Ads Allocation in Feed via Constrained Optimization

Abstract

Social networks and content publishing platforms have newsfeed applications, which show both organic content to drive engagement, and ads to drive revenue. This paper focuses on the problem of ads allocation in a newsfeed to achieve an optimal balance of revenue and engagement. To the best of our knowledge, we are the first to report practical solutions to this business-critical and popular problem in industry. The paper describes how large-scale recommender system like feed ranking works, and why it is useful to consider ads allocation as a post-operation once the ranking of organic items and (separately) the ranking of ads are done. A set of computationally lightweight algorithms are proposed based on various sets of assumptions in the context of ads on the LinkedIn newsfeed. Through both offline simulation and online A/B tests, benefits of the proposed solutions are demonstrated. The best performing algorithm is currently fully deployed on the LinkedIn newsfeed and is serving all live traffic.