• Corpus ID: 197640799

Time-Aware Prospective Modeling of Users for Online Display Advertising

@article{Gligorijevic2019TimeAwarePM,
  title={Time-Aware Prospective Modeling of Users for Online Display Advertising},
  author={Djordje Gligorijevic and Jelena Gligorijevic and Aaron Flores},
  journal={ArXiv},
  year={2019},
  volume={abs/1911.05100}
}
Prospective display advertising poses a great challenge for large advertising platforms as the strongest predictive signals of users are not eligible to be used in the conversion prediction systems. To that end efforts are made to collect as much information as possible about each user from various data sources and to design powerful models that can capture weaker signals ultimately obtaining good quality of conversion prediction probability estimates. In this study we propose a novel time… 

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References

SHOWING 1-10 OF 21 REFERENCES
Ad click prediction: a view from the trenches
TLDR
The goal of this paper is to highlight the close relationship between theoretical advances and practical engineering in this industrial setting, and to show the depth of challenges that appear when applying traditional machine learning methods in a complex dynamic system.
Modeling Mobile User Actions for Purchase Recommendation using Deep Memory Networks
TLDR
This study proposes a novel approach to learn representations of mobile user actions using Deep Memory Networks and validate the proposed approach on millions of app usage sessions built from large scale feeds of mobile app events and mobile purchase receipts.
Deep Neural Net with Attention for Multi-channel Multi-touch Attribution
TLDR
This paper proposes a novel attribution algorithm based on deep learning to assess the impact of each advertising channel and presents Deep Neural Net With Attention multi-touch attribution model (DNAMTA), which incorporates user-context information, such as user demographics and behavior, as control variables to reduce the estimation biases of media effects.
Understanding Consumer Journey using Attention based Recurrent Neural Networks
TLDR
An attention based recurrent neural network (RNN) which ingests a user activity trail, and predicts the user's conversion probability along with attention weights for each activity (analogous to its position in the funnel) is proposed.
Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks
TLDR
A novel framework based on Recurrent Neural Networks (RNN) is introduced that directly models the dependency on user's sequential behaviors into the click prediction process through the recurrent structure in RNN.
Modelling customer online behaviours with neural networks: applications to conversion prediction and advertising retargeting
TLDR
The proposed model enables the use of largely available customer online behaviours data for advanced digital marketing analysis and uses Monte Carlo simulation to estimate the conversion rates of each potential customer in the future visiting.
Deeply supervised model for click-through rate prediction in sponsored search
TLDR
A deeply supervised architecture that jointly learns the semantic embeddings of a query and an ad as well as their corresponding CTR is proposed and a novel cohort negative sampling technique for learning implicit negative signals is proposed.
Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising
TLDR
This paper forms conversion prediction as a multi-task learning problem, so that the prediction models for different types of conversions can be learned together, and proposes Multi-Task Field-weighted Factorization Machine (MT-FwFM) to solve these tasks jointly.
DeepIntent: Learning Attentions for Online Advertising with Recurrent Neural Networks
TLDR
This paper proposes a novel attention network, which learns to assign attention scores to different word locations according to their intent importance, and shows that using the learned attention scores, one is able to produce sub-queries that are of better qualities than those of the state-of-the-art methods.
A Large Scale Prediction Engine for App Install Clicks and Conversions
TLDR
This paper describes how a scalable machine learning pipeline was built from scratch to predict the probability of users clicking and installing apps in response to ad impressions and dives into how sequential model training, deep learning, and transfer learning resulted in a further 7% lift in conversion rate and 11% lifts in revenue.
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