What You Look Matters?

Zhao, Zhichen; Li, Lei; Zhang, Bowen; Wang, Meng; Jiang, Yuning; Xu, Li; Wang, Fengkun; Ma, Wei‐Ying

doi:10.1145/3357384.3357813

Cited by 14 publications

(4 citation statements)

References 13 publications

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“…This work has implications for the problem of "cold start" (Perlich et al, 2014;Mo et al, 2015;Zhao et al, 2019), where the machine learning-based BT requires relatively large training data to achieve desirable performance. Before an advertising campaign is started, there are no data at all on whether or not users would convert after having been shown the advertisement.…”

Section: Research Implicationsmentioning

confidence: 98%

Who to show the ad to? Behavioral targeting in Internet advertising

Xiong

Tian

2022

JIDE

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PurposeThe performance of behavioral targeting (BT) mainly relies on the effectiveness of user classification since advertisers always want to target their advertisements to the most relevant users. In this paper, the authors frame the BT as a user classification problem and describe a machine learning–based approach for solving it.Design/methodology/approachTo perform such a study, two major research questions are investigated: the first question is how to represent a user’s online behavior. A good representation strategy should be able to effectively classify users based on their online activities. The second question is how different representation strategies affect the targeting performance. The authors propose three user behavior representation methods and compare them empirically using the area under the receiver operating characteristic curve (AUC) as a performance measure.FindingsThe experimental results indicate that ad campaign effectiveness can be significantly improved by combining user search queries, clicked URLs and clicked ads as a user profile. In addition, the authors also explore the temporal aspect of user behavior history by investigating the effect of history length on targeting performance. The authors note that an improvement of approximately 6.5% in AUC is achieved when user history is extended from 1 day to 14 days, which is substantial in targeting performance.Originality/valueThis paper confirms the effectiveness of BT on user classification and provides a validation of BT for Internet advertising.

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Section: Research Implicationsmentioning

confidence: 98%

Who to show the ad to? Behavioral targeting in Internet advertising

Xiong

Tian

2022

JIDE

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“…Thus, creative ranking research has focused more on offline assessments and designing high-efficiency online algorithms. Previous studies like NIMA (Talebi and Milanfar 2018) and PEAC (Zhao et al 2019) focused on offline creative quality evaluation based on image and text content. On the other hand, PEAC demonstrates the importance of online user feedback for creative ranking.…”

Section: Related Workmentioning

confidence: 99%

Parallel Ranking of Ads and Creatives in Real-Time Advertising Systems

Yang,

Sang,

Wang

et al. 2024

AAAI

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Creativity is the heart and soul of advertising services. Effective creatives can create a win-win scenario: advertisers each target users and achieve marketing objectives more effectively, users more quickly find products of interest, and platforms generate more advertising revenue. With the advent of AI-Generated Content, advertisers now can produce vast amounts of creative content at a minimal cost. The current challenge lies in how advertising systems can select the most pertinent creative in real-time for each user personally. Existing methods typically perform serial ranking of ads or creatives, limiting the creative module in terms of both effectiveness and efficiency. In this paper, we propose for the first time a novel architecture for online parallel estimation of ads and creatives ranking, as well as the corresponding offline joint optimization model. The online architecture enables sophisticated personalized creative modeling while reducing overall latency. The offline joint model for CTR estimation allows mutual awareness and collaborative optimization between ads and creatives. Additionally, we optimize the offline evaluation metrics for the implicit feedback sorting task involved in ad creative ranking. We conduct extensive experiments to compare ours with two state-of-the-art approaches. The results demonstrate the effectiveness of our approach in both offline evaluations and real-world advertising platforms online in terms of response time, CTR, and CPM.

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“…A CTR predictor aims to predict the probability that a user clicks an ad given certain contexts which play an important role in improving user experience for many online services, e.g., e-commerce sites, social media platforms, and search engines. Recent studies extract visual features from the cover image of ad for better CTR predicting [7,40,41,63,66]. Chen et al [7] apply deep neural network (DNN) on ad image for CTR prediction.…”

Section: Related Workmentioning

confidence: 99%

AdSEE: Investigating the Impact of Image Style Editing on Advertisement Attractiveness

Jiang

Chen

et al. 2023

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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Online advertisements are important elements in e-commerce sites, social media platforms, and search engines. With the increasing popularity of mobile browsing, many online ads are displayed with visual information in the form of a cover image in addition to text descriptions to grab the attention of users. Various recent studies have focused on predicting the click rates of online advertisements aware of visual features or composing optimal advertisement elements to enhance visibility. In this paper, we propose Advertisement Style Editing and Attractiveness Enhancement (AdSEE), which explores whether semantic editing to ads images can affect or alter the popularity of online advertisements. We introduce StyleGANbased facial semantic editing and inversion to ads images and train a click rate predictor attributing GAN-based face latent representations in addition to traditional visual and textual features to click rates. Through a large collected dataset named QQ-AD, containing 20,527 online ads, we perform extensive offline tests to study how different semantic directions and their edit coefficients may impact click rates. We further design a Genetic Advertisement Editor to efficiently search for the optimal edit directions and intensity given an input ad cover image to enhance its projected click rates. Online A/B tests performed over a period of 5 days have verified the increased click-through rates of AdSEE-edited samples as compared to a control group of original ads, verifying the relation between image styles and ad popularity. We open source the code for AdSEE research at https://github.com/LiyaoJiang1998/adsee.

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What You Look Matters?

Cited by 14 publications

References 13 publications

Who to show the ad to? Behavioral targeting in Internet advertising

Who to show the ad to? Behavioral targeting in Internet advertising

Parallel Ranking of Ads and Creatives in Real-Time Advertising Systems

AdSEE: Investigating the Impact of Image Style Editing on Advertisement Attractiveness

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