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AiAds: Automated and Intelligent Advertising System for Sponsored Search

Published: 25 July 2019 Publication History

Abstract

Sponsored search has more than 20 years of history, and it has been proven to be a successful business model for online advertising. Based on the pay-per-click pricing model and the keyword targeting technology, the sponsored system runs online auctions to determine the allocations and prices of search advertisements. In the traditional setting, advertisers should manually create lots of ad creatives and bid on some relevant keywords to target their audience. Due to the huge amount of search traffic and a wide variety of ad creations, the limits of manual optimizations from advertisers become the main bottleneck for improving the efficiency of this market. Moreover, as many emerging advertising forms and supplies are growing, it's crucial for sponsored search platform to pay more attention to the ROI metrics of ads for getting the marketing budgets of advertisers. In this paper, we present the AiAds system developed at Baidu, which use machine learning techniques to build an automated and intelligent advertising system. By designing and implementing the automated bidding strategy, the intelligent targeting and the intelligent creation models, the AiAds system can transform the manual optimizations into multiple automated tasks and optimize these tasks in advanced methods. AiAds is a brand-new architecture of sponsored search system which changes the bidding language and allocation mechanism, breaks the limit of keyword targeting with end-to-end ad retrieval framework and provides global optimization of ad creation. This system can increase the advertiser's campaign performance, the user experience and the revenue of the advertising platform simultaneously and significantly. We present the overall architecture and modeling techniques for each module of the system and share our lessons learned in solving several of key challenges. Finally, online A/B test and long-term grouping experiment demonstrate the advancement and effectiveness of this system.

