In online advertising campaigns, to measure purchase propensity, click-through rate (CTR), defined as a ratio of number of clicks to number of impressions, is one of the most informative metrics used in business activities such as performance evaluation and budget planning. No matter what channel an ad goes through (display ads, sponsored search or contextual advertising), CTR estimation for ra...

To better extract users’ interest by exploiting the rich historical behavior data is crucial for building the click-through rate (CTR) prediction model in the online advertising system in e-commerce industry. There are two key observations on user behavior data: i) diversity. Users are interested in different kinds of goods when visiting e-commerce site. ii) local activation. Whether users clic...

We study the problem of click-through rate (CTR) prediction, where the goal is to predict the probability that a user will click on a search advertisement given information about his issued query and account. In this paper, we formulate a model for CTR prediction using logistic regression, then assess the performance of stochastic gradient descent (SGD) and online limited-memory BFGS (oLBFGS) f...

A search engine is a system that tries to retrieve documents that the user needs, given the user’s query. No search engine is able to do this perfectly, for many reasons. Learning systems have previously been examined to get better retrieval, but with mixed results. The main goal of the report is to show that active exploration is necessary for learning in order to get good results. An algorith...

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 a Disguise-AdversarialNetworks (DAN) to improve the accuracy of supervised learning with limited positive-class information. In the c...

In a sponsored search auction the advertisement slots on a search result page are generally ordered by click-through rate. Bidders have a valuation, which is usually assumed to be linear in the click-through rate, a budget constraint, and receive at most one slot per search result page (round). We study multi-round sponsored search auctions, where the different rounds are linked through the bud...

For the past few years, we used Apache Lucene as recommendation framework in our scholarly-literature recommender system of the reference-management software Docear. In this paper, we share three lessons learned from our work with Lucene. First, recommendations with relevance scores below 0.025 tend to have significantly lower click-through rates than recommendations with relevance scores above...

Native advertising is a popular form of online advertisements that has similar styles and functions with the native content displayed on platforms, such as news, sports social websites. It can better capture users’ attention, they have gained increasing popularity in many platforms among advertisers. In advertising, Click Trough Rate (CTR) prediction essential but challenging due to data sparsi...

Promotions are becoming more important and prevalent in e-commerce to attract customers boost sales, leading frequent changes of occasions, which drives users behave differently. In such situations, most existing Click-Through Rate (CTR) models can’t generalize well online serving due distribution uncertainty the upcoming occasion. this paper, we propose a novel CTR model named MOEF for recomme...