Recommendation Engines for News Are Terrible. Scoring Stories Could Help.
For news media, recommendation engines are a horror show. The NQS project I’m working on at Stanford forced me to look at the way publishers try to keep readers on their property — and how the vast majority conspire to actually lose them.
I will resist putting terrible screenshots I collected for my research… Instead, we'll look at practices that prevent a visitor from continuing to circulate inside a website (desktop or mobile):
— Most recommended stories are simply irrelevant. Automated, keyword-based recommendations yield poor results: merely mentioning a person's name, or various named entities (countries, cities, brands) too often digs up items that have nothing to do with the subject matter. In other words, without a relevancy weight attached to keywords in the context of a story, keyword-based recommendations are useless. Unfortunately, they're widespread.
Similarly, little or no effort is made to disambiguate possibly confusing words: in a major legacy media, I just saw an op-ed about sexual harassment that referred to Harvey Weinstein connected to… a piece on Donald Trump’s dealings with Hurricane Harvey; the article is also linked to Amazon's takeover of the retail industry… only because of a random coincidence: the articles happened to mention Facebook.