In this blog post we demonstrate how to generate a dataset for recommending Reddit posts based on semantic similarity. The Reddit API and the PRAW Python library are used to extract data from the AskScience subreddit. The posts are then analysed using LIP and built into a Chrome extension for searching similar content.
We have many different ways of delivering the Lateral API to clients who would like to install it in their own environment. One of those is as an Azure VHD for deployment to Azure VMs. In this post I will cover how to create a VHD that is fully compatible with Azure from an Ubuntu Cloud Image base.
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.
Wikipedia is one of the most widely used websites globally. We built a simple extension to 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. In this blog post, I will create a Chrome extension that modifies this blog to set a custom background and to modify the HTML.
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.
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.
We recently had to migrate our multiple PostgreSQL databases between cloud providers. We wanted to keep downtime to an absolute minimum. This is what we did using Londiste.
Creating a similar content recommender for all the abstracts of this year’s EMNLP 2015 Conference in Lisbon using Lateral’s machine learning API.
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. We have recently started working on supporting new languages, and thought we would share some initial impressions here.