References

[1]
Deepak Agarwal, Souvik Ghosh, Kai Wei, and Siyu You. 2014. Budget pacing for targeted online advertisements at linkedin. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1613--1619.
[2]
Tasos Anastasakos, Dustin Hillard, Sanjay Kshetramade, and Hema Raghavan. 2009. A collaborative filtering approach to ad recommendation using the query-ad click graph. In Proceedings of the 18th ACM conference on Information and knowledge management. ACM, 1927--1930.
[3]
Yoram Bachrach, Sofia Ceppi, Ian A Kash, Peter Key, and David Kurokawa. 2014. Optimising trade-offs among stakeholders in ad auctions. In Proceedings of the fifteenth ACM conference on Economics and computation. ACM, 75--92.
[4]
Baidu. 2019 a. Baidu ad formats. http://yingxiao.baidu.com/new/home/product/product/id/50?ly=product_union_author_lists Retrieved 2019 from
[5]
Baidu. 2019 b. Baidu conversion tracking. http://ocpc.baidu.com/developer/d/guide Retrieved 2019 from
[6]
Bing. 2019. Bing automated bid strategies. https://help.bingads.microsoft.com/apex/index/3/en/56786 Retrieved 2019 from
[7]
Andrei Broder, Peter Ciccolo, Evgeniy Gabrilovich, Vanja Josifovski, Donald Metzler, Lance Riedel, and Jeffrey Yuan. 2009. Online expansion of rare queries for sponsored search. In Proceedings of the 18th international conference on World wide web. ACM, 511--520.
[8]
Andrei Broder, Evgeniy Gabrilovich, Vanja Josifovski, George Mavromatis, and Alex Smola. 2011. Bid generation for advanced match in sponsored search. In Proceedings of the fourth ACM international conference on Web search and data mining. ACM, 515--524.
[9]
Andrei Z Broder, Peter Ciccolo, Marcus Fontoura, Evgeniy Gabrilovich, Vanja Josifovski, and Lance Riedel. 2008. Search advertising using web relevance feedback. In Proceedings of the 17th ACM conference on Information and knowledge management. ACM, 1013--1022.
[10]
Matthew Cary, Aparna Das, Ben Edelman, Ioannis Giotis, Kurtis Heimerl, Anna R Karlin, Claire Mathieu, and Michael Schwarz. 2007. Greedy bidding strategies for keyword auctions. In Proceedings of the 8th ACM conference on Electronic commerce. ACM, 262--271.
[11]
Ruggiero Cavallo, Prabhakar Krishnamurthy, Maxim Sviridenko, and Christopher A Wilkens. 2017. Sponsored search auctions with rich ads. In Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 43--51.
[12]
Yejin Choi, Marcus Fontoura, Evgeniy Gabrilovich, Vanja Josifovski, Mauricio Mediano, and Bo Pang. 2010. Using landing pages for sponsored search ad selection. In Proceedings of the 19th international conference on World wide web. ACM, 251--260.
[13]
Yuxiao Dong, Nitesh V Chawla, and Ananthram Swami. 2017. metapath2vec: Scalable representation learning for heterogeneous networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 135--144.
[14]
Eyal Even Dar, Vahab S Mirrokni, S Muthukrishnan, Yishay Mansour, and Uri Nadav. 2009. Bid optimization for broad match ad auctions. In Proceedings of the 18th international conference on World wide web. ACM, 231--240.
[15]
Jon Feldman and S Muthukrishnan. 2008. Algorithmic methods for sponsored search advertising. In Performance Modeling and Engineering . Springer, 91--122.
[16]
Negin Golrezaei, Ilan Lobel, and Renato Paes Leme. 2018. Auction Design for ROI-Constrained Buyers. (2018).
[17]
Google. 2019 a. Google ads extensions. https://support.google.com/google-ads/answer/2375499 Retrieved 2019 from
[18]
Google. 2019 b. Google automated bid strategies. https://support.google.com/google-ads/answer/2979071?hl=en Retrieved 2019 from
[19]
Mihajlo Grbovic, Nemanja Djuric, Vladan Radosavljevic, Fabrizio Silvestri, Ricardo Baeza-Yates, Andrew Feng, Erik Ordentlich, Lee Yang, and Gavin Owens. 2016. Scalable semantic matching of queries to ads in sponsored search advertising. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 375--384.
[20]
Sonal Gupta, Mikhail Bilenko, and Matthew Richardson. 2009. Catching the drift: learning broad matches from clickthrough data. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 1165--1174.
[21]
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems. 1024--1034.
[22]
Jason Hartline, Nicole Immorlica, Mohammad Reza Khani, Brendan Lucier, and Rad Niazadeh. 2018. Fast Core Pricing for Rich Advertising Auctions. In Proceedings of the 2018 ACM Conference on Economics and Computation. ACM, 111--112.
[23]
Benamin Heymann. 2018. ROI constrained Auctions. arXiv preprint arXiv:1809.08837 (2018).
[24]
Jukka Jyl"anki. 2010. A thousand ways to pack the bin-a practical approach to two-dimensional rectangle bin packing. retrived from http://clb. demon. fi/files/RectangleBinPack. pdf (2010).
[25]
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. Lightgbm: A highly efficient gradient boosting decision tree. In Advances in Neural Information Processing Systems. 3146--3154.
[26]
Kuang-Chih Lee, Ali Jalali, and Ali Dasdan. 2013. Real time bid optimization with smooth budget delivery in online advertising. In Proceedings of the Seventh International Workshop on Data Mining for Online Advertising . ACM, 1.
[27]
Vahab Mirrokni, Renato Paes Leme, Pingzhong Tang, and Song Zuo. 2016. Dynamic auctions with bank accounts. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. AAAI Press, 387--393.
[28]
Vahab Mirrokni, Renato Paes Leme, Pingzhong Tang, and Song Zuo. 2017. Non-clairvoyant dynamic mechanism design. (2017).
[29]
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. 2013. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602 (2013).
[30]
