Deeply supervised model for click-through rate prediction in sponsored search
نویسندگان
چکیده
منابع مشابه
Deeply Supervised Semantic Model for Click-Through Rate Prediction in Sponsored Search
In sponsored search it is critical to match ads that are relevant to a query and to accurately predict their likelihood of being clicked. Commercial search engines typically use machine learning models for both query-ad relevance matching and click-through-rate (CTR) prediction. However, matching models are based on the similarity between a query and an ad, ignoring the fact that a retrieved ad...
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We describe a new Bayesian click-through rate (CTR) prediction algorithm used for Sponsored Search in Microsoft’s Bing search engine. The algorithm is based on a probit regression model that maps discrete or real-valued input features to probabilities. It maintains Gaussian beliefs over weights of the model and performs Gaussian online updates derived from approximate message passing. Scalabili...
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In this paper, we report our approach of KDD Cup 2012 track 2 to predicting the click-through rate (CTR) of advertisements. To accurately predict the CTR of an ad is important for commercial search engine companies for deciding the click prices and the order of impressions. We first implemented three existing methods including Online Bayesian Probit Regression (BPR), Support Vector Machine (SVM...
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Sponsored search is a multi-billion dollar industry and makes up a major source of revenue for search engines (SE). click-through-rate (CTR) estimation plays a crucial role for ads selection, and greatly affects the SE revenue, advertiser traffic and user experience. We propose a novel architecture for solving CTR prediction problem by combining artificial neural networks (ANN) with decision tr...
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Click prediction is one of the fundamental problems in sponsored search. Most of existing studies took advantage of machine learning approaches to predict ad click for each event of ad view independently. However, as observed in the real-world sponsored search system, user’s behaviors on ads yield high dependency on how the user behaved along with the past time, especially in terms of what quer...
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ژورنال
عنوان ژورنال: Data Mining and Knowledge Discovery
سال: 2019
ISSN: 1384-5810,1573-756X
DOI: 10.1007/s10618-019-00625-3