# Inside Glassdoor's Multi-Stage Recommendation System

DevFeed: [Inside Glassdoor's Multi-Stage Recommendation System](<https://devfeed.tech/articles/inside-glassdoor-s-multi-stage-recommendation-system-22611.md>)

Original publisher: [Read original article](<https://medium.com/glassdoor-engineering/inside-glassdoors-multi-stage-recommendation-system-cee58b52a75a?source=rss----288d984af747---4>)

Author: Prateek Shekhar

Published: 2025-08-12T19:37:39Z

Content type: article

Language: en

Sources: [Glassdoor Engineering](<https://devfeed.tech/sources/glassdoor-engineering.md>)

Topics: [recommendation systems](<https://devfeed.tech/topics/recommendation-systems.md>), [recommendations](<https://devfeed.tech/topics/recommendations.md>), [personalization](<https://devfeed.tech/topics/personalization.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>), [ordering](<https://devfeed.tech/topics/ordering.md>), [ann](<https://devfeed.tech/topics/ann.md>)

Tags: [algorithms](<https://devfeed.tech/tags/algorithms.md>), [ann](<https://devfeed.tech/tags/ann.md>), [glassdoor](<https://devfeed.tech/tags/glassdoor.md>), [ordering](<https://devfeed.tech/tags/ordering.md>), [personalization](<https://devfeed.tech/tags/personalization.md>), [recommendation-system](<https://devfeed.tech/tags/recommendation-system.md>), [recommendation-systems](<https://devfeed.tech/tags/recommendation-systems.md>), [recommender-systems](<https://devfeed.tech/tags/recommender-systems.md>)

## AI overview

This article gives a high-level overview of Glassdoor's hybrid, multi-stage recommendation system. It describes how the system uses user behavior, similar-user behavior, and item features to produce personalized recommendations for the Community Feed, Email, and Push notifications, with stages including candidate generation, filtering, scoring, and ordering.

## Source excerpt

Introduction: Recommendation systems are central to modern digital platforms, delivering personalized content by intelligently analyzing user behavior and preferences through advanced algorithms. Widely used in e-commerce, streaming services, social media, and online learning, they drive user satisfaction, engagement, and retention while supporting business growth. Typically, these systems operate using one or more of the following approaches: Collaborative Filtering: This approach examines user behavior by comparing how users interact with items. It identifies patterns by analyzing similar user actions. For example, if two users have watched or purchased similar items, the system will recommend those same items to other users with similar behavior. Content-Based Filtering: In this approach, the system analyzes the characteristics of the items themselves, such as text, images, or other features. It recommends items that have features similar to those a user has previously liked. For example, if a user enjoyed books with specific keywords, the system will suggest books with those exact keywords. Hybrid Approach: This approach combines collaborative and content-based filtering to enhance personalization and accuracy. It utilizes user behavior patterns in conjunction with item features, offering more personalized and accurate recommendations. At Glassdoor, our Community Feed, Email, and Push notifications are powered by a recommendation system that follows a hybrid approach. It analyzes individual users' behavior, such as posts, comments, and reactions, as well as the behavior of similar users, to deliver tailored, personalized recommendations. This post presents a high-level overview of the multi-stage architecture behind our system, highlighting candidate generation, filtering, scoring, ordering, and supporting infrastructure. Glassdoor's Recommendation System ArchitectureFigure 1: Glassdoor's Recommendation System Architecture Glassdoor's Recommendation System follo