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Model-Based Reinforcement Learning for Whole-Chain Recommendations [article]

Xiangyu Zhao and Long Xia and Dawei Yin and Jiliang Tang
2019 arXiv   pre-print
In this paper, we study the recommendation problem with multiple (consecutive) scenarios, i.e., whole-chain recommendations.  ...  With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in developing RL-based recommender systems.  ...  PROBLEM STATEMENT We formulate the whole-chain recommendation task as a multiagent model-based reinforcement learning problem.  ... 
arXiv:1902.03987v2 fatcat:stxyidjb5zey7bl6j2kdou3tgq

Reinforcement Learning for Online Information Seeking [article]

Xiangyu Zhao and Long Xia and Jiliang Tang and Dawei Yin
2019 arXiv   pre-print
In this paper, we give an overview of deep reinforcement learning for search, recommendation, and online advertising from methodologies to applications, review representative algorithms, and discuss some  ...  With recent great advances in deep reinforcement learning (DRL), there have been increasing interests in developing DRL based information seeking techniques.  ...  DDPG reduces the computational cost of conventional value-based reinforcement learning methods, thus it is a fitting choice for the whole page recommendation setting [Cai et al. 2018a; Cai et al. 2018b  ... 
arXiv:1812.07127v4 fatcat:pyc75g5hufcs5b3f75gonbkp24

JDRec: Practical Actor-Critic Framework for Online Combinatorial Recommender System [article]

Xin Zhao
2022 arXiv   pre-print
Novel and practical model architectures are designed for these sub-problems aiming at jointly optimizing effectiveness and efficiency.  ...  The CR is formulated as a combinatorial optimization problem with the objective of maximizing the recommendation reward of the whole list.  ...  learning for Recommender System.  ... 
arXiv:2207.13311v1 fatcat:eaarntabvvg7nmcotebtrjmrmy

Data science and AI in FinTech: An overview [article]

Longbing Cao, Qiang Yang, Philip S. Yu
2021 arXiv   pre-print
blockchain, and the DSAI techniques including complex system methods, quantitative methods, intelligent interactions, recognition and responses, data analytics, deep learning, federated learning, privacy-preserving  ...  Smart FinTech synthesizes broad DSAI and transforms finance and economies to drive intelligent, automated, whole-of-business and personalized economic and financial businesses, services and systems.  ...  Agentbased modeling Multiagent systems, belief-desireintention model, reactive model, swarm intelligence, reinforcement learning Testing economic hypotheses, simulating policies, supply chain relation  ... 
arXiv:2007.12681v2 fatcat:jntzuwaktjg2hmmjypi5lvyht4

Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation

Xiaocong Chen, Chaoran Huang, Lina Yao, Xianzhi Wang, Wei liu, Wenjie Zhang
2020 2020 International Joint Conference on Neural Networks (IJCNN)  
computational cost of reinforcement learning optimization, and performance degradation for reinforcement learning based recommendation systems.  ...  In particular, we propose a set of techniques and models for the improved interactive recommendation via reinforcement learning.  ...  DeepPage [13] : A DDPG-based reinforcement learning model that learns a ranking vector for page-wise recommendation.  ... 
doi:10.1109/ijcnn48605.2020.9207010 dblp:conf/ijcnn/Chen0Y00Z20 fatcat:3hlajcrneze4vbsnotyy475roa

Application of Reinforcement Learning Algorithm in Delivery Order System under Supply Chain Environment

Haozhe Huang, Xin Tan, Sang-Bing Tsai
2021 Mobile Information Systems  
This article introduces the strategy research of supply chain management order based on a reinforcement learning algorithm.  ...  based on the deep learning algorithm.  ...  Reinforcement Learning Problem and Supply Chain Structure Modeling Modeling of Reinforcement Learning Problems.  ... 
doi:10.1155/2021/5880795 fatcat:tra7k3qqovdvhf3ovy3afntwmi

Expert2Vec: Distributed Expert Representation Learning in Question Answering Community [chapter]

Xiaocong Chen, Chaoran Huang, Xiang Zhang, Xianzhi Wang, Wei Liu, Lina Yao
2019 Lecture Notes in Computer Science  
This finally adopts the reinforcement learning framework with the user-topic matrix to improve it internally.  ...  Hence hereby we propose Expert2Vec, a distributed Expert Representation learning in question answering community to boost the recommendation of the domain expert.  ...  Zhao et al. proposed a model-based deep RL for a sequential recommendation especially in whole-chain recommendation [27] which uses user's feedback as the reward and adopting the auto-encoder.  ... 
doi:10.1007/978-3-030-35231-8_21 fatcat:4nvg3r4b7ffsbf4qvly5kxscgy

Blockchain Framework for Artificial Intelligence Computation [article]

Jie You
2022 arXiv   pre-print
sharing and AI model design for common problems.  ...  Here, we pose proof-of-work as a reinforcement-learning problem by modeling the blockchain growing as a Markov decision process, in which a learning agent makes an optimal decision over the environment's  ...  Figure 1 The blockchain model based on reinforcement learning Figure 2 The mechanism for blocks to store data and being linked In any block of the chain, the stored Hash value of the previous block prevents  ... 
arXiv:2202.11264v1 fatcat:fz3ig4w5y5ffnoyn65mjp33mlm

