衡量广告对LinkedIn Feed的长期影响
Measuring Long-term Impact of Ads on LinkedIn Feed
摘要：
自然更新（来自会员网络）和赞助更新（或广告，来自广告商）共同构成了LinkedIn上的新闻源。新闻源是成员的默认主页，吸引他们参与，为他们带来价值，并帮助LinkedIn成长。互动和收入是两个关键的，但往往相互冲突的目标。因此，设计一个良好的收入-参与权衡（Revenue-Engagement Tradeoff，简称RISK）机制来将广告混合在提要中是很重要的。在本文中，我们设计了实验，以了解如何成员的行为随着时间的推移，不同的广告经验。这些体验因广告密度而异，而广告的质量（由相关性模型确保）保持不变。我们的实验是在随机成员桶上进行的，我们使用两种实验设计来测量各种治疗的短期和长期效果。根据前三个月的数据，我们观察到，在我们的应用中，长期影响的规模远远小于短期影响。此外，我们观察到不同的成员群体（基于用户活动水平）随着时间的推移适应和反应不同。
英文摘要：
Organic updates (from a member's network) and sponsored updates (or ads, from advertisers) together form the newsfeed on LinkedIn. The newsfeed, the default homepage for members, attracts them to engage, brings them value and helps LinkedIn grow. Engagement and Revenue on feed are two critical, yet often conflicting objectives. Hence, it is important to design a good Revenue-Engagement Tradeoff (RENT) mechanism to blend ads in the feed. In this paper, we design experiments to understand how members' behavior evolve over time given different ads experiences. These experiences vary on ads density, while the quality of ads (ensured by relevance models) is held constant. Our experiments have been conducted on randomized member buckets and we use two experimental designs to measure the short term and long term effects of the various treatments. Based on the first three months' data, we observe that the long term impact is at a much smaller scale than the short term impact in our application. Furthermore, we observe different member cohorts (based on user activity level) adapt and react differently over time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
