paper list in the area of reinforcenment learning for recommendation systems

Overview

RL4Recsys

paper list in the area of reinforcenment learning for recommendation systems

https://github.com/cszhangzhen/DRL4Recsys

2020

SIGIR, Self-Supervised Reinforcement Learning for Recommender Systems, https://arxiv.org/abs/2006.05779

WSDM, Model-Based Reinforcement Learning for Whole-Chain Recommendations, https://arxiv.org/abs/1902.03987

WSDM, End-to-End Deep Reinforcement Learning based Recommendation with Supervised Embedding, https://dl.acm.org/doi/abs/10.1145/3336191.3371858

WSDM, Pseudo Dyna-Q: A Reinforcement Learning Framework for Interactive Recommendation, https://dl.acm.org/doi/abs/10.1145/3336191.3371801

AAAI, Simulating User Feedback for Reinforcement Learning Based Recommendations, https://arxiv.org/pdf/1906.11462.pdf

KBS, State representation modeling for deep reinforcement learning based recommendation, https://www.sciencedirect.com/science/article/abs/pii/S095070512030407X

MOReL : Model-Based Offline Reinforcement Learning, https://arxiv.org/abs/2005.05951

KDD, MBCAL: Sample Efficient and Variance Reduced Reinforcement Learning for Recommender Systems, https://arxiv.org/pdf/1911.02248.pdf

Generator and Critic: A Deep Reinforcement Learning Approach for Slate Re-ranking in E-commerce, https://arxiv.org/pdf/2005.12206.pdf

2019

NIPS, Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation, paper and code: http://papers.nips.cc/paper/9257-a-model-based-reinforcement-learning-with-adversarial-training-for-online-recommendation

NIPS, Benchmarking Batch Deep Reinforcement Learning Algorithms, https://arxiv.org/abs/1910.01708, code: https://github.com/sfujim/BCQ

ICML, Off-Policy Deep Reinforcement Learning without Exploration, https://arxiv.org/abs/1812.02900, code: https://github.com/sfujim/BCQ

ICML, Challenges of Real-World Reinforcement Learning, https://arxiv.org/abs/1904.12901

ICML, Horizon: Facebook's Open Source Applied Reinforcement Learning Platform, https://arxiv.org/pdf/1811.00260.pdf

ICML, Generative Adversarial User Model for Reinforcement Learning Based Recommendation System, paper and code, http://proceedings.mlr.press/v97/chen19f.html

KDD, Deep Reinforcement Learning for List-wise Recommendations,https://arxiv.org/pdf/1801.00209.pdf code: https://github.com/luozachary/drl-rec

WSDM, Top-K Off-Policy Correction for a REINFORCE Recommender System, https://arxiv.org/pdf/1812.02353.pdf

SigWeb, Deep reinforcement learning for search, recommendation, and online advertising: a survey, https://dl.acm.org/doi/abs/10.1145/3320496.3320500

UIST, Learning Cooperative Personalized Policies from Gaze Data, https://dl.acm.org/doi/abs/10.1145/3332165.3347933

Toward Simulating Environments in Reinforcement Learning Based Recommendations, https://arxiv.org/abs/1906.11462

RecSys, PyRecGym: a reinforcement learning gym for recommender systems, https://dl.acm.org/doi/abs/10.1145/3298689.3346981

Recsys, Revisiting offline evaluation for implicit-feedback recommender systems, https://dl.acm.org/doi/pdf/10.1145/3298689.3347069

IJCAI, Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology, https://arxiv.org/pdf/1905.12767.pdf

AAAI, Virtual-Taobao: Virtualizing Real-world Online Retail Environment for Reinforcement Learning, https://arxiv.org/pdf/1805.10000.pdf

WWW, Towards Neural Mixture Recommender for Long Range Dependent User Sequences, https://dl.acm.org/doi/abs/10.1145/3308558.3313650

Deep Reinforcement Learning for Online Advertising in Recommender Systems, https://arxiv.org/abs/1909.03602

Towards Characterizing Divergence in Deep Q-Learning, https://arxiv.org/abs/1903.08894

Dynamic Search -- Optimizing the Game of Information Seeking, https://arxiv.org/abs/1909.12425

RecSim: A Configurable Simulation Platform for Recommender Systems, https://arxiv.org/abs/1909.04847

2018

KDD, Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application, https://arxiv.org/pdf/1803.00710.pdf

WWW, DRN: A Deep Reinforcement Learning Framework for News Recommendation, http://www.personal.psu.edu/~gjz5038/paper/www2018_reinforceRec/www2018_reinforceRec.pdf

General RL Materials

https://github.com/higgsfield/RL-Adventure-2, PyTorch tutorial of: actor critic / proximal policy optimization / acer / ddpg / twin dueling ddpg / soft actor critic / generative adversarial imitation learning / hindsight experience replay

