CTR-Click Through Rate
Convolutional Neural Networks based Click Through Rate Prediction with Multiple Feature Sequences.
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Abstract Convolutional Neural Network (CNN) achieved satisfying performance in click through rate (CTR) prediction in recent studies. Since features used in CTR prediction have no meaningful sequence in nature, the features can be arranged in any order. As CNN
Disguise Adversarial Networks for Click through Rate Prediction.
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We introduced an adversarial learning framework for improving CTR prediction in Ads recommendation. Our approach was motivated by observing the extremely low click through rate and imbalanced label distribution in the historical Ads impressions. We hence proposed
Comparing click logs and editorial labels for training query rewriting
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In this model, the higher the predicted score, the worse the relevance. 3. COMPARISON BETWEEN EDITORIAL LABELS AND CLICK THROUGH RATE Jones et al evaluated their query rewrite system on the basis of a four-point scale of editorial relevance judgments
Accurate Prediction of Advertisement Clicks based on Impression and Click Through Rate using Extreme Gradient Boosting.
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Online travel agencies (OTAs) aim to use digital media advertisements in the most efficient way to increase their market share. One of the most commonly used digital media environments by OTAs are the metasearch bidding engines. In metasearch bidding engines
Post-impressions: internet advertising without click through .
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Internet Advertising Effectiveness Measurement of Internet advertising was traditionally based on CPM (Novak Hoffman) but relatively quickly click through rate became the standard measure Table 1: Click Through and Post-Impression Rate Means by Placement Type
The business next door: Click through rate modeling for local search
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Computational advertising has received a tremendous amount of attention from the business and academic community recently. Great advances have been made in modeling click through rates in well studied settings, such as, sponsored search and context match
Modeling contextual factors of click rates
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search results. If every link is always shown in a particular position, it is hard to separate the advertiser and positional effects on its click through rate unless we use some global measurement of positional effects. For these reasons
Predicting ads clickthrough rate with decision rules
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Page 1. Predicting Ads Click Through Rate with Decision Rules In this context, an ads quality can be measured by the probability of it being clicked assuming it was noticed by the user ( click through rate CTR). There are two problems hereIn the cost per click pricing model, an advertiser pays an ad network only when a user clicks on an ad; in turn, the ad network gives a share of that revenue to the publisher where the ad was impressed. Still, advertisers be unsatisfied with ad networks charging them for
Predict the click through rate and average cost per click for keywords using machine learning methodologies
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In search engine optimization (SEO), advertisers bid on tons of keywords on Google, for example, so that their clickable ads can appear in Googles search results. In order for the advertisers to maintain their budget more efficiently and achieve the best performance The application of traditional machine learning algorithms on click data has met great challenges working with severe sparse and transient ID features, which tend to bloat data size and prolong training time considerably. On the other hand, due to the data size
Deep Time-Stream Framework for Click through Rate Prediction by Tracking Interest Evolution.
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Click through rate (CTR) prediction is an essential task in industrial applications such as video recommendation. Recently, deep learning models have been proposed to learn the representation of users overall interests, while ignoring the fact that interests
Building a Cloud-Based Regression Model to Predict Click Through Rate in Business Messaging Campaigns
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The goal of the research presented here is to describe an innovative approach to predicting the impact of a business messaging campaign, by estimating the percentage of message recipients who will engage with a message. The motivation is to facilitate business
Constructing social intentional corpora to predict click through rate for search advertising
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In the beginning, search engines provide placements next to the original search results for advertisers on specific keywords. Since users often search for their interests or purchasing decision, timely presenting proper advertisements to users will encourage them to click on
The effect on click through of combining sponsored and non-sponsored search engine results in a single listing
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However, based on calculations of click through rate versus impressions for many key terms on Yahoo! and Googles sponsored search platforms, the ration of sponsored to non-sponsored links appears lower than 25 or 30 percent that has been reported
The impact of user reach of personalized advertisements on the click through rate
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This study investigates how the size of target market influences the click through rates of personalized online advertisement. We examined in a randomized field experiment the effectiveness of personalized banners shown on a social media website (Facebook). The
Ctr prediction based on click statistic
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ru Abstract A new click through rate predicting formula which is based on statistics of clicks and shows is described The main problem that we discuss in our paper is the part of click through rate prediction (CTR) used for Sponsored Search
Optimizing click through in online rankings for partially anonymous consumers
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Keywords: consumer search, hotel industry, popularity rankings, platform, collaborative fil- tering, click through rate customization, targeting, sorting, filtering, search refinement a targeted ranking of search results that maximizes the aggregate click through rate (CTR), which
The impact of banner advertisement frequency on click through responses
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A variety of factors have been identified that could improve the click through rate (CTR) such as BA size (Euijin Edwards), image (Chalmers), animation (Loutia, Donthu, Hershberger), product involvement (Dahlen, Rasch, Rosengren), and
Click through prediction for sponsored search advertising with hybrid models
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ABSTRACT In this paper, we report our approach of KDD Cup 2012 track 2 to predicting the click through rate (CTR) of adver- tisements 3.4.1 Weighted Ensemble of Individual Features Given all the features, our goal is to get the click through rate of each feature