A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Filters
Reinforcement Learning for Online Information Seeking
[article]
2019
arXiv
pre-print
Preserved Fulltext
Other Versions
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
2021
Journal of Cybersecurity and Privacy
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2021 pre-print doi:10.1145/3447548.3467175 arXiv:2105.14188v1 fatcat:ax7a2imaqnb7na2jlb6f6eahqmOther Versions
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]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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
2021
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
« Previous
Showing results 1 — 15 out of 12,956 results