# Personalized Fishbowl Recommendations with Learned Embeddings: Part 2

DevFeed: [Personalized Fishbowl Recommendations with Learned Embeddings: Part 2](<https://devfeed.tech/articles/personalized-fishbowl-recommendations-with-learned-embeddings-part-2-22616.md>)

Original publisher: [Read original article](<https://medium.com/glassdoor-engineering/personalized-fishbowl-recommendations-with-learned-embeddings-part-2-78a16b04d396?source=rss----288d984af747---4>)

Author: Ahmad Khan

Published: 2022-04-05T00:24:03Z

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>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [datasets](<https://devfeed.tech/topics/datasets.md>)

Tags: [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [doc2vec](<https://devfeed.tech/tags/doc2vec.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [model](<https://devfeed.tech/tags/model.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [personalization](<https://devfeed.tech/tags/personalization.md>), [quality](<https://devfeed.tech/tags/quality.md>), [ranking](<https://devfeed.tech/tags/ranking.md>), [recommendations](<https://devfeed.tech/tags/recommendations.md>), [recommender-systems](<https://devfeed.tech/tags/recommender-systems.md>), [research](<https://devfeed.tech/tags/research.md>), [word-embeddings](<https://devfeed.tech/tags/word-embeddings.md>)

## AI overview

This engineering article explains how Fishbowl recommendations can use learned embeddings. It describes Doc2Vec-based post and user embeddings, collaborative signals from users with similar likes, cosine-similarity ranking, and additional personalization features such as employer, job title, work city, and feed information. It also introduces transfer learning for text content embeddings.

## Source excerpt

Introduction In the previous blog post, we saw how we can utilize text based embeddings to help recommend posts to users on Fishbowl, a professional networking community that was recently acquired by Glassdoor, in which working professionals can have workplace related conversations with other peers in industry. On Fishbowl, users can anonymously write what's on their mind in posts and also comment on posts from other anonymous users in what we call "bowls" or "feeds"; a collection of posts related to a certain industry or topic. Previously we discussed how in the absence of clear negative signals from click stream data we cannot as easily use a supervised learning setup to rank items to users. Given the inputs we used were text based, we can instead use more unsupervised methods like Doc2Vec [1] to generate post text embeddings. We can treat the text of the posts as individual documents and use those to train a Doc2Vec model to generate a post text embedding. For users, we can take the average post text embedding of the posts the user liked and consider that as the user embedding. We can also add the user embeddings of other users who liked a post into our post embedding calculation so we incorporate some "collaborative" notion of what other similar users liked as opposed to a pure content similarity ranking. We can next compute the cosine similarity between the user and posts embeddings and use the similarity score to rank posts to recommend to users. Ranking via Embeddings Overview However, such an approach can have some shortcomings. First the text of the post is just one of many features we can use for personalization. We can also leverage other user provided information when users agree to sign up like the employer, job title, work city of a user as features. In addition, the bowl or feed name and its description can be considered important features for posts as posts under the same feed tend to be about similar topics. Second, the previous Doc2Vec approach tra