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An Offline Metric for the Debiasedness of Click Models

Click models aim to identify and extract biases in search data, but we do not evaluate whether they actually do so. We propose a metric based on relevance annotations that measures the debiasedness of click models, and consequently their usefulness in learning-to-rank from implicit feedback.

Offline Evaluation for Reinforcement Learning-based Recommendation - A Critical Issue and Some Alternatives

We discussed offline evaluation for RL-based recommender systems and noted that the most common evaluation protocol, i.e., next-item prediction, is unsuited to such approaches.

Generative Slate Recommendation with Reinforcement Learning

Learning to recommend slates with RL is hard, because of the combinatorial action-space. Instead of making assumptions on user behavior, we tried taking actions in the latent space of a VAE, which then decodes slates.

Articles Tagged: "Selected_publication"

An Offline Metric for the Debiasedness of Click Models

Offline Evaluation for Reinforcement Learning-based Recommendation - A Critical Issue and Some Alternatives

Generative Slate Recommendation with Reinforcement Learning