Understanding the Constructs of Human Preferences

Every day, nearly every person in the world is faced with having to make decisions about what they want to eat, where they want to go, and what they want to read. Our minds make these decisions by using some form of biological algorithm that processes data made up of a combination of our DNA, experiences, environment, social relationships, moral compass, and a variety of other things that help give us our sense of self. I’m not exactly suggesting that our minds work just like some advanced computer algorithm does (although I’m sure it would be reasonable to suggest a correlation), but it is safe to assume that there is generally some rhyme and reason to the way people make decisions — an algorithm unique to that person.

Whereas computer software runs tens of thousands of calculations a second to determine some output, when we think about whether or not we will like something, we’re often less calculative than a computer would be. For instance, when evaluating a book for purchase, we mentally reference and note books that we’ve already read that are similar (or not at all similar) to the one we’re considering buying, but we do not think of a precise number that indicates just how much we liked those previous books. We do not think “This book is 0.6 in similarity, 0.4 in how fun it is to read, and 0.943 in certainty that my friends will approve of this purchase.” People are much more fuzzy, broad, and generalists than computers are. Computers love to munch on numbers all day long; it’s the language they were built to understand. People can’t think in precision for every decision they make. Instead, a person thinks “I believe this is the book my mother recommended. It will look beautiful on my coffee table. It reminds me of a book a saw at my cousin’s house. And it’s on sale!”

The disconnect between people and computers when it comes to understanding human preferences should begin to become obvious now. Computers expect to be able to process algorithms with precise data, but people expect to “process their algorithms” using abstract concepts and feelings which are not usually directly expressed in numeral notations. In order for computers to help people understand what their tastes and preferences are, they will need to begin to start thinking like people do; a task that researchers have spent a half of a century trying to do (artificial intelligence, neural networks, etc).

As a stop gap until computers can begin to understand human expressions (both objective and subjective ones), many researchers and companies have built software that can approximate what a person feels about something. Such software can solicit users for opinions using ranking systems (stars, thumbs up/down, like), and then compare their submissions to aggregates of other data, such as ratings from other users, purchase histories, or other forms of collective intelligence. Several companies have implemented recommender systems that have been wildly successful (such as NetFlix and Pandora), while others are in many ways hit-or-miss (Amazon, …). Recommendations are a problem of the future, with glimpses of solutions in the present.

No one has mastered a solution to this problem though, and in many ways it’s still the Wild, Wild West. Several startups are working hard to come up with their own great solutions (Hunch, Directed Edge) and they’ve done quite a fantastic job thus far. There is no single way to solve many of these problems. And these problems interest us quite intimately. They’re at the core thought processes of people in our company (WeLike), and over the next couple of years we will be addressing some of these problems very seriously. Nearly every product we make will have some tie into personal taste and recommendations. I think our approach will be different than what most companies have done so far. But given that we see things differently, we still see plenty of room for competition and strategic partnerships. It’s a large enough problem that no single company will be able to provide a single all-encompassing solution.

In the future, I envision technologies that will help fill the computer-human gap, by helping computers think more abstractly and humans more concretely. Even more likely will be the creation of bridging technology, allowing some computers to stay statistical and numerical (perhaps quantum computer software?) and humans to remain abstract. Bridging technology would function similarly to the role of a corporate translator: helping two foreign companies, who don’t speak the same language or share the same culture, to communicate and end up on the same page in their relationship with each other.

In an effort to further explore the existing technologies and techniques, as well as begin to examine some additional ways to approach these problems, I will write additional articles in this category every month. I am going to specifically focus on how our minds and personalities define us and our tastes, and potential ways to mimmic and interact with them via software products. If this is a topic that interests you, I encourage you to join in to the conversation by submitting comments on each article that is posted or sending an email to me at kevin@welikeinc.com.

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