Machine Learning for Survival Analysis: A Survey <s> Click-Through Rate <s> Search engine advertising has become a significant element of the Web browsing experience. Choosing the right ads for the query and the order in which they are displayed greatly affects the probability that a user will see and click on each ad. This ranking has a strong impact on the revenue the search engine receives from the ads. Further, showing the user an ad that they prefer to click on improves user satisfaction. For these reasons, it is important to be able to accurately estimate the click-through rate of ads in the system. For ads that have been displayed repeatedly, this is empirically measurable, but for new ads, other means must be used. We show that we can use features of ads, terms, and advertisers to learn a model that accurately predicts the click-though rate for new ads. We also show that using our model improves the convergence and performance of an advertising system. As a result, our model increases both revenue and user satisfaction. <s> BIB001 </s> Machine Learning for Survival Analysis: A Survey <s> Click-Through Rate <s> In this paper we focus on estimating the post-click engagement on native ads by predicting the dwell time on the corresponding ad landing pages. To infer relationships between features of the ads and dwell time we resort to the application of survival analysis techniques, which allow us to estimate the distribution of the length of time that the user will spend on the ad. This information is then integrated into the ad ranking function with the goal of promoting the rank of ads that are likely to be clicked and consumed by users (dwell time greater than a given threshold). The online evaluation over live traffic shows that considering post-click engagement has a consistent positive effect on both CTR, decreases the number of bounces and increases the average dwell time, hence leading to a better user post-click experience. <s> BIB002
Nowadays, many free web services, including online news portals, search engines, and social networks present users with advertisements BIB002 ). Both the topics and the display orders of the ads will affect user clicking probability BIB001 . Studies have mostly focused on predicting the Click-Through Rate (CTR), which indicates the percentage of users who click on a given ad. This can be calculated as the ratio of the clicking times and the corresponding presentation times (no. of ad impressions). The CTR value indicates the attraction effect of the ad to users BIB002 ). The goal is to predict how likely the user will be to click on specific ads based on the available information about the website, users, and ads. The time taken to click the ad is considered the event time. Those users who did not click on the ads are considered to be censored observations.