Deeply supervised model for click-through rate prediction in sponsored search

Gligorijević, Jelena; Gligorijevic, Djordje; Stojković, Ivan; Bai, Xiao; Goyal, Amit; Obradović, Zoran

doi:10.1007/s10618-019-00625-3

Cited by 21 publications

(17 citation statements)

References 35 publications

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“…The underlying driving technology for online advertising is Click-Through Rates (CTR) estimation, in which the task is to predict the click probability of the browsers for some commodities in certain scenarios [2]. Accurate prediction of CTR will not only benefit advertisers' promotion of products but also ensure users' good experiences and interests [3].…”

Section: Introductionmentioning

confidence: 99%

A New Click-Through Rates Prediction Model Based on Deep&Cross Network

2020

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With the development of E-commerce, online advertising began to thrive and has gradually developed into a new mode of business, of which Click-Through Rates (CTR) prediction is the essential driving technology. Given a user, commodities and scenarios, the CTR model can predict the user’s click probability of an online advertisement. Recently, great progress has been made with the introduction of Deep Neural Networks (DNN) into CTR. In order to further advance the DNN-based CTR prediction models, this paper introduces a new model of FO-FTRL-DCN, based on the prestigious model of Deep&Cross Network (DCN) augmented with the latest optimization technique of Follow The Regularized Leader (FTRL) for DNN. The extensive comparative experiments on the iPinYou datasets show that the proposed model has outperformed other state-of-the-art baselines, with better generalization across different datasets in the benchmark.

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Section: Introductionmentioning

confidence: 99%

A New Click-Through Rates Prediction Model Based on Deep&Cross Network

2020

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“…Recently, models with representation learning capabilities have also been proposed for CTR and CVR prediction tasks, e.g., factorization machines [15] for CVR or deep residual networks [18] and Siamese networks [8] for CTR that tackle problems of learning nonlinear interactions of features. Also, more prominently, models that capture information from the sequence such are RNNs have been proposed recently [1,5,6,21] and they reportedly perform significantly better than their non-sequential counterparts.…”

Section: Modeling Users' Conversion Predictionmentioning

confidence: 99%

“…Bi-directional RNN ensures that the model learns complex relations between events, which is particularly important for user trails where events may be grouped by sessions which carry higher order information than the events themselves [8]. The resulting embeddings д e i are in…”

Section: Temporal Attention Learningmentioning

confidence: 99%

“…In order to learn rich representations of user's trail, it is imperative to focus on events that carry the most information. To learn representations that focus on important parts of the user trail we employ a dedicated attention mechanism on top of sequence modeling features [8]. Employed attention block yields event scores, that highlight events of greater importance for the task at hand.…”

Section: Temporal Attention Learningmentioning

confidence: 99%

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Prospective Modeling of Users for Online Display Advertising via Deep Time-Aware Model

Gligorijevic

Gligorijević

Flores

2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Prospective display advertising poses a particular challenge for large advertising platforms. The existing machine learning algorithms are easily biased towards the highly predictable retargeting events that are often non-eligible for the prospective campaigns, thus exhibiting a decline in advertising performance. To that end, efforts are made to design powerful models that can learn from signals of various strength and temporal impact collected about each user from different data sources and provide a good quality and early estimation of users' conversion rates. In this study, we propose a novel deep time-aware approach designed to model sequences of users' activities and capture implicit temporal signals of users' conversion intents. On several real-world datasets, we show that the proposed approach consistently outperforms other, previously proposed approaches by a significant margin while providing interpretability of signal impact to conversion probability. CCS CONCEPTS • Information systems → Display advertising; Web mining; • Computing methodologies → Machine learning.

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“…17 More recently, such architectures were further advanced. [18][19][20] Motivated by these advances, we propose DeepMatch (DM), a novel deep learning model designed specifically to rank investigators for clinical trials through pointwise regression on their estimated enrollment. DM learns deep representations of (1) investigators given their specialty indications and patients' history and (2) clinical trials given their official description, primary indication (PI) and primary therapeutic area (PTA).…”

Section: Introductionmentioning

confidence: 99%

Optimizing clinical trials recruitment via deep learning

Gligorijević

Gligorijevic

Pavlovski

et al. 2019

Journal of the American Medical Informatics Association

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Objective Clinical trials, prospective research studies on human participants carried out by a distributed team of clinical investigators, play a crucial role in the development of new treatments in health care. This is a complex and expensive process where investigators aim to enroll volunteers with predetermined characteristics, administer treatment(s), and collect safety and efficacy data. Therefore, choosing top-enrolling investigators is essential for efficient clinical trial execution and is 1 of the primary drivers of drug development cost. Materials and Methods To facilitate clinical trials optimization, we propose DeepMatch (DM), a novel approach that builds on top of advances in deep learning. DM is designed to learn from both investigator and trial-related heterogeneous data sources and rank investigators based on their expected enrollment performance on new clinical trials. Results Large-scale evaluation conducted on 2618 studies provides evidence that the proposed ranking-based framework improves the current state-of-the-art by up to 19% on ranking investigators and up to 10% on detecting top/bottom performers when recruiting investigators for new clinical trials. Discussion The extensive experimental section suggests that DM can provide substantial improvement over current industry standards in several regards: (1) the enrollment potential of the investigator list, (2) the time it takes to generate the list, and (3) data-informed decisions about new investigators. Conclusion Due to the great significance of the problem at hand, related research efforts are set to shift the paradigm of how investigators are chosen for clinical trials, thereby optimizing and automating them and reducing the cost of new therapies.

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Deeply supervised model for click-through rate prediction in sponsored search

Cited by 21 publications

References 35 publications

A New Click-Through Rates Prediction Model Based on Deep&Cross Network

A New Click-Through Rates Prediction Model Based on Deep&Cross Network

Prospective Modeling of Users for Online Display Advertising via Deep Time-Aware Model

Optimizing clinical trials recruitment via deep learning

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