importance of the task, it has been largely ignored by the research community so far.
Distance-based Positive and Unlabeled Learning for Ranking
Learning to rank -- producing a ranked list of items specific to a query and with respect to a set of supervisory items -- is a problem of general interest.
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.
On the Problem of Underranking in Group-Fair Ranking
We give a fair ranking algorithm that takes any given ranking and outputs another ranking with simultaneous underranking and group fairness guarantees comparable to the lower bound we prove.
On the Relationship between Explanation and Recommendation: Learning to Rank Explanations for Improved Performance
Explaining to users why some items are recommended is critical, as it can help users to make better decisions, increase their satisfaction, and gain their trust in recommender systems (RS).
An Efficient Approach for Cross-Silo Federated Learning to Rank
Traditional learning-to-rank (LTR) models are usually trained in a centralized approach based upon a large amount of data.
Pairwise Learning for Neural Link Prediction
The framework treats link prediction as a pairwise learning to rank problem and consists of four main components, i. e., neighborhood encoder, link predictor, negative sampler and objective function.
Fantastic Rewards and How to Tame Them: A Case Study on Reward Learning for Task-oriented Dialogue Systems
Prior works mainly focus on adopting advanced RL techniques to train the ToD agents, while the design of the reward function is not well studied.
An Offline Metric for the Debiasedness of Click Models
We prove that debiasedness is a necessary condition for recovering unbiased and consistent relevance scores and for the invariance of click prediction under covariate shift.