Data science and AI in FinTech: an overview

Longbing Cao, Qiang Yang, Philip S. Yu
2021 International Journal of Data Science and Analytics  
blockchain, and the DSAI techniques including complex system methods, quantitative methods, intelligent interactions, recognition and responses, data analytics, deep learning, federated learning, privacy-preserving  ...  Smart FinTech synthesizes broad DSAI and transforms finance and economies to drive intelligent, automated, whole-of-business and personalized economic and financial businesses, services and systems.  ...  fraud in financial services and statements [12] ; and -federated learning models for privacy-preserving opendomain and whole-of-business financial applications and services [19, 57] .  ... 
doi:10.1007/s41060-021-00278-w fatcat:4qo3swacjbaaxh56p5bvhmjzqa

Introduction to the Special Issue on "Innovation and Application of Intelligent Processing of Data, Information and Knowledge in E-Commerce"

Honghao Gao, Jung Yoon Kim, Yuyu Yin
2022 CMES - Computer Modeling in Engineering & Sciences  
Acknowledgement: We would like to thank the authors for their contributions to this special issue. We also thank the journal of CMES for their supports for publications of this special issue.  ...  Funding Statement: The authors received no specific funding for this study. Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.  ...  In the paper entitled "Dynamic Pricing Model of E-Commerce Platforms Based on Deep Reinforcement Learning" by Yin et al.  ... 
doi:10.32604/cmes.2022.019665 fatcat:h5zxvu6kqvcqpex5qbu2hidpqm

Table of Content

2021 2021 IEEE 7th International Conference on Network Softwarization (NetSoft)  
Online Contextual Bandit 133 Machine Learning-Based Auto-Scaler for Video Conferencing Systems 142 Reinforcement Learning based Load Balancing for Data Center Networks 151 Flow-based Service  ...  9 Policy Gradient-based Deep Reinforcement Learning for Deadline-aware Transfer over Wide Area Networks 166 LiONv2: An Experimental Network Construction Tool Considering Disaggregation of Network Configuration  ... 
doi:10.1109/netsoft51509.2021.9492551 fatcat:kczddggaonawbh2ylhgwtxaxba

A Survey of Online Course Recommendation Techniques

Jinliang Lu
2022 Open Journal of Applied Sciences  
However, with the increasing amount of data information, it is increasingly difficult for people to find appropriate learning materials from a large number of educational resources.  ...  With the development of information technology, online learning has gradually become an indispensable way of knowledge acquisition.  ...  Acknowledgements The author is grateful to Jinan University for encouraging them to do this research.  ... 
doi:10.4236/ojapps.2022.121010 fatcat:ww7vgve2bvecjfjyzc2iqevcam

D2RLIR : an improved and diversified ranking function in interactive recommendation systems based on deep reinforcement learning [article]

Vahid Baghi, Seyed Mohammad Seyed Motehayeri, Ali Moeini, Rooholah Abedian
2021 arXiv   pre-print
This paper proposes a deep reinforcement learning based recommendation system by utilizing Actor-Critic architecture to model dynamic users' interaction with the recommender agent and maximize the expected  ...  Recently, interactive recommendation systems based on reinforcement learning have been attended by researchers due to the consider recommendation procedure as a dynamic process and update the recommendation  ...  reinforcement learning-based recommendation system.  ... 
arXiv:2110.15089v2 fatcat:t353qiznn5exdfevdjwvs2rb3a

Exact-K Recommendation via Maximal Clique Optimization [article]

Yu Gong, Yu Zhu, Lu Duan, Qingwen Liu, Ziyu Guan, Fei Sun, Wenwu Ou, Kenny Q. Zhu
2019 arXiv   pre-print
Then we propose Reinforcement Learning from Demonstrations (RLfD) which combines the advantages in behavior cloning and reinforcement learning, making it sufficient- and-efficient to train the model.  ...  It is different from traditional top-K recommendation, as it focuses more on (constrained) combinatorial optimization which will optimize to recommend a whole set of K items called card, rather than ranking  ...  GRU based listwise model (Listwise-GRU) a.k.a DLCM [2] is a SOTA model for whole-page ranking refinement. It applies GRU to encode the candidate items with a list-wise ranking loss.  ... 
arXiv:1905.07089v1 fatcat:llt5kzvxqngsto2lyugbrqr7ii

Explore-Exploit: A Framework for Interactive and Online Learning [article]

Honglei Liu, Anuj Kumar, Wenhai Yang, Benoit Dumoulin
2018 arXiv   pre-print
This framework provides a suite of online learning operators for various tasks such as personalization ranking, candidate selection and active learning.  ...  Interactive user interfaces need to continuously evolve based on the interactions that a user has (or does not have) with the system.  ...  Deploy and iterate on reinforcement learning models with proper exploration.  ... 
arXiv:1812.00116v1 fatcat:tjq2gdc3vrblrl2awwn5r2n3cq
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