TY - GEN
T1 - Revenue and User Traffic Maximization in Mobile Short-Video Advertising
AU - Ran, Dezhi
AU - Zheng, Weiqiang
AU - Li, Yunqi
AU - Bian, Kaigui
AU - Zhang, Jie
AU - Deng, Xiaotie
N1 - Funding Information: This work is partially supported by National Key R&D Program of China (2020AAA0105200), Beijing Academy of Artificial Intelligence (BAAI), NSFC 62032003 and 61632017, BJNSF L192004, and a Leverhulme Trust Research Project Grant (2021-2024). Publisher Copyright: © 2022 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved; 21st International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022 ; Conference date: 09-05-2022 Through 13-05-2022
PY - 2022/5/31
Y1 - 2022/5/31
N2 - A new mobile attention economy has emerged with the explosive growth of short-video apps such as TikTok. In this internet market, three types of agents interact with each other: the platform, influencers, and advertisers. A short-video platform encourages its influencers to attract users by creating appealing content through short-form videos and allows advertisers to display their ads in short-form videos. There are two options for the advertisers: one is to bid for platform advert slots in a similar way to search engine auctions; the other is to pay an influencer to make engaging short videos and promote them through the influencer's channel. The second option will generate a higher conversion ratio if advertisers choose the right influencers whose followers match their target market. Although displaying influencer ads will generate less revenue, it is more engaging than platform ads, which is better for maintaining user traffic. Therefore, it is crucial for a platform to balance these factors by establishing a sustainable business agreement with its influencers and advertisers. In this paper, we develop a two-stage solution for a platform to maximize short-term revenue and long-term user traffic maintenance. In the first stage, we estimate the impact of user traffic generated by displaying influencer ads and characterize the user traffic the platform should allocate to influencers for overall revenue maximization. In the second stage, we devise an optimal (1 - 1/e)-competitive algorithm for ad slot allocation. To complement this analysis, we examine the ratio of the revenue generated by our online algorithm to the optimal offline revenue. Our simulation results show that this ratio is 0.94 on average, which is much higher than (1 - 1/e) and outperforms four baseline algorithms.
AB - A new mobile attention economy has emerged with the explosive growth of short-video apps such as TikTok. In this internet market, three types of agents interact with each other: the platform, influencers, and advertisers. A short-video platform encourages its influencers to attract users by creating appealing content through short-form videos and allows advertisers to display their ads in short-form videos. There are two options for the advertisers: one is to bid for platform advert slots in a similar way to search engine auctions; the other is to pay an influencer to make engaging short videos and promote them through the influencer's channel. The second option will generate a higher conversion ratio if advertisers choose the right influencers whose followers match their target market. Although displaying influencer ads will generate less revenue, it is more engaging than platform ads, which is better for maintaining user traffic. Therefore, it is crucial for a platform to balance these factors by establishing a sustainable business agreement with its influencers and advertisers. In this paper, we develop a two-stage solution for a platform to maximize short-term revenue and long-term user traffic maintenance. In the first stage, we estimate the impact of user traffic generated by displaying influencer ads and characterize the user traffic the platform should allocate to influencers for overall revenue maximization. In the second stage, we devise an optimal (1 - 1/e)-competitive algorithm for ad slot allocation. To complement this analysis, we examine the ratio of the revenue generated by our online algorithm to the optimal offline revenue. Our simulation results show that this ratio is 0.94 on average, which is much higher than (1 - 1/e) and outperforms four baseline algorithms.
KW - competitive ratio
KW - revenue maximization
KW - Short-video advertising
UR - http://www.scopus.com/inward/record.url?scp=85134303281&partnerID=8YFLogxK
U2 - 10.5555/3535850.3535972
DO - 10.5555/3535850.3535972
M3 - Chapter in a published conference proceeding
T3 - Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
SP - 1092
EP - 1100
BT - AAMAS '22: Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems
PB - Association for Computing Machinery
ER -