A Dynamic Neural Network Model for Click-Through Rate Prediction in Real-Time Bidding

Qu, Xianshan; Li, Li; Liu, Xi; Chen, Rui; Ge, Yong; Choi, Soo-Hyun

doi:10.1109/bigdata47090.2019.9005598

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…In this work, we evaluate review helpfulness from the perspective of review quality. For future work, we may rank the helpfulness of reviews by incorporating a user's own preferences [22] in order to make personalized recommendations.…”

Section: Resultsmentioning

confidence: 99%

An Attention Model of Customer Expectation to Improve Review Helpfulness Prediction

Farkas

et al. 2020

Lecture Notes in Computer Science

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Many people browse reviews online before making purchasing decisions. It is essential to identify the subset of helpful reviews from the large number of reviews of varying quality. This paper aims to build a model to predict review helpfulness automatically. Our work is inspired by the observation that a customer's expectation of a review can be greatly affected by review sentiment and the degree to which the customer is aware of pertinent product information. Consequently, a customer may pay more attention to that specific content of a review which contributes more to its helpfulness from their perspective. To model such customer expectations and capture important information from a review text, we propose a novel neural network which leverages review sentiment and product information. Specifically, we encode the sentiment of a review through an attention module, to get sentiment-driven information from review text. We also introduce a product attention layer that fuses information from both the target product and related products, in order to capture the product related information from review text. Our experimental results show an AUC improvement of 5.4% and 1.5% over the previous state of the art model on Amazon and Yelp data sets, respectively.

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Section: Resultsmentioning

confidence: 99%

An Attention Model of Customer Expectation to Improve Review Helpfulness Prediction

Farkas

et al. 2020

Lecture Notes in Computer Science

Self Cite

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show abstract

“…The first step is to pre-process the raw dataset, e.g., cleaning abnormal data, filling missing data, and removing duplicate samples, etc. The second step is to conduct feature engineering based on attributes of the raw data, in order to select and extract useful features for CTR prediction (Qu et al, 2019;Moneera et al, 2021). The third step is to build the CTR prediction model by using statistical or machine learning techniques, which will be elaborated in more detail in Section 4.…”

Section: 𝑜 𝑡mentioning

confidence: 99%

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

Yang

Zhai

2022

Information Processing & Management

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Predictive Programmatic Classification Model to Improve Ad-Campaign Click Through Rate

Saseendran¹,

Sneha²

2021

Communications in Computer and Information Science

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A Dynamic Neural Network Model for Click-Through Rate Prediction in Real-Time Bidding

Cited by 5 publications

References 23 publications

An Attention Model of Customer Expectation to Improve Review Helpfulness Prediction

An Attention Model of Customer Expectation to Improve Review Helpfulness Prediction

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

Predictive Programmatic Classification Model to Improve Ad-Campaign Click Through Rate

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