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A Survey on Reinforcement Learning for Recommender Systems

A Survey on Reinforcement Learning for Recommender Systems

2026년 8월 29일1 min read

References

  • paper link: https://arxiv.org/pdf/2109.10665.pdf
  • Reinforcement Learning based Recommender Systems: A Survey

링크된 언급

1
Reinforcement Learning

machine learning 기법 중 하나. For RS A Survey on Reinforcement Learning for Recommender Systems Related

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Deep reinforcement learning for search, recommendation, and online advertising - a survey

Paper link arxiv.org/abs/1812.07127 .

Reinforcement Learning for Slate-based Recommender Systems - A Tractable Decomposition and Practical Methodology

Empirical Evaluation: Live Experiments YouTube 에 SARSA-TS 알고리즘을 실험 candidate -> ranker 를 거치게 되는데, ranker 의 scoring 함수에서 사용하는 myopic(근시안적) engagement 측정값을 LTV estimate 로 변경함...

DRN - A Deep Reinforcement Learning Framework for News Recommendation

Abstract 뉴스 추천을 위한 딥러닝 기반의 강화 학습 프레임워크를 제안한다. news feature 들과 user 의 preferences 의 변동성 (dynamic) 을 설명하는 것은 상당히 어렵다.

Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning

Discussion Q-learning based offline 학습 방식이고, 모델도 무겁고.

Reinforcement Learning

machine learning 기법 중 하나.

Solving Continual Combinatorial Selection via Deep Reinforcement Learning

Abstract S-MDP 문제를 다룬 paper 이다.

Deep Exploration via Bootstrapped DQN

Abstract 해결하려는 문제: 강화학습에서의 효율적인 exploration Randomized value functions offer a promising approach to efficient exploration with generalization, but existing algorithms are not...

Graph Learning based Recommender Systems

Graph 기반 추천을 하는 이유 추천 시스템 내 대부분의 데이터는 그래프 구조를 가진다. real world 에서 객체들은 서로 explicit 또는 implicit 하게 연결되어 있다. 그리고 그 객체는 사용자, 아이템, 속성등 이 될 수 있다.

Exploration by Random Network Distillation

RL methods work by maximizing the expected return of a policy.

Exploring compact reinforcement-learning representations with linear regression

paper Link: arxiv.org/pdf/1205.2606.pdf KWIK Linear Regression KWIK (Knows What It Knows) is a framework for studying supervised learning algorithms and was designed to unify...