Understanding Instagram’s Content Recommendation: A Probabilistic Approach using Markov Chains

Abstract

Social media platforms like Instagram personalise user experience by curating content feeds based on individual preferences and behaviour. This study explores the mathematical foundation behind Instagram’s personalised suggestions using a Markov Chain-based probabilistic model. By treating each user interaction as a transition between content categories (states), the system calculates the probability of the next likely interaction, refining the content shown over time. This sequential modelling not only helps platforms maximise engagement by predicting the most relevant content but also raises concerns about selective and incidental exposure, where users are consistently shown similar viewpoints. The research presents a conceptual explanation of how Markov Chains guide content flow to highlight the recursive nature of content ranking. It also emphasises the importance of balancing personalisation with content diversity. Theoretical and practical implications of the study reveal that while algorithms optimise user satisfaction, they may also limit exposure variety. By understanding the probabilistic structures, platforms can adjust their recommendation logic to introduce greater diversity, mitigating the effects of filter bubbles and echo chambers. This framework can inform future development of algorithms that not only predict but also broaden user engagement in healthier, more balanced digital spaces.