Tootfinder

Multi-task Offline Reinforcement Learning for Online Advertising in Recommender Systems
Langming Liu, Wanyu Wang, Chi Zhang, Bo Li, Hongzhi Yin, Xuetao Wei, Wenbo Su, Bo Zheng, Xiangyu Zhao
https://arxiv.org/abs/2506.23090

Multi-task Offline Reinforcement Learning for Online Advertising in Recommender Systems
Online advertising in recommendation platforms has gained significant attention, with a predominant focus on channel recommendation and budget allocation strategies. However, current offline reinforcement learning (RL) methods face substantial challenges when applied to sparse advertising scenarios, primarily due to severe overestimation, distributional shifts, and overlooking budget constraints. To address these issues, we propose MTORL, a novel multi-task offline RL model that targets two key…

Meta sues Joy Timeline, which makes nudify app CrushAI, in Hong Kong to "prevent them from advertising CrushAI apps on Meta platforms"; CrushAI had run 5K ads (Emanuel Maiberg/404 Media)
https://www.404media.co/meta-sues-nudify-app-that…

Meta Sues Nudify App That Keeps Advertising on Instagram
As part of what it claims is a new crackdown, Meta is suing a nudify app and "strengthening" its enforcement.

IAB survey: 86% of digital ad buyers use or plan to use generative AI for video ad creation, and expect it will account for 40% of all video ads by 2026 (Matthew Keys/TheDesk.net)
https://thedesk.net/2025/07/iab-generative-ai-ctv-advertising-report/

IAB: Generative AI, connected TV platforms influencing ad production
More than half of advertisers are using generative AI to create spots, the IAB concludes in its latest report.

This https://arxiv.org/abs/2503.09492 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Learning Cascade Ranking as One Network
Cascade Ranking is a prevalent architecture in large-scale top-k selection systems like recommendation and advertising platforms. Traditional training methods focus on single-stage optimization, neglecting interactions between stages. Recent advances have introduced interaction-aware training paradigms, but still struggle to 1) align training objectives with the goal of the entire cascade ranking (i.e., end-to-end recall of ground-truth items) and 2) learn effective collaboration patterns for d…

Hybrid Advertising in the Sponsored Search
Zhen Zhang, Weian Li, Yuhan Wang, Qi Qi, Kun Huang
https://arxiv.org/abs/2507.07711 https://

Hybrid Advertising in the Sponsored Search
Online advertisements are a primary revenue source for e-commerce platforms. Traditional advertising models are store-centric, selecting winning stores through auction mechanisms. Recently, a new approach known as joint advertising has emerged, which presents sponsored bundles combining one store and one brand in ad slots. Unlike traditional models, joint advertising allows platforms to collect payments from both brands and stores. However, each of these two advertising models appeals to distin…

Optimal Calibrated Signaling in Digital Auctions
Zhicheng Du, Wei Tang, Zihe Wang, Shuo Zhang
https://arxiv.org/abs/2507.17187 https://arxiv.org/pdf/2507.1…

Optimal Calibrated Signaling in Digital Auctions
In digital advertising, online platforms allocate ad impressions through real-time auctions, where advertisers typically rely on autobidding agents to optimize bids on their behalf. Unlike traditional auctions for physical goods, the value of an ad impression is uncertain and depends on the unknown click-through rate (CTR). While platforms can estimate CTRs more accurately using proprietary machine learning algorithms, these estimates/algorithms remain opaque to advertisers. This information as…

This https://arxiv.org/abs/2406.02463 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Click Without Compromise: Online Advertising Measurement via Per User Differential Privacy
Online advertising is a cornerstone of the Internet ecosystem, with advertising measurement playing a crucial role in optimizing efficiency. Ad measurement entails attributing desired behaviors, such as purchases, to ad exposures across various platforms, necessitating the collection of user activities across these platforms. As this practice faces increasing restrictions due to rising privacy concerns, safeguarding user privacy in this context is imperative. Our work is the first to formulate …

Optimal Auction Design in the Joint Advertising
Yang Li, Yuchao Ma, Qi Qi
https://arxiv.org/abs/2507.07418 https://arxiv.org/pdf/2507…

Optimal Auction Design in the Joint Advertising
Online advertising is a vital revenue source for major internet platforms. Recently, joint advertising, which assigns a bundle of two advertisers in an ad slot instead of allocating a single advertiser, has emerged as an effective method for enhancing allocation efficiency and revenue. However, existing mechanisms for joint advertising fail to realize the optimality, as they tend to focus on individual advertisers and overlook bundle structures. This paper identifies an optimal mechanism for jo…

WPP Media expects ad revenue from user-generated content and platforms like YouTube and TikTok to surpass that from traditional media for the first time in 2025 (Michael Savage/The Guardian)
https://www.theguardian.com/media/2025/jun

Blind Targeting: Personalization under Third-Party Privacy Constraints
Anya Shchetkina
https://arxiv.org/abs/2507.05175 https://arxiv…

Blind Targeting: Personalization under Third-Party Privacy Constraints
Major advertising platforms recently increased privacy protections by limiting advertisers' access to individual-level data. Instead of providing access to granular raw data, the platforms only allow a limited number of aggregate queries to a dataset, which is further protected by adding differentially private noise. This paper studies whether and how advertisers can design effective targeting policies within these restrictive privacy preserving data environments. To achieve this, I develop a p…

WPP Media expects ad revenue from user-generated content and platforms like YouTube and TikTok to surpass that from traditional media for the first time in 2025 (Michael Savage/The Guardian)
https://www.theguardian.com/media/2025/jun

Beyond Advertising: Mechanism Design for Platform-Wide Marketing Service "QuanZhanTui"
Ningyuan Li, Zhilin Zhang, Tianyan Long, Yuyao Liu, Rongquan Bai, Yurong Chen, Xiaotie Deng, Pengjie Wang, Chuan Yu, Jian Xu, Bo Zheng
https://arxiv.org/abs/2507.02931

Beyond Advertising: Mechanism Design for Platform-Wide Marketing Service "QuanZhanTui"
On e-commerce platforms, sellers typically bid for impressions from ad traffic to promote their products. However, for most sellers, the majority of their sales come from organic traffic. Consequently, the relationship between their ad spending and total sales remains uncertain, resulting in operational inefficiency. To address this issue, e-commerce platforms have recently introduced a novel platform-wide marketing service known as QuanZhanTui, which has reportedly enhanced marketing efficienc…

This https://arxiv.org/abs/2503.09492 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Learning Cascade Ranking as One Network
Cascade Ranking is a prevalent architecture in large-scale top-k selection systems like recommendation and advertising platforms. Traditional training methods focus on single-stage optimization, neglecting interactions between stages. Recent advances have introduced interaction-aware training paradigms, but still struggle to 1) align training objectives with the goal of the entire cascade ranking (i.e., end-to-end recall of ground-truth items) and 2) learn effective collaboration patterns for d…