Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
144 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Deep Automated Mechanism Design for Integrating Ad Auction and Allocation in Feed (2401.01656v2)

Published 3 Jan 2024 in cs.GT and cs.AI

Abstract: E-commerce platforms usually present an ordered list, mixed with several organic items and an advertisement, in response to each user's page view request. This list, the outcome of ad auction and allocation processes, directly impacts the platform's ad revenue and gross merchandise volume (GMV). Specifically, the ad auction determines which ad is displayed and the corresponding payment, while the ad allocation decides the display positions of the advertisement and organic items. The prevalent methods of segregating the ad auction and allocation into two distinct stages face two problems: 1) Ad auction does not consider externalities, such as the influence of actual display position and context on ad Click-Through Rate (CTR); 2) The ad allocation, which utilizes the auction-winning ad's payment to determine the display position dynamically, fails to maintain incentive compatibility (IC) for the advertisement. For instance, in the auction stage employing the traditional Generalized Second Price (GSP) , even if the winning ad increases its bid, its payment remains unchanged. This implies that the advertisement cannot secure a better position and thus loses the opportunity to achieve higher utility in the subsequent ad allocation stage. Previous research often focused on one of the two stages, neglecting the two-stage problem, which may result in suboptimal outcomes...

Definition Search Book Streamline Icon: https://streamlinehq.com
References (34)
  1. EXTR: Click-Through Rate Prediction with Externalities in E-Commerce Sponsored Search. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2732–2740.
  2. Hierarchically Constrained Adaptive Ad Exposure in Feeds. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 3003–3012.
  3. Calibrating user response predictions in online advertising. In Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track: European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part IV. Springer, 208–223.
  4. Georges E Dupret and Benjamin Piwowarski. 2008. A user browsing model to predict search engine click data from past observations.. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval. 331–338.
  5. Internet advertising and the generalized second-price auction: Selling billions of dollars worth of keywords. American economic review 97, 1 (2007), 242–259.
  6. Gabriele Farina and Nicola Gatti. 2016. Ad auctions and cascade model: GSP inefficiency and algorithms. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 30.
  7. Externalities among advertisers in sponsored search. In International Symposium on Algorithmic Game Theory. Springer, 105–116.
  8. A truthful learning mechanism for contextual multi-slot sponsored search auctions with externalities. In Proceedings of the 13th ACM Conference on Electronic Commerce. 605–622.
  9. Arpita Ghosh and Mohammad Mahdian. 2008. Externalities in online advertising. In Proceedings of the 17th international conference on World Wide Web. 161–168.
  10. Deep Position-wise Interaction Network for CTR Prediction. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1885–1889.
  11. David Kempe and Mohammad Mahdian. 2008. A cascade model for externalities in sponsored search. In International Workshop on Internet and Network Economics. Springer, 585–596.
  12. Towards a characterization of truthful combinatorial auctions. In 44th Annual IEEE Symposium on Foundations of Computer Science, 2003. Proceedings. IEEE, 574–583.
  13. Learning-Based Ad Auction Design with Externalities: The Framework and A Matching-Based Approach. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 1291–1302.
  14. Optimally integrating ad auction into E-commerce platforms. Theoretical Computer Science 976 (2023), 114141.
  15. NMA: Neural Multi-slot Auctions with Externalities for Online Advertising. arXiv preprint arXiv:2205.10018 (2022).
  16. Deep Page-Level Interest Network in Reinforcement Learning for Ads Allocation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2292–2296.
  17. Cross DQN: Cross Deep Q Network for Ads Allocation in Feed. arXiv preprint arXiv:2109.04353 (2021).
  18. Neural auction: End-to-end learning of auction mechanisms for e-commerce advertising. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 3354–3364.
  19. Whole page optimization: how page elements interact with the position auction. In Proceedings of the fifteenth ACM conference on Economics and computation. 583–600.
  20. Roger B Myerson. 1981. Optimal auction design. Mathematics of operations research 6, 1 (1981), 58–73.
  21. Sponsored search auctions: Recent advances and future directions. ACM Transactions on Intelligent Systems and Technology (TIST) 5, 4 (2015), 1–34.
  22. Kevin Roberts. 1979. The characterization of implementable choice rules. Aggregation and revelation of preferences 12, 2 (1979), 321–348.
  23. Tuomas Sandholm and Anton Likhodedov. 2015. Automated design of revenue-maximizing combinatorial auctions. Operations Research 63, 5 (2015), 1000–1025.
  24. MDDL: A Framework for Reinforcement Learning-based Position Allocation in Multi-Channel Feed. arXiv preprint arXiv:2304.09087 (2023).
  25. Hal R Varian. 2007. Position auctions. international Journal of industrial Organization 25, 6 (2007), 1163–1178.
  26. Attention is all you need. arXiv preprint arXiv:1706.03762 (2017).
  27. Learning List-wise Representation in Reinforcement Learning for Ads Allocation with Multiple Auxiliary Tasks. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 3555–3564.
  28. Hybrid Transfer in Deep Reinforcement Learning for Ads Allocation. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 4560–4564.
  29. Hierarchical Reinforcement Learning for Integrated Recommendation. In Proceedings of AAAI.
  30. Multi-channel Integrated Recommendation with Exposure Constraints. arXiv preprint arXiv:2305.12319 (2023).
  31. Ads allocation in feed via constrained optimization. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 3386–3394.
  32. The whole-page optimization via dynamic ad allocation. In Companion Proceedings of the The Web Conference. 1407–1411.
  33. DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 750–758.
  34. Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1059–1068.
Citations (2)

Summary

We haven't generated a summary for this paper yet.

X Twitter Logo Streamline Icon: https://streamlinehq.com