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Found 10 papers, 0 papers with code
Online Learning to Rank with Top-k Feedback
We consider two settings of online learning to rank where feedback is restricted to top ranked items.
Phased Exploration with Greedy Exploitation in Stochastic Combinatorial Partial Monitoring Games
The implementation of their algorithm depends on two separate offline oracles and the distribution dependent regret additionally requires existence of a unique optimal action for the learner.
Generalization error bounds for learning to rank: Does the length of document lists matter?
We consider the generalization ability of algorithms for learning to rank at a query level, a problem also called subset ranking.
Online Learning to Rank with Feedback at the Top
We consider an online learning to rank setting in which, at each round, an oblivious adversary generates a list of $m$ documents, pertaining to a query, and the learner produces scores to rank the documents.
Personalized Advertisement Recommendation: A Ranking Approach to Address the Ubiquitous Click Sparsity Problem
We study the problem of personalized advertisement recommendation (PAR), which consist of a user visiting a system (website) and the system displaying one of $K$ ads to the user.
Handling Class Imbalance in Link Prediction using Learning to Rank Techniques
We consider the link prediction problem in a partially observed network, where the objective is to make predictions in the unobserved portion of the network.
Perceptron like Algorithms for Online Learning to Rank
We show that, if there exists a perfect oracle ranker which can correctly rank each instance in an online sequence of ranking data, with some margin, the cumulative loss of perceptron algorithm on that sequence is bounded by a constant, irrespective of the length of the sequence.
Online Ranking with Top-1 Feedback
We consider a setting where a system learns to rank a fixed set of $m$ items.
On Lipschitz Continuity and Smoothness of Loss Functions in Learning to Rank
In binary classification and regression problems, it is well understood that Lipschitz continuity and smoothness of the loss function play key roles in governing generalization error bounds for empirical risk minimization algorithms.
Perceptron-like Algorithms and Generalization Bounds for Learning to Rank
En route to developing the online algorithm and generalization bound, we propose a novel family of listwise large margin ranking surrogates.
