A Comparative Survey Of Algorithmic Feed Recommendation System Designs

Social media platforms are highly reliant on algorithmic feed systems to deliver content to users. Unlike content recommender systems typically studied in academia, recommendation algorithms for social media feeds are multi-stakeholder and designed to maximize usage, rather than relevance or affinity. How feed algorithms are designed and exactly what content is recommended to users has come under increasing scrutiny from the public and lawmakers. Companies have responded to this scrutiny with more transparency around their systems, including their recommendation algorithms. To aid in comparisons of these newly-transparent systems, we perform a survey of social media feed algorithm systems by conducting a qualitative document analysis of primary source documents. Our survey identifies salient design choices that different apps have made, and algorithm traits that result from those design choices. The key areas of our survey are feed content inventory selection, features used for ranking and four key algorithm traits, along with metrics that capture those traits. We also perform a case study of X’s recently open-sourced feed algorithm, with a particular focus on the key characteristics and algorithm traits identified in our larger survey.