- tumblr: We've introduced a new feature that kills your firstborn.
- xkit guy: welp, time to program an extension that paints lamb's blood on your sidebar
Marketers, if you’re interested in this topic, even if you don’t intend on setting up infrastructure on your own, I recommend clicking through to their blog and watching the 44 minute tutorial. You may think, “I’m a marketer, I don’t need to get this stuff.” Then how come you’re studying social network analysis? (There’s a bit of overlap! #gotcha)
In the tutorial, you’ll learn a bit about recommender systems in general. Also, if you are interested in your own set-up, he gives really cool options for every step of the way. You don’t haaaaaave to use the Mortar service. You can use free/open source stuff for every part.
A few weeks ago we made an announcement: our recommendation engine, which our engineers and data science advisors built in consultation with select partner companies, is now open source and available to all. The response from the community has been tremendous—tens of thousands of people read our blog post announcing the news. And many of them dove right into our Github repo and tutorials and got to work.
If your company is among the many that use MongoDB, we’ve just made the path to generating personalized recommendations even smoother. Our step-by-step tutorial on building a recommendation engine is peppered with callouts that highlight information specific to Mongo users. We have an entire section of our documentation site devoted to analyzing MongoDB data with Hadoop, which is at the core of our recommendation engine. And our CEO, K Young, recently showed MongoDB users how it all fits together by hosting a webinar that walks through the process of building a recommendation engine with Mongo data.
Our goal in building the Mortar recommendation engine was to create a product that any data scientist or engineer could use, regardless of budget or experience with Hadoop and its associated technologies. So we made all our code available for free, and we wrote an obsessively detailed tutorial that is as informative as it is easy to follow. Our integration with Mongo (and extensive documentation thereof) makes the recommendation engine even more broadly applicable. If you missed the webinar, a video recording of the presentation is embedded below, along with K’s slides. So give it a look—and then dive into our tutorial to start building a powerful recommender with your Mongo data.