May 18, 2021 · This paper studies the problem of learning interactive recommender systems from logged feedbacks without any exploration in online environments.
(2) We propose a general offline learning framework for interactive recommendation with logged feedbacks, in- cluding support constraints, supervised ...
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Abstract. This paper studies the problem of learning interactive recommender systems from logged feedbacks without any exploration in online environments.
This paper first introduces a probabilistic generative model for interactive recommendation, and then proposes an effective inference algorithm for discrete ...
Jan 20, 2020 · The proposed PDQ not only avoids the instability of convergence and high computation cost of existing approaches but also provides unlimited ...
WSDM, Pseudo Dyna-Q: A Reinforcement Learning Framework for Interactive Recommendation ... MOReL : Model-Based Offline Reinforcement Learning, https://arxiv.org/ ...
Jul 18, 2023 · In this paper, we aim to alleviate the Matthew effect in offline RL-based recommendation. Through theoretical analyses, we find that the conservatism of ...
Oct 30, 2023 · This Reinforcement Learning (RL) modulation can recombine high-value short-term transitions across different interaction trajectories to form ...
A practical alternative is to build a recommender agent offline from logged data, whereas directly using logged data offline leads to the problem of selection ...
It thereby propose a world model to reduce the instability of convergence and high computation cost for interacting with users by imitating the offline dataset.