NMA: Neural Multi-slot Auctions with Externalities for Online Advertising

• URL: http://arxiv.org/abs/2205.10018v3
• Date: Fri, 8 Sep 2023 08:21:07 GMT
• Title: NMA: Neural Multi-slot Auctions with Externalities for Online Advertising
• Authors: Guogang Liao, Xuejian Li, Ze Wang, Fan Yang, Muzhi Guan, Bingqi Zhu, Yongkang Wang, Xingxing Wang, Dong Wang
• Abstract summary: We propose novel auction mechanisms named Neural Multi-slot Auctions (NMA) to tackle the challenges. NMA obtains higher revenue with balanced social welfare than other existing auction mechanisms. We have successfully deployed NMA on Meituan food delivery platform.
• Score: 19.613777564235555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Online advertising driven by auctions brings billions of dollars in revenue for social networking services and e-commerce platforms. GSP auctions, which are simple and easy to understand for advertisers, have almost become the benchmark for ad auction mechanisms in the industry. However, most GSP-based industrial practices assume that the user click only relies on the ad itself, which overlook the effect of external items, referred to as externalities. Recently, DNA has attempted to upgrade GSP with deep neural networks and models local externalities to some extent. However, it only considers set-level contexts from auctions and ignores the order and displayed position of ads, which is still suboptimal. Although VCG-based multi-slot auctions (e.g., VCG, WVCG) make it theoretically possible to model global externalities (e.g., the order and positions of ads and so on), they lack an efficient balance of both revenue and social welfare. In this paper, we propose novel auction mechanisms named Neural Multi-slot Auctions (NMA) to tackle the above-mentioned challenges. Specifically, we model the global externalities effectively with a context-aware list-wise prediction module to achieve better performance. We design a list-wise deep rank module to guarantee incentive compatibility in end-to-end learning. Furthermore, we propose an auxiliary loss for social welfare to effectively reduce the decline of social welfare while maximizing revenue. Experiment results on both offline large-scale datasets and online A/B tests demonstrate that NMA obtains higher revenue with balanced social welfare than other existing auction mechanisms (i.e., GSP, DNA, WVCG) in industrial practice, and we have successfully deployed NMA on Meituan food delivery platform.

Related papers

• Ad Auctions for LLMs via Retrieval Augmented Generation [12.9128551468564]
  This paper introduces novel auction mechanisms for ad allocation and pricing within the textual outputs of large language models (LLMs) We propose a segment auction where an ad is probabilistically retrieved for each discourse segment according to its bid and relevance, following the RAG framework. We show that our auction maximizes logarithmic social welfare, a new notion of welfare that balances allocation efficiency and fairness, and we characterize the associated incentive-compatible pricing rule.
  arXiv  Detail & Related papers  (2024-06-12T22:05:51Z)
• Multi-attribute Auction-based Resource Allocation for Twins Migration in Vehicular Metaverses: A GPT-based DRL Approach [85.65587846913793]
  Vehicular Metaverses are developed to enhance the modern automotive industry with an immersive and safe experience among connected vehicles. We propose an attribute-aware auction-based mechanism to optimize resource allocation during VTs migration. We train a DDA auctioneer using a generative pre-trained transformer (GPT)-based deep reinforcement learning (DRL) algorithm to adjust the auction clocks efficiently.
  arXiv  Detail & Related papers  (2024-06-08T09:41:38Z)
• Neural Auctions Compromise Bidder Information [43.82512707595423]
  Single-shot auctions are commonly used as a means to sell goods, for example when selling ad space or allocating radio frequencies. It has been shown that neural networks can be used to approximate optimal mechanisms while satisfying the constraints that an auction be strategyproof and individually rational. We show that despite such auctions maximizing revenue, they do so at the cost of revealing private bidder information.
  arXiv  Detail & Related papers  (2023-02-28T22:36:00Z)
• VFed-SSD: Towards Practical Vertical Federated Advertising [53.08038962443853]
  We propose a semi-supervised split distillation framework VFed-SSD to alleviate the two limitations. Specifically, we develop a self-supervised task MatchedPair Detection (MPD) to exploit the vertically partitioned unlabeled data. Our framework provides an efficient federation-enhanced solution for real-time display advertising with minimal deploying cost and significant performance lift.
  arXiv  Detail & Related papers  (2022-05-31T17:45:30Z)
• Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising [42.7415188090209]
  We develop deep models to efficiently extract contexts from auctions, providing rich features for auction design. DNAs have been successfully deployed in the e-commerce advertising system at Taobao.
  arXiv  Detail & Related papers  (2021-06-07T13:20:40Z)
• Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising [28.343122250701498]
  In e-commerce advertising, the ad platform usually relies on auction mechanisms to optimize different performance metrics, such as user experience, advertiser utility, and platform revenue. We propose a new mechanism called Deep GSP auction, which leverages deep learning to design new rank score functions within the celebrated GSP auction framework.
  arXiv  Detail & Related papers  (2020-12-05T02:51:11Z)
• A novel auction system for selecting advertisements in Real-Time bidding [68.8204255655161]
  Real-Time Bidding is a new Internet advertising system that has become very popular in recent years. We propose an alternative betting system with a new approach that not only considers the economic aspect but also other relevant factors for the functioning of the advertising system.
  arXiv  Detail & Related papers  (2020-10-22T18:36:41Z)
• ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning [55.76903822619047]
  We study the design of revenue-maximizing auctions with strong incentive guarantees. We extend techniques for approximating auctions using deep learning to address concerns of fairness while maintaining high revenue and strong incentive guarantees.
  arXiv  Detail & Related papers  (2020-10-13T13:54:21Z)
• Real-Time Optimization Of Web Publisher RTB Revenues [10.908037452134302]
  This paper describes an engine to optimize web publisher revenues from second-price auctions. The engine is able to predict, for each auction, an optimal reserve price in approximately one millisecond.
  arXiv  Detail & Related papers  (2020-06-12T11:14:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.