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Found 17 papers, 5 papers with code
A Large Scale Search Dataset for Unbiased Learning to Rank
The unbiased learning to rank (ULTR) problem has been greatly advanced by recent deep learning techniques and well-designed debias algorithms.
Enhanced Doubly Robust Learning for Debiasing Post-click Conversion Rate Estimation
Based on it, a more robust doubly robust (MRDR) estimator has been proposed to further reduce its variance while retaining its double robustness.
Neural Interactive Collaborative Filtering
Therefore, the proposed exploration policy, to balance between learning the user profile and making accurate recommendations, can be directly optimized by maximizing users' long-term satisfaction with reinforcement learning.
User Retention-oriented Recommendation with Decision Transformer
However, deploying the DT in recommendation is a non-trivial problem because of the following challenges: (1) deficiency in modeling the numerical reward value; (2) data discrepancy between the policy learning and recommendation generation; (3) unreliable offline performance evaluation.
Model-based Unbiased Learning to Rank
Existing methods in unbiased learning to rank typically rely on click modeling or inverse propensity weighting (IPW).
Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems
Though reinforcement learning~(RL) naturally fits the problem of maximizing the long term rewards, applying RL to optimize long-term user engagement is still facing challenges: user behaviors are versatile and difficult to model, which typically consists of both instant feedback~(e. g. clicks, ordering) and delayed feedback~(e. g. dwell time, revisit); in addition, performing effective off-policy learning is still immature, especially when combining bootstrapping and function approximation.
Toward Simulating Environments in Reinforcement Learning Based Recommendations
Thus, it calls for a user simulator that can mimic real users' behaviors where we can pre-train and evaluate new recommendation algorithms.
Meta-Learning for Neural Relation Classification with Distant Supervision
Distant supervision provides a means to create a large number of weakly labeled data at low cost for relation classification.
Optimal Mixture Weights for Off-Policy Evaluation with Multiple Behavior Policies
Off-policy evaluation is a key component of reinforcement learning which evaluates a target policy with offline data collected from behavior policies.
Data-Efficient Reinforcement Learning for Malaria Control
This work introduces a practical, data-efficient policy learning method, named Variance-Bonus Monte Carlo Tree Search~(VB-MCTS), which can copy with very little data and facilitate learning from scratch in only a few trials.
Pre-trained Language Model based Ranking in Baidu Search
However, it is nontrivial to directly apply these PLM-based rankers to the large-scale web search system due to the following challenging issues:(1) the prohibitively expensive computations of massive neural PLMs, especially for long texts in the web-document, prohibit their deployments in an online ranking system that demands extremely low latency;(2) the discrepancy between existing ranking-agnostic pre-training objectives and the ad-hoc retrieval scenarios that demand comprehensive relevance modeling is another main barrier for improving the online ranking system;(3) a real-world search engine typically involves a committee of ranking components, and thus the compatibility of the individually fine-tuned ranking model is critical for a cooperative ranking system.
A Survey on Reinforcement Learning for Recommender Systems
To understand the challenges and relevant solutions, there should be a reference for researchers and practitioners working on RL-based recommender systems.
PReGAN: Answer Oriented Passage Ranking with Weakly Supervised GAN
Beyond topical relevance, passage ranking for open-domain factoid question answering also requires a passage to contain an answer (answerability).
Approximated Doubly Robust Search Relevance Estimation
We further instantiate the proposed unbiased relevance estimation framework in Baidu search, with comprehensive practical solutions designed regarding the data pipeline for click behavior tracking and online relevance estimation with an approximated deep neural network.
Whole Page Unbiased Learning to Rank
To address the above challenges, we propose a Bias Agnostic whole-page unbiased Learning to rank algorithm, named BAL, to automatically find the user behavior model with causal discovery and mitigate the biases induced by multiple SERP features with no specific design.
Unconfounded Propensity Estimation for Unbiased Ranking
The goal of unbiased learning to rank (ULTR) is to leverage implicit user feedback for optimizing learning-to-rank systems.
Evolutionary Reinforcement Learning: A Systematic Review and Future Directions
In response to the limitations of reinforcement learning and evolutionary algorithms (EAs) in complex problem-solving, Evolutionary Reinforcement Learning (EvoRL) has emerged as a synergistic solution.
