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.
A simple extension to Wikipedia that displays similar pages at the top of every Wikipedia page!
A technique we use to visualise how Lateral recommendations would look and work on a website is to create a Chrome extension that inserts the recommendations at load time. This is useful because: No access is required to the websites source files The extension shares assets with the page, so matching styling is easy It allows […]
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 […]
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 […]
We recently had to migrate our multiple PostgreSQL databases between cloud providers. We wanted to keep downtime to an absolute minimum. To achieve this we created replicas of the live databases on a new database server. Then to switch to the new database all we had to do was momentarily take a server offline while […]
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 […]
Previously we’ve written about how machines can learn meaning. One of the exciting opportunities of this approach is that it also means they can learn new languages very quickly. All you need is enough text data. Wikipedia offers a great starting point and partnering with content providers enables us to quickly gather additional data. We […]
A while back we partnered up with Blockspring to enable anyone to use our API without needing to write any code. They’ve created an awesome solution that allows you to make use of a range of great APIs using only a spreadsheet. This enables you to bring data into your spreadsheet, run text-analysis and much more. […]
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 […]