LLM Recommendations

Here's another area where large language models can do a fantastic job: Content recommendations.

It's no secret that the recommendation algorithms of YouTube, Spotify, TikTok, etc have come under scrutiny. One issue is that they're blindly optimized to drive engagement, which has been shown to lead viewers down rabbit holes of increasingly "extreme" content. TikTok, for example, is frighteningly good at sensing if content pushes your buttons, and before you know it, your feed is nothing but that, dialled up to 11 by the content creators vying for relevance on the platform.

But even if the algorithms were mostly harmless in their recommendations, they're also exceptionally bland. I have yet to make mind-blowing discoveries purely via the "you might also like" feature. These features are, for the most part, just presenting you with the average stuff people like you would listen to or watch. That inevitably pulls it into mediocrity and the least common denominator.

Recently, I tried out just asking ChatGPT. I told it about the artists and styles I generally like, and that I needed music for a road trip. We went back and forth with it, putting forth albums to listen to, and I told it what I liked/didn't like about each one. We ended up in pretty unexpected places, and I discovered several new bands that I'll keep following.

Now, music picking isn't the most world-changing use case, but the implications are larger. The lack of real understanding of traditional recommender algorithms means that their usefulness is limited, leading you either down rabbit holes or in circles. With a better understanding of the underlying subject, a recommender can unearth gems that are just what you wanted.