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et almbox. 2015. Human-level control through deep reinforcement learning. Nature, Vol. 518, 7540 (2015), 529.
[31]
Sandeep Pandey, Kunal Punera, Marcus Fontoura, and Vanja Josifovski. 2010. Estimating advertisability of tail queries for sponsored search. In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval. ACM, 563--570.
[32]
Tao Qin, Wei Chen, and Tie-Yan Liu. 2015. Sponsored search auctions: Recent advances and future directions. ACM Transactions on Intelligent Systems and Technology (TIST), Vol. 5, 4 (2015), 60.
[33]
Sujith Ravi, Andrei Broder, Evgeniy Gabrilovich, Vanja Josifovski, Sandeep Pandey, and Bo Pang. 2010. Automatic generation of bid phrases for online advertising. In Proceedings of the third ACM international conference on Web search and data mining. ACM, 341--350.
[34]
Benjamin Rey and Ashvin Kannan. 2010. Conversion rate based bid adjustment for sponsored search. In Proceedings of the 19th international conference on World wide web. ACM, 1173--1174.
[35]
Yelong Shen, Xiaodong He, Jianfeng Gao, Li Deng, and Grégoire Mesnil. 2014a. A latent semantic model with convolutional-pooling structure for information retrieval. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. ACM, 101--110.
[36]
Yelong Shen, Xiaodong He, Jianfeng Gao, Li Deng, and Grégoire Mesnil. 2014b. Learning semantic representations using convolutional neural networks for web search. In Proceedings of the 23rd International Conference on World Wide Web. ACM, 373--374.
[37]
Hyun-Je Song, A Kim, Seong-Bae Park, et almbox. 2017. Translation of Natural Language Query Into Keyword Query Using a RNN Encoder-Decoder. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 965--968.
[38]
Statista. 2019. Search Advertising - worldwide | Statista Market Forecast. https://www.statista.com/outlook/219/100/search-advertising/worldwide Retrieved 2019 from
[39]
Yizhou Sun, Jiawei Han, Xifeng Yan, Philip S Yu, and Tianyi Wu. 2011. Pathsim: Meta path-based top-k similarity search in heterogeneous information networks. Proceedings of the VLDB Endowment, Vol. 4, 11 (2011), 992--1003.
[40]
Christopher A Wilkens, Ruggiero Cavallo, and Rad Niazadeh. 2016. Mechanism design for value maximizers. arXiv preprint arXiv:1607.04362 (2016).
[41]
Jian Xu, Kuang-chih Lee, Wentong Li, Hang Qi, and Quan Lu. 2015. Smart pacing for effective online ad campaign optimization. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 2217--2226.
[42]
Su Yan, Wei Lin, Tianshu Wu, Daorui Xiao, Xu Zheng, Bo Wu, and Kaipeng Liu. 2017. Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search. arXiv preprint arXiv:1712.10110 (2017).
[43]
Bianca Zadrozny and Charles Elkan. 2002. Transforming classifier scores into accurate multiclass probability estimates. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 694--699.
[44]
Weinan Zhang, Dingquan Wang, Gui-Rong Xue, and Hongyuan Zha. 2012a. Advertising keywords recommendation for short-text web pages using Wikipedia. ACM Transactions on Intelligent Systems and Technology (TIST), Vol. 3, 2 (2012), 36.
[45]
Weinan Zhang, Ying Zhang, Bin Gao, Yong Yu, Xiaojie Yuan, and Tie-Yan Liu. 2012b. Joint optimization of bid and budget allocation in sponsored search. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 1177--1185.
[46]
Wei Vivian Zhang, Xiaofei He, Benjamin Rey, and Rosie Jones. 2007. Query rewriting using active learning for sponsored search. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 853--854.
[47]
Ying Zhang, Weinan Zhang, Bin Gao, Xiaojie Yuan, and Tie-Yan Liu. 2014. Bid keyword suggestion in sponsored search based on competitiveness and relevance. Information Processing & Management, Vol. 50, 4 (2014), 508--523.
[48]
Jun Zhao, Guang Qiu, Ziyu Guan, Wei Zhao, and Xiaofei He. 2018. Deep Reinforcement Learning for Sponsored Search Real-time Bidding. arXiv preprint arXiv:1803.00259 (2018).
[49]
Han Zhu, Junqi Jin, Chang Tan, Fei Pan, Yifan Zeng, Han Li, and Kun Gai. 2017. Optimized cost per click in taobao display advertising. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2191--2200.

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cover image ACM Conferences
KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
July 2019
3305 pages
ISBN:9781450362016
DOI:10.1145/3292500
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 25 July 2019

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Author Tags

  1. automated bidding
  2. intelligent creation
  3. intelligent targeting
  4. sponsored search

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KDD '19 Paper Acceptance Rate 110 of 1,200 submissions, 9%;
Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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  • (2023)Optimal Real-Time Bidding Strategy for Position Auctions in Online AdvertisingProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614727(4766-4772)Online publication date: 21-Oct-2023
  • (2023) Feynman : Federated Learning-Based Advertising for Ecosystems-Oriented Mobile Apps Recommendation IEEE Transactions on Services Computing10.1109/TSC.2023.328593516:5(3361-3372)Online publication date: Sep-2023
  • (2022)Quantized training of gradient boosting decision treesProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3601637(18822-18833)Online publication date: 28-Nov-2022
  • (2022)A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online AdvertisingProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498373(1129-1139)Online publication date: 11-Feb-2022
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