Sponsored search adopts generalized second price (GSP) auction mechanism which works on the concept of pay per click which is most commonly used for the allocation of slots in the searched page. Two main aspects associated with GSP are the bidding amount and the click through rate (CTR). The CTR learning algorithms currently being used works on the basic principle of (#clicksi/#impressionsi) un...

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...

In online advertising, display ads are increasingly being placed based on realtime auctions where the advertiser who wins gets to serve the ad. This is called real-time bidding (RTB). In RTB, auctions have very tight time constraints on the order of 100ms. Therefore mechanisms for bidding intelligently such as clickthrough rate prediction need to be sufficiently fast. In this work, we propose t...

In recent years, with the rapid development of mobile Internet and its business applications, mobile advertising Click-Through Rate (CTR) estimation has become a hot research direction in the field of computational advertising, which is used to achieve accurate advertisement delivery for the best benefits in the three-side game between media, advertisers, and audiences. Current research on the ...

Our research question is motivated by a real problem faced an existing demand aggregator. The aggregator represents multiple advertisers, each of whom signs one two types contracts with the for bidding on RTB (real-time bidding) platform. A quality contract occurs cost-per-impression (CPM, i.e., cost per thousand impressions) basis. advertiser promised minimum number impressions and pays CPM th...

We describe a parallel bayesian online deep learning framework (PBODL) for clickthrough rate (CTR) prediction within today’s Tencent advertising system, which provides quick and accurate learning of user preferences. We first explain the framework with a deep probit regression model, which is trained with probabilistic back-propagation in the mode of assumed Gaussian density filtering. Then we ...

We study the impact of social influence on the performance of ads on social networks. Using data for several ads on Facebook for two different firms, we measure the impact of past connections on the click rate and endorsement rate of ads. We find that increase in the connections does not lead to an increase in the click performance and may even decrease the click performance. We also find that ...

Search advertising and display advertising are two major online advertising formats. Search advertising emphasizes ads’ click-through effect. Advertisers only pay when users click the link of their ads. Traditional display advertising emphasizes ads’ impression effect. Most display ads are charged based on the number of views on the ads. Considering that most online ads increase brand awareness...

Click-through information is considered as a valuable source of users’ implicit relevance feedback. As user behavior is usually influenced by a number of factors such as position, presentation style and site reputation, researchers have proposed a variety of assumptions (i.e. click models) to generate a reasonable estimation of result relevance. The construction of click models usually follow s...