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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  ...  Search, recommendation, and online advertising are the three most important information-providing mechanisms on the web.  ...  -search, recommendation, and online advertising.  ... 
arXiv:1812.07127v4 fatcat:pyc75g5hufcs5b3f75gonbkp24

Ads and Fraud: A Comprehensive Survey of Fraud in Online Advertising

Shadi Sadeghpour, Natalija Vlajic
2021 Journal of Cybersecurity and Privacy  
We then review different categories of ad fraud and present a taxonomy of known attacks on an online advertising system.  ...  Unfortunately, due to its large-scale adoption and significant revenue potential, digital advertising has become a very attractive and frequent target for numerous cybercriminal groups.  ...  with a reinforcement learning architecture-can be deployed.  ... 
doi:10.3390/jcp1040039 fatcat:hdcfm7gimvfbnk7kp2jzx2xyfi

A Survey on Reinforcement Learning for Recommender Systems [article]

Yuanguo Lin, Yong Liu, Fan Lin, Lixin Zou, Pengcheng Wu, Wenhua Zeng, Huanhuan Chen, Chunyan Miao
2022 arXiv   pre-print
To understand the challenges and relevant solutions, there should be a reference for researchers and practitioners working on RL-based recommender systems.  ...  In particular, Reinforcement Learning (RL) based recommender systems have become an emerging research topic in recent years.  ...  MemN2N [67] DQN Offline SCPR [68] DQN Online & Offline UNICORN [69] DQN Online & Offline CRM [70] Policy Search REINFORCE Online & Offline EAR [71] REINFORCE Online & Offline CRSAL [72] Actor-Critic  ... 
arXiv:2109.10665v2 fatcat:wx5ghn66hzg7faxee54jf7gspq

Reinforcement learning based recommender systems: A survey [article]

M. Mehdi Afsar, Trafford Crump, Behrouz Far
2022 arXiv   pre-print
In this paper, a survey on reinforcement learning based recommender systems (RLRSs) is presented.  ...  However, a new trend has emerged in the field since the introduction of deep reinforcement learning (DRL), which made it possible to apply RL to the recommendation problem with large state and action spaces  ...  ACKNOWLEDGEMENTS We wish to thank the anonymous reviewers for their constructional comments on the first versions of this paper.  ... 
arXiv:2101.06286v2 fatcat:alfslgagzvek5gx5kfepxc7xae

A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions [article]

Xiaocong Chen, Lina Yao, Julian McAuley, Guanglin Zhou, Xianzhi Wang
2021 arXiv   pre-print
In light of the emergence of deep reinforcement learning (DRL) in recommender systems research and several fruitful results in recent years, this survey aims to provide a timely and comprehensive overview  ...  of the recent trends of deep reinforcement learning in recommender systems.  ...  Why Deep Reinforcement Learning for Recommendation?  ... 
arXiv:2109.03540v2 fatcat:5gwrbfcj3rc7jfkd54eseck5ga

Domain-Constrained Advertising Keyword Generation [article]

Hao Zhou, Minlie Huang, Yishun Mao, Changlei Zhu, Peng Shu, Xiaoyan Zhu
2019 arXiv   pre-print
Advertising (ad for short) keyword suggestion is important for sponsored search to improve online advertising and increase search revenue. There are two common challenges in this task.  ...  Furthermore, a reinforcement learning algorithm is proposed to adaptively utilize domain-specific information in keyword generation.  ...  Advertising (ad for short) keyword suggestion is an important task for sponsored search which is one of the major types of online advertising and the major source of revenue for search companies.  ... 
arXiv:1902.10374v1 fatcat:jfmsyq23lrbe7fcphs6aqqtjse

User Response Prediction in Online Advertising [article]

Zhabiz Gharibshah, Xingquan Zhu
2021 arXiv   pre-print
In this survey, we provide a comprehensive review of user response prediction in online advertising and related recommender applications.  ...  Online advertising, as the vast market, has gained significant attention in various platforms ranging from search engines, third-party websites, social media, and mobile apps.  ...  As a result, scalability is a major challenge for recommender and online advertising.  ... 
arXiv:2101.02342v2 fatcat:clgefamcd5fmbeg5ephizy3zqu

RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising [article]

David Rohde, Stephen Bonner, Travis Dunlop, Flavian Vasile, Alexandros Karatzoglou
2018 arXiv   pre-print
in offline metrics for supervised learning and the online performance of the newly proposed models.  ...  We believe that this is an important step forward for the field of recommendation systems research, that could open up an avenue of collaboration between the recommender systems and reinforcement learning  ...  CONCLUSIONS In this paper we introduce RecoGym, the first Reinforcement Learning environment for recommendation in the context of online advertising.  ... 
arXiv:1808.00720v2 fatcat:img6bmapjjak3a27wuicmta5pi

We Know What You Want: An Advertising Strategy Recommender System for Online Advertising [article]

