Ad Click Prediction in Sequence with Long Short-Term Memory Networks

Deng, Weiwei; Ling, Xiaoliang; Yang, Qi; Tan, Tunzi; Manavoglu, Eren; Zhang, Qi

doi:10.1145/3209978.3210071

Cited by 11 publications

(8 citation statements)

References 17 publications

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“…Because recurrent neural networks are able to retain memory between samples and capture relations between instances for long time steps in input data, RNN-based methods have been leveraged to model sequential dependency on click data. The authors in [14] used these networks to consider user browsing behavior for click-through prediction to deal with externalities. In this case, click on an Ad might be affected by the quality of Ads shown in the long sequence of Ads.…”

Section: Click-through Rate (Ctr) Predictionmentioning

confidence: 99%

Deep Learning for User Interest and Response Prediction in Online Display Advertising

Gharibshah

Zhu

Hainline³

et al. 2020

Data Sci. Eng.

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User interest and behavior modeling is a critical step in online digital advertising. On the one hand, user interests directly impact their response and actions to the displayed advertisement (Ad). On the other hand, user interests can further help determine the probability of an Ad viewer becoming a buying customer. To date, existing methods for Ad click prediction, or click-through rate prediction, mainly consider representing users as a static feature set and train machine learning classifiers to predict clicks. Such approaches do not consider temporal variance and changes in user behaviors, and solely rely on given features for learning. In this paper, we propose two deep learning-based frameworks, LSTM cp and LSTM ip , for user click prediction and user interest modeling. Our goal is to accurately predict (1) the probability of a user clicking on an Ad and (2) the probability of a user clicking a specific type of Ad campaign. To achieve the goal, we collect page information displayed to the users as a temporal sequence and use long short-term memory (LSTM) network to learn features that represents user interests as latent features. Experiments and comparisons on real-world data show that, compared to existing static set-based approaches, considering sequences and temporal variance of user requests results in improvements in user Ad response prediction and campaign specific user Ad click prediction.

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Section: Click-through Rate (Ctr) Predictionmentioning

confidence: 99%

Deep Learning for User Interest and Response Prediction in Online Display Advertising

Gharibshah

Zhu

Hainline³

et al. 2020

Data Sci. Eng.

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show abstract

“…In the literature on advertising CTR prediction (e.g., Shan et al, 2016;Deng et al, 2018;Ling et al, 2017;Liao et al, 2014;Li et al, 2015;Zhang et al, 2017), features can be categorized into five classes, as summarized in Table 2: (1) advertising features; (2) user features; (3) context features; (4) query features; and (5) publisher features.…”

Section: Features For Ctr Predictionmentioning

confidence: 99%

Click-through rate prediction in online advertising: A literature review

Yang

Zhai

2022

Information Processing & Management

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“…Hochreiter et al [ 19 ] proposed that LSTM is used to solve the problem that ordinary RNN cannot solve long-term dependence and may bring gradient disappearance or gradient explosion. It has been proved that LSTM can effectively solve the problem of long sequence dependency [ 20 – 22 ].…”

Section: Related Workmentioning

confidence: 99%

Multiscale U-Net with Spatial Positional Attention for Retinal Vessel Segmentation

Liu

Xiao

2022

Journal of Healthcare Engineering

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Retinal vessel segmentation is essential for the detection and diagnosis of eye diseases. However, it is difficult to accurately identify the vessel boundary due to the large variations of scale in the retinal vessels and the low contrast between the vessel and the background. Deep learning has a good effect on retinal vessel segmentation since it can capture representative and distinguishing features for retinal vessels. An improved U-Net algorithm for retinal vessel segmentation is proposed in this paper. To better identify vessel boundaries, the traditional convolutional operation CNN is replaced by a global convolutional network and boundary refinement in the coding part. To better divide the blood vessel and background, the improved position attention module and channel attention module are introduced in the jumping connection part. Multiscale input and multiscale dense feature pyramid cascade modules are used to better obtain feature information. In the decoding part, convolutional long and short memory networks and deep dilated convolution are used to extract features. In public datasets, DRIVE and CHASE_DB1, the accuracy reached 96.99% and 97.51%. The average performance of the proposed algorithm is better than that of existing algorithms.

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Ad Click Prediction in Sequence with Long Short-Term Memory Networks

Cited by 11 publications

References 17 publications

Deep Learning for User Interest and Response Prediction in Online Display Advertising

Deep Learning for User Interest and Response Prediction in Online Display Advertising

Click-through rate prediction in online advertising: A literature review

Multiscale U-Net with Spatial Positional Attention for Retinal Vessel Segmentation

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