research-article

Multimedia features for click prediction of new ads in display advertising

Authors:

Roelof van Zwol,

Eren Manavoglu,

Vidhya NavalpakkamAuthors Info & Claims

KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 777 - 785

https://doi.org/10.1145/2339530.2339652

Published: 12 August 2012 Publication History

Abstract

Non-guaranteed display advertising (NGD) is a multi-billion dollar business that has been growing rapidly in recent years. Advertisers in NGD sell a large portion of their ad campaigns using performance dependent pricing models such as cost-per-click (CPC) and cost-per-action (CPA). An accurate prediction of the probability that users click on ads is a crucial task in NGD advertising because this value is required to compute the expected revenue. State-of-the-art prediction algorithms rely heavily on historical information collected for advertisers, users and publishers. Click prediction of new ads in the system is a challenging task due to the lack of such historical data. The objective of this paper is to mitigate this problem by integrating multimedia features extracted from display ads into the click prediction models. Multimedia features can help us capture the attractiveness of the ads with similar contents or aesthetics. In this paper we evaluate the use of numerous multimedia features (in addition to commonly used user, advertiser and publisher features) for the purposes of improving click prediction in ads with no history. We provide analytical results generated over billions of samples and demonstrate that adding multimedia features can significantly improve the accuracy of click prediction for new ads, compared to a state-of-the-art baseline model.

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https://doi.org/10.1541/ieejeiss.144.942
Sheng XYang FGong LWang BChan ZZhang YCheng YZhu YGe TZhu HJiang YXu JZheng BSerra ESpezzano F(2024)Enhancing Taobao Display Advertising with Multimodal Representations: Challenges, Approaches and InsightsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680068(4858-4865)Online publication date: 21-Oct-2024
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Multimedia features for click prediction of new ads in display advertising

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cover image ACM Conferences

KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2012

1616 pages

ISBN:9781450314626

DOI:10.1145/2339530

General Chair:
Qiang Yang
Hong Kong University of Science and Technology
,
Program Chairs:
Deepak Agarwal
LinkedIn
,
Jian Pei
Simon Fraser University

Copyright © 2012 ACM.

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 ACM 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: 12 August 2012

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KDD '12

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KDD '12: The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 12 - 16, 2012

Beijing, China

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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https://doi.org/10.1541/ieejeiss.144.942
Sheng XYang FGong LWang BChan ZZhang YCheng YZhu YGe TZhu HJiang YXu JZheng BSerra ESpezzano F(2024)Enhancing Taobao Display Advertising with Multimodal Representations: Challenges, Approaches and InsightsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680068(4858-4865)Online publication date: 21-Oct-2024
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Igret IMachidon AMachidon O(2024)Context-Based Adaptation of Neural Network Compression for Unmanned Aerial Vehicle (UAV) Weed Detection2024 IEEE 20th International Conference on Intelligent Computer Communication and Processing (ICCP)10.1109/ICCP63557.2024.10792992(1-7)Online publication date: 17-Oct-2024
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