Offline Recommender System Evaluation under Unobserved Confounding (CONSEQUENCES '23)

RecSys2023 CONSEQUENCES workshop - full recordingПодробнее

RecSys2023 CONSEQUENCES workshop - full recording

PS 5: How algorithmic confounding in recommendation systems increases homogeneity and...Подробнее

PS 5: How algorithmic confounding in recommendation systems increases homogeneity and...

Offline Recommender System Evaluation under Unobserved Confounding (CONSEQUENCES '23)Подробнее

Offline Recommender System Evaluation under Unobserved Confounding (CONSEQUENCES '23)

Tutorial 3C Offline Evaluation for Group Recommender SystemsПодробнее

Tutorial 3C Offline Evaluation for Group Recommender Systems

Session 9: Evaluation Framework for Cold Start Technologies in Large Scale Production SettingsПодробнее

Session 9: Evaluation Framework for Cold Start Technologies in Large Scale Production Settings

Online Evaluation Methods for the Causal Effect of RecommendationsПодробнее

Online Evaluation Methods for the Causal Effect of Recommendations

RecSys 2016: Paper Session 1 - Contrasting Offline and Online Results when Evaluating RecommendationПодробнее

RecSys 2016: Paper Session 1 - Contrasting Offline and Online Results when Evaluating Recommendation

Learn about Cold Start Problem in Recommendation Systems!Подробнее

Learn about Cold Start Problem in Recommendation Systems!

The future of evaluating Recommender Systems with User ExperimentsПодробнее

The future of evaluating Recommender Systems with User Experiments

Tutorial 3B Improving Recommender Systems with Human in the LoopПодробнее

Tutorial 3B Improving Recommender Systems with Human in the Loop

PS 6: Unbiased offline recommender evaluation for missing-not-at-random implicit feedbackПодробнее

PS 6: Unbiased offline recommender evaluation for missing-not-at-random implicit feedback

Debiased Off-Policy Evaluation for Recommender SystemsПодробнее

Debiased Off-Policy Evaluation for Recommender Systems

Recommender Systems: Classification, Evaluation | Pedro Ramaciotti MoralesПодробнее

Recommender Systems: Classification, Evaluation | Pedro Ramaciotti Morales

Towards Unified Metrics for Accuracy and Diversity for Recommender SystemsПодробнее

Towards Unified Metrics for Accuracy and Diversity for Recommender Systems

A Common Misassumption in Online Experiments with Machine Learning Models (PERSPECTIVES '23)Подробнее

A Common Misassumption in Online Experiments with Machine Learning Models (PERSPECTIVES '23)

Paper Session 6: Uplift-based Evaluation and Optimization of Recommenders - M Sato, et al.Подробнее

Paper Session 6: Uplift-based Evaluation and Optimization of Recommenders - M Sato, et al.