# fishbowl

Published articles for fishbowl.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## Personalized Fishbowl Recommendations with Learned Embeddings: Part 1

DevFeed: [Personalized Fishbowl Recommendations with Learned Embeddings: Part 1](<https://devfeed.tech/articles/personalized-fishbowl-recommendations-with-learned-embeddings-part-1-22615.md>)

Original publisher: [Read original article](<https://medium.com/glassdoor-engineering/personalized-fishbowl-recommendations-with-learned-embeddings-part-1-6031abe84661?source=rss----288d984af747---4>)

Author: Ahmad Khan

Published: 2022-01-07T21:48:24Z

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>), [personalization](<https://devfeed.tech/topics/personalization.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>)

Tags: [doc2vec](<https://devfeed.tech/tags/doc2vec.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [fishbowl](<https://devfeed.tech/tags/fishbowl.md>), [glassdoor](<https://devfeed.tech/tags/glassdoor.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [personalization](<https://devfeed.tech/tags/personalization.md>), [recommendation-system](<https://devfeed.tech/tags/recommendation-system.md>), [recommendations](<https://devfeed.tech/tags/recommendations.md>), [word-embeddings](<https://devfeed.tech/tags/word-embeddings.md>)

### 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