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Deep Reinforcement Learning for Sponsored Search Real-time Bidding [article]

Jun Zhao, Guang Qiu, Ziyu Guan, Wei Zhao, Xiaofei He
2018 arXiv   pre-print
Rather than generating bid prices directly, we decide a bidding model for impressions of each hour and perform real-time bidding accordingly.  ...  Most previous methods for DA cannot be applied. We propose a reinforcement learning (RL) solution for handling the complex dynamic environment.  ...  The contribution of this work is summarized as follows: (1) We propose a novel research problem, Sponsored Search Real-Time Bidding (SS-RTB), and properly motivate it. (2) A novel deep reinforcement learning  ... 
arXiv:1803.00259v1 fatcat:kqjd46oojfdh5p2kin42w7muua

Optimizing AD Pruning of Sponsored Search with Reinforcement Learning [article]

Yijiang Lian, Zhijie Chen, Xin Pei, Shuang Li, Yifei Wang, Yuefeng Qiu, Zhiheng Zhang, Zhipeng Tao, Liang Yuan, Hanju Guan, Kefeng Zhang, Zhigang Li (+1 others)
2020 arXiv   pre-print
It is also the first time to use reinforcement learning techniques to tackle this problem.  ...  The idea has been successfully realized in Baidu's sponsored search system, and online long time A/B test shows remarkable improvements on revenue.  ...  However, there are only a few existing works that incorporate RL/DRL techniques to sponsored search, e.g. [15] for Real-Time Bidding.  ... 
arXiv:2008.02014v1 fatcat:k6jy4tsxczg6nicst6j5nduosa

Optimizing Sponsored Search Ranking Strategy by Deep Reinforcement Learning [article]

Li He, Liang Wang, Kaipeng Liu, Bo Wu, Weinan Zhang
2018 arXiv   pre-print
In this work, we proposed a generic framework to optimize the ranking functions by deep reinforcement learning methods.  ...  From the advertisers' side, participating in ranking the search results by paying for the sponsored search advertisement to attract more awareness and purchase facilitates their commercial goal.  ...  in the real-time bidding display advertising as a reinforcement learning problem. e method is based on assumptions that the winning rate depends only on the bid price and the actual clicks can be well  ... 
arXiv:1803.07347v3 fatcat:ufe36fw3srefhpgbpnqigarpr4

Reinforcement Mechanism Design, with Applications to Dynamic Pricing in Sponsored Search Auctions [article]

Weiran Shen, Binghui Peng, Hanpeng Liu, Michael Zhang, Ruohan Qian, Yan Hong, Zhi Guo, Zongyao Ding, Pengjun Lu, Pingzhong Tang
2017 arXiv   pre-print
We implement our approach within the current sponsored search framework of a major search engine: we first train a buyer behavior model, via a real bidding data set, that accurately predicts bids given  ...  In this study, we apply reinforcement learning techniques and propose what we call reinforcement mechanism design to tackle the dynamic pricing problem in sponsored search auctions.  ...  to train a deep neural network in our setting, i.e. deep Q-learning network(DQN) [14] or asynchronous advantage actor-critic (A3C) [15] .  ... 
arXiv:1711.10279v1 fatcat:yggucdoabnardkh5tiavbkbmuu

Reinforcement Mechanism Design: With Applications to Dynamic Pricing in Sponsored Search Auctions

Weiran Shen, Binghui Peng, Hanpeng Liu, Michael Zhang, Ruohan Qian, Yan Hong, Zhi Guo, Zongyao Ding, Pengjun Lu, Pingzhong Tang
We first train a buyer behavior model, with a real bidding data set from a major search engine, that predicts bids given information disclosed by the search engine and the bidders' performance data from  ...  We then formulate the dynamic pricing problem as an MDP and apply a reinforcement-based algorithm that optimizes reserve prices over time.  ...  Deep reinforcement learning also showed powerful potential of developing control policies in physical systems.  ... 
doi:10.1609/aaai.v34i02.5600 fatcat:rkqbztco3jcupmcpefjkrpy3yu

Diversity driven Query Rewriting in Search Advertising [article]

Akash Kumar Mohankumar, Nikit Begwani, Amit Singh
2021 arXiv   pre-print
For head and torso search queries, sponsored search engines use a huge repository of same intent queries and keywords, mined ahead of time.  ...  In this work, we introduce CLOVER, a framework to generate both high-quality and diverse rewrites by optimizing for human assessment of rewrite quality using our diversity-driven reinforcement learning  ...  Matching search queries with bid keywords online in real-time places latency and compute constraints on the matching algorithm, often resulting in lower quality and coverage.  ... 
arXiv:2106.03816v1 fatcat:2oak2rrn5za4hdktscajmh7ioi

Identifying machine learning techniques for classification of target advertising

Jin-A Choi, Kiho Lim
2020 ICT Express  
This study investigates and classifies various machine learning techniques that are used to enhance targeted online advertising.  ...  Twenty-three machine learning-based online targeted advertising strategies are identified and classified largely into two categories, user-centric and content-centric approaches.  ...  Real-Time Bidding Real-time bidding (RTB) through machine learning allows instantaneous decisions to be made on whether to show a particular advertisement to a specific user based on insights gained from  ... 
doi:10.1016/j.icte.2020.04.012 fatcat:5qnbssw625chhfeeqkwzkgcjxm

