Two decades ago, a then-nascent DVD rental service adopted an emerging technology: a relatively basic algorithm that made simple recommendations to users based on what their fellow members liked. The service was Netflix, its software was Cinematch and it represented a turning point in the history of entertainment as we know it.
As a precursor to the recommendation systems that power streaming today, Cinematch’s algorithm was based on collaborative filtering. It took millions of its members’ film ratings and used them to predict how much other members would enjoy the same movies. It had its limitations. The recommendations were only as accurate as the ratings from members. It struggled to recommend films to users who were yet to rate titles. It failed to factor in that multiple people may be using the same profile. Yet with Cinematch, Netflix saw the benefits that personalising people’s experiences could have on engagement. Alongside the likes of Amazon...