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Found 7 papers, 1 papers with code
Fair Effect Attribution in Parallel Online Experiments
We suggest an approach to measure and disentangle the effect of simultaneous experiments by providing a cost sharing approach based on Shapley values.
Low-variance estimation in the Plackett-Luce model via quasi-Monte Carlo sampling
When adopting the PL model as a ranking policy, both tasks require the computation of expectations with respect to the model.
Ranker-agnostic Contextual Position Bias Estimation
This paper introduces a method for modeling the probability of an item being seen in different contexts, e. g., for different users, with a single estimator.
A Linear Bandit for Seasonal Environments
Here we present a contextual bandit algorithm which detects and adapts to abrupt changes of the reward function and leverages previous estimations whenever the environment falls back to a previously observed state.
Knowledge-aware Autoencoders for Explainable Recommender Sytems
Recommender Systems have been widely used to help users in finding what they are looking for thus tackling the information overload problem.
Computing recommendations via a Knowledge Graph-aware Autoencoder
In the last years, deep learning has shown to be a game-changing technology in artificial intelligence thanks to the numerous successes it reached in diverse application fields.
Auto-Encoding User Ratings via Knowledge Graphs in Recommendation Scenarios
In the last decade, driven also by the availability of an unprecedented computational power and storage capabilities in cloud environments we assisted to the proliferation of new algorithms, methods, and approaches in two areas of artificial intelligence: knowledge representation and machine learning.
