  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.
