Social Is The New Black

One of the most abused phrases I hear these days is Social + [insert any noun here]. Conventional wisdom appears to regard communication between people as a bold new concept. This exciting new medium adds value to any business (check out the astronomical valuations of the privately held companies on SharesPost).

Sarcasm aside -

so·cial (adjective) : offering opportunity for interaction; allowing people to meet and interact with others in a friendly way

The truth of the matter is that social is asked about whenever I discuss a recsys implementation with clients – no matter the vertical. Some opportunities lend themselves well to an articulate social component plus a recsys while still others struggle to find social relativity. Thus, I have proactively endeavored to find concrete strategies in which to use ‘social’ data in productive and meaningful ways, especially with respect to an implementation of a recsys.

Do I Really Want Recommendations From My Friends

We may have 500 friends on Facebook or Google Plus but do we really want each and every one of them driving recommendation decisions for us? Imagine the experience you have every time you visit your wall, how it’s littered mostly with things that aren’t directly or immediately interesting to you. Now imagine that same experience applied to music, books or TV. Simply adding friends to the mix will not add value; more likely it will invoke churn from your service.

Hey friend, what do you like?

There are more informed approaches for incorporating the data from the social graph that can deliver a positive uptick in consumption. One such approach is the representation of a user’s friend set in order to reinforce a recommendation. This means we still rely on a user’s behavior within an ecosystem (supervised learning) to arrive at a recommendation decision but choose to complement the result with a list of friends that have also engaged with the recommended item. In this way we benefit from both machine learning and the social graph – we just don’t use the social graph to drive the decision. Cool.

Filter My Friends

Another interesting approach examines the relative usefulness (conversion percentage) of your friends’ recommendations to you. Observing the success and failure of your friends’ recommendations to you effectively turns them into a channel from which statistical probabilities of success can be rendered.

I like what you picked!

This success metric can be used to rank order future recommendations from ‘high quality’ friends and can cleanly winnow away the noise.

A beneficial side effect of using a qualified friend recommender is the possibility for more prolific experiences of serendipity. The facility to surprise and delight a consumer cannot be underestimated. While we are refining mathematical pathways to successfully discover interesting elements that fall outside the known model of the user, the incorporation of high quality external influencers is a welcome commodity.

Social DOES Have Value

When more than a hand wave toward social is given we can discover many influential uses for social data. The overuse of the term will surely relax over time – I will just have to hold my tongue until the next big buzzword takes its place.