Key Papers from OpenAI, https://spinningup.openai.com/en/latest/spinningup/keypapers.html

Strategic Exploration in Reinforcement Learning - New Algorithms and Learning Guarantees, https://www.ml.cmu.edu/research/phd-dissertation-pdfs/cmu-ml-19-116-dann.pdf

Other Paper

Learning to Recommend via Meta Parameter Partition, https://arxiv.org/pdf/1912.04108.pdf

Adversarial Machine Learning in Recommender Systems: State of the art and Challenges, https://arxiv.org/abs/2005.10322

WWW20, Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations, https://dl.acm.org/doi/abs/10.1145/3366424.3386195

ICLR2020, On the Variance of the Adaptive Learning Rate and Beyond, https://github.com/LiyuanLucasLiu/RAdam, code: https://github.com/LiyuanLucasLiu/RAdam

WSDM2020, Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback, https://dl.acm.org/doi/abs/10.1145/3336191.3371783

Recsys2019, Recommending what video to watch next: a multitask ranking system, https://dl.acm.org/doi/abs/10.1145/3298689.3346997

Recsys2019, Addressing delayed feedback for continuous training with neural networks in CTR prediction, https://dl.acm.org/doi/abs/10.1145/3298689.3347002

IJCAI2019, Sequential Recommender Systems: Challenges, Progress and Prospects, https://arxiv.org/abs/2001.04830

KDD2019, Fairness in Recommendation Ranking through Pairwise Comparisons, https://dl.acm.org/doi/abs/10.1145/3292500.3330745

BoTorch: Programmable Bayesian Optimization in PyTorch, https://arxiv.org/abs/1910.06403

Implementation of the method proposed in the paper "Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation"

Neural Descriptor Fields (NDF) PyTorch implementation for training continuous 3D neural fields to represent dense correspondence across objects, and u

167 Jan 06, 2023
Code for the paper: Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization (https://arxiv.org/abs/2002.11798)

Representation Robustness Evaluations Our implementation is based on code from MadryLab's robustness package and Devon Hjelm's Deep InfoMax. For all t

Sicheng 19 Dec 07, 2022
In real-world applications of machine learning, reliable and safe systems must consider measures of performance beyond standard test set accuracy

PixMix Introduction In real-world applications of machine learning, reliable and safe systems must consider measures of performance beyond standard te

Andy Zou 79 Dec 30, 2022
TorchDistiller - a collection of the open source pytorch code for knowledge distillation, especially for the perception tasks, including semantic segmentation, depth estimation, object detection and instance segmentation.

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This is a work in progress reimplementation of Instant Neural Graphics Primitives

Neural Hash Encoding This is a work in progress reimplementation of Instant Neural Graphics Primitives Currently this can train an implicit representa

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CL-Gym: Full-Featured PyTorch Library for Continual Learning

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ICON: Implicit Clothed humans Obtained from Normals (CVPR 2022)

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Elegy is a framework-agnostic Trainer interface for the Jax ecosystem.

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435 Dec 30, 2022
Supplementary materials to "Spin-optomechanical quantum interface enabled by an ultrasmall mechanical and optical mode volume cavity" by H. Raniwala, S. Krastanov, M. Eichenfield, and D. R. Englund, 2022

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Stefan Krastanov 1 Jan 17, 2022
A Web API for automatic background removal using Deep Learning. App is made using Flask and deployed on Heroku.

Automatic_Background_Remover A Web API for automatic background removal using Deep Learning. App is made using Flask and deployed on Heroku. ๐Ÿ‘‰ https:

Gaurav 16 Oct 29, 2022
Joint Channel and Weight Pruning for Model Acceleration on Mobile Devices

Joint Channel and Weight Pruning for Model Acceleration on Mobile Devices Abstract For practical deep neural network design on mobile devices, it is e

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Official code for "On the Frequency Bias of Generative Models", NeurIPS 2021

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35 Nov 01, 2022
Conservative Q Learning for Offline Reinforcement Reinforcement Learning in JAX

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Karush Suri 8 Nov 07, 2022
The World of an Octopus: How Reporting Bias Influences a Language Model's Perception of Color

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1 Nov 13, 2021
Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations

Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations Code repo for paper Trans-Encoder: Unsupervised sentence-pa

Amazon 101 Dec 29, 2022
Collection of in-progress libraries for entity neural networks.

ENN Incubator Collection of in-progress libraries for entity neural networks: Neural Network Architectures for Structured State Entity Gym: Abstractio

25 Dec 01, 2022
Semantic Edge Detection with Diverse Deep Supervision

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OverFeat is a Convolutional Network-based image classifier and feature extractor.

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Making a music video with Wav2CLIP and VQGAN-CLIP

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Joel Jang | ์žฅ์š”์—˜ 163 Dec 26, 2022
vit for few-shot classification

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Martin Dong 26 Nov 30, 2022