通过生存分析提高原生广告的点击后用户粘性

Nicola Barbieri, F. Silvestri, M. Lalmas
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引用次数: 57

摘要

在本文中,我们主要通过预测相应广告登陆页面的停留时间来估计原生广告的点击后粘性。为了推断广告特征与停留时间之间的关系,我们采用了生存分析技术,这使我们能够估计用户将在广告上花费的时间长度的分布。然后将这些信息整合到广告排名功能中,目标是提高可能被用户点击和消费的广告的排名(停留时间大于给定阈值)。对实时流量的在线评估表明,考虑点击后粘性对点击率和平均停留时间都有持续的积极影响,减少了反弹次数,增加了平均停留时间,从而带来了更好的用户点击后体验。
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Improving Post-Click User Engagement on Native Ads via Survival Analysis
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.
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