# glassdoor Published articles for glassdoor. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Inside Glassdoor's Multi-Stage Recommendation System DevFeed: [Inside Glassdoor's Multi-Stage Recommendation System]() Original publisher: [Read original article]() Author: Prateek Shekhar Published: 2025-08-12T19:37:39Z Content type: article Language: en Sources: [Glassdoor Engineering]() Topics: [recommendation systems](), [recommendations](), [personalization](), [Algorithms](), [ordering](), [ann]() Tags: [algorithms](), [ann](), [glassdoor](), [ordering](), [personalization](), [recommendation-system](), [recommendation-systems](), [recommender-systems]() ### 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 ## Building Glassdoor's Machine Learning Platform and Engineering Team DevFeed: [Building Glassdoor's Machine Learning Platform and Engineering Team]() Original publisher: [Read original article]() Author: Malathi Sankar Published: 2022-08-01T07:19:34Z Content type: article Language: en Sources: [Glassdoor Engineering]() Topics: [MLOps](), [Machine Learning & Artificial Intelligence](), [Data Infrastructure](), [DevOps](), [Monitoring](), [airflow](), [debug](), [Provisioning]() Tags: [airflow](), [data](), [data-infrastructure](), [debugging](), [devops](), [engineering](), [glassdoor](), [infrastructure](), [machine-learning](), [machine-learning-platform](), [ml-engineering](), [mlops](), [monitoring](), [organization]() ### AI overview This article describes how Glassdoor developed and scaled its machine learning platform and engineering team. It covers the organization's early machine learning systems, deployment and infrastructure challenges, adoption of MLOps practices, and the use of combined open-source, commercial, and proprietary tools to orchestrate its machine learning pipeline. ### Source excerpt In this post, I would like to share how Glassdoor built and scaled its ML platform and engineering team and what we learned along the way. Historic ML journey at Glassdoor Ten years ago, machine learning at Glassdoor started with a straightforward logistic regression model, which was used to power our job recommendations. Today machine learning is ubiquitous. It is used across most of our products -- search, ads, reviews, salaries, interviews, benefits, and Fishbowl post recommendations, to name a few. Data quality, model innovations, hardware advancements, data infrastructure, resource investments, and tools propelled this growth. Until 2021, our machine learning team was mostly comprised of scientists who were experts in math and statistics. They worked with data engineers to prepare data and build optimized models to solve problems. Batch deployments were done in Airflow by the scientists, while project engineers carried out the online model deployments. Infrastructure provisioning was left to the DevOps team. This organizational structure was less than ideal, however, and created many challenges. Communication gaps and resource misallocations made deployments tedious and time-consuming. Monitoring, observing, maintaining, and debugging models were difficult. Machine Learning's technical debt grew rapidly and had a negative impact on Glassdoor's operations. Adoption of MLOps framework: To deal with the above challenges, we conceptualized real-time machine learning as primarily an engineering and infrastructure problem. As Scully et al. have discussed in this paper [1], only a small fraction of a real-world ML system is composed of ML code (illustrated by the black box in Figure 1), while the surrounding infrastructure is vast and complex. By adopting a set of DevOps and Engineering best practices known as MLOps, we were able to effectively manage this complexity. As previously discussed in our blog, our initial approach comprised a mix of open source tools, commer ## Personalized Fishbowl Recommendations with Learned Embeddings: Part 1 DevFeed: [Personalized Fishbowl Recommendations with Learned Embeddings: Part 1]() Original publisher: [Read original article]() Author: Ahmad Khan Published: 2022-01-07T21:48:24Z Content type: article Language: en Sources: [Glassdoor Engineering]() Topics: [recommendation systems](), [personalization](), [Machine learning](), [Embeddings]() Tags: [doc2vec](), [embeddings](), [fishbowl](), [glassdoor](), [machine-learning](), [personalization](), [recommendation-system](), [recommendations](), [word-embeddings]() ### AI overview Glassdoor's Fishbowl team describes the recommendation problem created by a growing volume of posts and explains the move from recency and global-popularity rankings toward personalized recommendations using machine learning and learned embeddings. ### Source excerpt Glassdoor recently acquired Fishbowl, a professional networking community where working professionals can have workplace related conversations with other peers in the industry. Fishbowl users can anonymously write posts and see posts from other anonymous users in what we call "bowls": a collection of posts related to a certain workplace, industry or topic. Bowls can be an effective way to gain insights into workplace topics and conversations. The anonymous nature of the app can further encourage honest and frank discussion on topics users may otherwise feel uncomfortable discussing. Users can subscribe to different bowls and then see new posts from their subscribed bowls show up in their main home feed when they open the app. Every day Fishbowl users post thousands of new posts. With a growing and increasingly active user base that number will keep increasing. Surfacing the most interesting content to users can therefore become increasingly challenging with scale and a lack of personalization can detract from the overall user experience. Given the large number of possible posts to recommend and the small number of posts that can be surfaced to the user in app at any time, we have a typical recommendation system problem. From Global Rankings to Personalized Recommendations To personalize the posts recommended to a user we decided to use Machine Learning shortly after Glassdoor's acquisition. Prior to this Fishbowl just used the recency and global popularity of a post to sort what to show users in app. At the time of starting the project we also did not collect any explicit user click data that could have defined our problem into a classic supervised learning problem (e.g: predicting the probability of a user clicking a post given they saw the post). While we collected data on what posts a user anonymously liked or commented on we did not collect good data on if they saw or clicked a post at the time. Why is this important? If training a supervised model, we can use s ## Questions to Ask a Potential Employer Before and During a Job Interview DevFeed: [Questions to Ask a Potential Employer Before and During a Job Interview]() Original publisher: [Read original article]() Published: 2017-09-19T00:00:00Z Content type: opinion Language: en Sources: [Paweł Chudzik]() Topics: [Job](), [To-Do]() Tags: [career](), [company](), [decision-making](), [glassdoor](), [interview](), [job](), [linkedin](), [management](), [opinions](), [skills](), [work]() ### AI overview A personal advice article about evaluating a potential employer before and during a job interview. It recommends researching the company through Glassdoor and LinkedIn, asking colleagues about it, and examining interview questions and workplace problems to identify possible red flags. ### Source excerpt At some point in your career, you will not be happy with just a job, you'll be looking for something which will keep you interested and will sharpen your skills. Lately, I've been looking for something new to do. Since I got burned last time I've jumped ships I decided it is the time to prepare a list of the interview questions for my potential employer. Read more