Recommending Recommenders

It seems like more and more of my engagement with various services I use involves some sort of recommender. The new version of Google Maps on my phone recommends a certain way to get from one place to another along with a couple other options. SoundCloud and Spotify are generating awesome recommended listening streams to me. Twitter tells me what I missed when I was away and gives me Highlights on my phone. Gmail recommends who else to send the email to. Etsy shows me things I might want to buy.

I am sure all of you are experiencing the same thing. Web and mobile apps are getting smarter and smarter about each of us and recommending things to us that until recently we had to figure out all by ourselves. It almost seems like recommenders are table stakes these days. You can’t even play in the game unless you can do this sort of thing. And that requires a data science team to sift through all the data on your service and make smart recommendations to your users.

This is one of many things that has tilted the web and mobile game in favor of the larger and more mature companies. But there are also tools that you can use to get machine learning as a service to compete with the big guys. Our portfolio company Clarifai has an API for machine learning for images and video, for example. If you are building a service that has a lot of images and video and know you need to build a recommender but don’t have the machine learning expertise in house, you may be able to do a lot with Clarifai.

My partner Albert calls this sort of thing the “unbundling of scale” and it is something entrepreneurs need to do more than ever as the big “new incumbents” are turning scale into advantage using data science.