Reinforcement mechanism design

Pingzhong Tang
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
schemes of advertisement auctions of the search engine.  ...  For the Taobao case, our framework automatically designs mechanisms that allocate buyer impressions for the e-commerce website; for the Baidu case, our framework automatically designs dynamic reserve pricing  ...  Optimization via Deep Reinforcement Learning As argued in the previous section, the MDP has continuous action spaces and exponential many states in the number of sellers.  ... 
doi:10.24963/ijcai.2017/739 dblp:conf/ijcai/Tang17a fatcat:2qqutl5xtzgk7dqwylpsnttzd4

Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising [article]

Di Wu, Xiujun Chen, Xun Yang, Hao Wang, Qing Tan, Xiaoxun Zhang, Jian Xu, Kun Gai
2018 arXiv   pre-print
Real-time bidding (RTB) is an important mechanism in online display advertising, where a proper bid for each page view plays an essential role for good marketing results.  ...  Therefore, we innovate a reward function design methodology for the reinforcement learning problems with constraints.  ...  Deep Reinforcement Learning to Bid Putting them together, we present our Deep Reinforcement Learning to Bid (DRLB) framework.  ... 
arXiv:1802.08365v5 fatcat:cnrfajyftjch3dfdkdhxvo6dlq

GamePlan: Game-Theoretic Multi-Agent Planning with Human Drivers at Intersections, Roundabouts, and Merging [article]

Rohan Chandra, Dinesh Manocha
2022 arXiv   pre-print
We compare with methods based on DRL, deep learning, and game theory and present our benefits over these approaches.  ...  We compare our approach with prior state-of-the-art auction techniques including economic auctions, time-based auctions (first-in first-out), and random bidding and show that each of these methods result  ...  [13] in terms of computational time in real world application. Kai et al. [13] use multi-task deep reinforcement learning which takes on average 12 seconds to turn at an intersection.  ... 
arXiv:2109.01896v5 fatcat:katwxrsulbdktndcb7ejfvnn4u

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.  ...  The learning rate is 0.0001 for supervised learning and 0.00001 for reinforcement learning. The models were run at most 10 epochs for supervised learning and 2 epochs for reinforcement learning.  ... 
arXiv:1902.10374v1 fatcat:jfmsyq23lrbe7fcphs6aqqtjse

Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting [article]

Jun Wang, Weinan Zhang, Shuai Yuan
2017 arXiv   pre-print
The most significant progress in recent years in online display advertising is what is known as the Real-Time Bidding (RTB) mechanism to buy and sell ads.  ...  RTB essentially facilitates buying an individual ad impression in real time while it is still being generated from a user's visit.  ...  We would expect deep reinforcement learning techniques [Mnih et al., 2015] to be explored for modelling the bidding decision process.  ... 
arXiv:1610.03013v2 fatcat:f2ewm5rdhzfi3pdndao4uww6re

Bid Prediction in Repeated Auctions with Learning [article]

Gali Noti, Vasilis Syrgkanis
2020 arXiv   pre-print
We consider the problem of bid prediction in repeated auctions and evaluate the performance of econometric methods for learning agents using a dataset from a mainstream sponsored search auction marketplace  ...  We propose new econometric approaches to simultaneously learn the parameters of a player's utility and her learning rule, and apply these methods in a real-world dataset from the BingAds sponsored search  ...  Using a large auction dataset from Microsoft's BingAds sponsored search auction marketplace, we show that regret-based bid prediction methods perform comparable to bid-based time-series machine learning  ... 
arXiv:2007.13193v2 fatcat:ckcrbxdbnbeptbtrke34etozbe

CIA-Towards a Unified Marketing Optimization Framework for e-Commerce Sponsored Search [article]

Hao Liu, Qinyu Cao, Xinru Liao, Guang Qiu, Sheng Li, Jiming Chen
2019 arXiv   pre-print
As the largest e-commerce platform, Taobao helps advertisers reach billions of search queries each day via sponsored search, which has also contributed considerable revenue to the platform.  ...  Moreover, CIA has been deployed online as a major bidding tool in TSA.  ...  A similar idea was also used in JD DSP business, with deep Q-learner in its bidder . Reinforcement learning is a straightforward solution to the scenario of delayed rewards in online advertising.  ... 
arXiv:1806.05799v2 fatcat:wgvddroidnd4tdmxerr7q62ncq

Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics [article]

Amir Mosavi, Pedram Ghamisi, Yaser Faghan, Puhong Duan
2020 arXiv   pre-print
DRL through a wide range of capabilities from reinforcement learning (RL) and deep learning (DL) for handling sophisticated dynamic business environments offers vast opportunities.  ...  The popularity of deep reinforcement learning (DRL) methods in economics have been exponentially increased.  ...  Zhao et al [122] focused on real-time bidding (RTB) applied to sponsored search (SS) auction in complicated stochastic environment associated with user action and bidding policies (see Table 15 ).  ... 
arXiv:2004.01509v1 fatcat:4ggjzkfdi5fe3g7uwb7dwbvtue
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