ML System Designs: Personalized Feed Ranking System

Architecture for Efficient Personalized Feed Ranking System

Problem Navigation

Requirements

Business Objective: To design a personalized newsfeed ranking system that prioritizes and surfaces relevant content, such as posts, videos, and articles, based on user preferences, historical interactions, engagement patterns, and content attributes.

Real-world Examples:

Entertainment (Social Network)

• Facebook: Facebook’s News Feed ranks and displays posts, photos, videos, and links from friends, groups, and pages a user follows. The ranking is powered by machine learning algorithms that analyze user interactions, preferences, and engagement patterns to prioritize content most likely to be relevant or engaging for the user.

Professional Networking

• LinkedIn: LinkedIn’s feed showcases posts, articles, job updates, and user activities based on professional interests, connections, and engagement patterns. It uses signals such as content relevance, user profile attributes, and interactions to ensure personalized ranking of updates.

News Aggreration

• Flipboard: Flipboard curates and ranks articles and news stories tailored to…