This is how we recommend brand new TV shows.

Watchworthy is a personalized TV recommendation app that leverages over 1B votes cast by TV fans on Ranker lists. Visitors to Ranker.com can vote the newest and most talked about TV Shows up and down on various lists, from "Best New Horror Shows" to "Funniest Sitcoms Ever Made." These votes constitute a trove of anonymous crowdsourced data that gives us valuable insight into taste correlations. But despite this massive volume of users we have to train on, like most developers using a user-to-item dataset, we face the classic "cold start" issue: how do we recommend brand-new shows that relatively few people have voted on? This problem is further complicated by a request to use voting behavior (rather than metadata) as much as possible when building out this recommendation engine. We present an unique approach that parses out existing user voting profiles to create additional users, called “split users". These split users will be grouped into separate training sets to create multiple sub-models. By creating an ensemble based on the submodels and applying a "most pleasure" strategy, we achieve the goal of recommending new shows.