Liyi Guo, Junqi Jin, Haoqi Zhang, Zhenzhe Zheng, Zhiye Yang, Zhizhuang Xing, Fei Pan, Lvyin Niu, Fan Wu, Haiyang Xu, Chuan Yu, Yuning Jiang (+1 others)
2021 arXiv   pre-print
Providing good advertising experiences for advertisers through reducing their costs of trial and error for discovering the optimal advertising strategies is crucial for the long-term prosperity of online  ...  In this work, we first deploy a prototype of strategy recommender system on Taobao display advertising platform, recommending bid prices and targeted users to advertisers.  ...  We first design a prototype system for bid price and crowd recommendation, and prove its effectiveness through online A/B test.  ... 
arXiv:2105.14188v2 fatcat:t7n42qqo7ffifgfiyzipsgel34

Deep Meta-learning in Recommendation Systems: A Survey [article]

Chunyang Wang, Yanmin Zhu, Haobing Liu, Tianzi Zang, Jiadi Yu, Feilong Tang
2022 arXiv   pre-print
Therefore, this survey provides a timely and comprehensive overview of current deep meta-learning based recommendation methods.  ...  Recently, a growing number of studies on deep meta-learning based recommenddation systems have emerged for improving the performance under recommendation scenarios where available data is limited, e.g.  ...  Deep Meta-learning in Recommendation Systems: A Survey 111:29  ... 
arXiv:2206.04415v1 fatcat:w5rax6bjy5efjfmxunvf4j6kly

A Survey on Deep Reinforcement Learning for Data Processing and Analytics [article]

Qingpeng Cai, Can Cui, Yiyuan Xiong, Wei Wang, Zhongle Xie, Meihui Zhang
2022 arXiv   pre-print
Recently, reinforcement learning, deep reinforcement learning (DRL) in particular, is increasingly explored and exploited in many areas because it can learn better strategies in complicated environments  ...  Then, we survey the application of DRL in data processing and analytics, ranging from data preparation, natural language processing to healthcare, fintech, etc.  ...  We hope the survey would serve as a basis for research and development in this emerging area, and better integration of DRL techniques into data processing pipelines and stacks.  ... 
arXiv:2108.04526v3 fatcat:kcusgp7jzfbf7ov5os7gwf2e6i

A Survey of Reinforcement Learning Algorithms for Dynamically Varying Environments [article]

Sindhu Padakandla
2020 arXiv   pre-print
Reinforcement learning (RL) algorithms find applications in inventory control, recommender systems, vehicular traffic management, cloud computing and robotics.  ...  This paper provides a survey of RL methods developed for handling dynamically varying environment models.  ...  Most of the prior works are targeted towards deep reinforcement learning (DRL), where only deep neural network architectures are used for function approximation of value functions and policies.  ... 
arXiv:2005.10619v1 fatcat:35rikwhrwvcf7pvvn7rcokgzxq

Explainable Reinforcement Learning: A Survey [article]

Erika Puiutta, Eric MSP Veith
2020 arXiv   pre-print
But, especially considering Machine Learning (ML) methods like Reinforcement Learning (RL) where the system learns autonomously, the necessity to understand the underlying reasoning for their decisions  ...  Since, to the best of our knowledge, there exists no single work offering an overview of Explainable Reinforcement Learning (XRL) methods, this survey attempts to address this gap.  ...  distinction between Reinforcement Learning and Deep Reinforcement Learning (DRL), for the sake of simplicity, we will refer to both as just Reinforcement Learning going forward.  ... 
arXiv:2005.06247v1 fatcat:5qqohnjnongqbhovks3w5mx26e

A Survey on Trustworthy Recommender Systems [article]

Yingqiang Ge, Shuchang Liu, Zuohui Fu, Juntao Tan, Zelong Li, Shuyuan Xu, Yunqi Li, Yikun Xian, Yongfeng Zhang
2022 arXiv   pre-print
Through this survey, we hope to deliver readers with a comprehensive view of the research area and raise attention to the community about the importance, existing research achievements, and future research  ...  All of these create an urgent need for Trustworthy Recommender Systems (TRS) so as to mitigate or avoid such adverse impacts and risks.  ...  More specifically, for deep learning based CF, the similarity learning approach adopts simple user/item representations (such as one-hot vector) and learns a complex matching function (such as a neural  ... 
arXiv:2207.12515v1 fatcat:lsnuwdtl5rboznmhhux2n5y5om

A Survey on Skill Identification from Online Job Ads

Imane Khaouja, Ismail Kassou, Mounir Ghogho
2021 IEEE Access  
A changing job market, influenced by different factors such as globalization and demographic growth, urges close monitoring.  ...  The aim of this survey is to review current research on skill identification from job ads and to discuss possible future research directions.  ...  use of deep learning for skill tagging from job ads, e.g  ... 
doi:10.1109/access.2021.3106120 fatcat:6qesk5koenh37bgoxpfztlk4wa
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