A fastText-based hybrid recommender

Facebook Research’s new fastText library can learn the meaning of metadata from the text it labels. By labelling documents with the users who read them, we used fastText to hack together a “hybrid recommender” system, able to recommend documents to users using both collaborative information (“people who read this also liked that”) and whether the text in the documents is thematically similar to things they read previously. Early signs are it performs quite well, so we’ll continue to experiment with it.

Give me five


Give me five is an open source Chrome extension that allows you to recommend the content you push to Lateral based on the content of the page you’re currently visiting. It’s the same code base that the NewsBot Chrome extension is built upon. The screencast shows the extension in action: You can find the source code […]

Clustering debates from UK politicians


What kind of language do British parliamentarians use? We scraped, parsed and vectorised a sample of recent debates from the House of Commons. We then applied a k-means clustering algorithm to these vectors, and created a word cloud for each cluster. Click on the image below for an enlarged version. The image contains 24 word clouds, representing the 24 categories into […]

EMNLP 2015 accepted papers recommender

Ben, who is in charge of machine learning and data science at Lateral, is currently at EMNLP 2015 (a Conference on Empirical Methods in Natural Language Processing) in Lisbon. Ben was using the Conference4me app and messaged me saying that we should create a recommender for all the abstracts from the conference. It sounded like […]

Article Extractor API


Today we are pleased to announce the release of our Article Extractor API! When recommending content it’s important to ensure you are only recommending for the relevant text of an article. We have often faced this challenge with online articles and blogs. We’d want to fetch a URL but just extract the main body of […]