With Gaurav Gandhi
Recommendation engines are everywhere. These systems recommend what shows to watch on Netflix and what products to buy on Amazon. Since at least the Netflix Prize, the conventional wisdom is that recommendation engines have become very good. Except that they are not. Some of the deficiencies are deliberate. Netflix has made a huge bet on its shows, and it makes every effort to highlight its Originals over everything else. Some other efficiencies are a result of a lack of content. The fact is easily proved. How often have you done a futile extended search for something “good” to watch?
Take the above two explanations out, and still, the quality of recommendations is poor. For instance, Youtube struggles to recommend high-quality, relevant videos on machine learning. It fails on relevance because it either recommends videos that are too difficult or too easy. And it fails on quality—the opaqueness of explanation makes most points hard to understand. When I look back, most of the high-quality content on machine learning that I have come across is a result of subscribing to the right channels—human curation. Take another painful aspect of most recommendations: a narrow understanding of our interests. You watch a few food travel shows, and YouTube will recommend twenty others.
What is the next best thing to learn? It is an important question to ask. To answer it, we need to know the objective function. But the objective function is too hard to formalize and yet harder to estimate. Is it money we want, or is it knowledge, or is it happiness? Say we decide its money. For most people, after accounting for risk, the answer may be: learn to program. But what would the equilibrium effects be if everyone did that? Not great. So we ask a simpler question: what is the next reasonable unit of information to learn?
Meno’s paradox states that we cannot be curious about something that we know. Pair it with a Rumsfeld-ism: we cannot be curious about things we don’t know we don’t know. The domain of things we can be curious about hence are things we know that we don’t know. For instance, I know that I don’t know enough about dark matter. But the complete set of things we can be curious about includes things we don’t know we don’t know.
The set of things that we don’t know is very large. But that is not the set of information we can know. The set of relevant information is described by the frontier of our knowledge. The unit of information we are ready to consume is not a random unit from the set of things we don’t know but the set of things we can learn given what we know. As I note above, a bunch of ML lectures that YouTube recommends are beyond me.
There is a further constraint on ‘relevance.’ Of the relevant set, we are only curious about things we are interested in. But it is an open question about how we entice people to learn about things that they will find interesting. It is the same challenge Netflix faces when trying to educate people about movies people haven’t heard or seen.
Conditional on knowing the next best substantive unit, we care about quality. People want content that will help them learn what they are interested in most efficiently. So we need to solve for the best source to learn the information.
For things we know, the big question is how do we optimally retain things we know. It may be through Flashcards or what have you.
Exploring the Unknown
- Learn What a Person Knows (Doesn’t Know): The first step is in learning the set of information that the person doesn’t know is to learn what a person knows. The best way to learn what a person knows is to build a system that monitors all the information we consume on the Internet.
- Classify. Next, use ML to split the information into broad areas.
- Estimate The Frontier of Knowledge. To establish the frontier of knowledge, we need to put what people know on a scale. We can derive that scale by exploiting syllabi and class structure (101, 102, etc.) and associated content and then scaling all the content (Youtube video, books, etc.) by estimating similarity with the relevant level of content. (We can plausibly also establish a scale by following the paths people follow–videos that they start but don’t finish are good indications of being too easy or too hard, for instance.)
We can also use tools like quizzes to establish the frontier, but the quizzes will need to be built from a system that understands how information is stacked.
- Estimate Quality of Content. Rank content within each topic and each level by quality. Infer quality through both explicit and implicit measures. Use this to build the relevant set.
- Recommend from the relevant set. Recommend a wide variety of content from the relevant set.