When it comes to museums, recommendation systems are a natural solution for the problem of the customized tour. How can a museum offer each visitor suggestions for exhibits and experiences that will uniquely serve their interests? There are many lovely example of museums providing quirky tours based on particular interests. For example, The Tate Modern offers a set of pamphlets featuring different tours of the museum based on emotional mood. You can pick up the "I've just split up" tour and wallow in depression, or the "I'm an animal freak" tour and explore your wilder side. And the site I Like Museums lets you find whole institutions of interest based on your preference for trails like "making things," "nice cup of tea," or simply "pigs."
But what if you want to provide a truly emergent recommendation system, like the one used to recommend new songs to you on Pandora or new movies on Netflix? These systems use forms of collaborative filtering to analyze what you've liked and find things that might be similar based on both expert and user data. In this way, you could imagine a visitor moving through the museum, starting by expressing her love of optics, then discovering via an enjoyable exhibit that she also is into magnets, and so on.
There are two problems you have to address to create a great museum recommendation system.
Problem #1: Getting the Data
The first challenge is technical--the lack of explicit data. Recommendation systems use a combination of explicit and implicit information to provide you with suggestions. You make explicit designations by making purchases, expressing preference via ratings or reviews, or choosing some things over others. But you are also always generating implicit data passively via the things you click on, items you spend a long time looking at or listening to, and the choices your friends are making. In the physical space of a museum, visitors make very few explicit data contributions. You may buy a ticket to a special exhibition or show, or actively elect to take up an audio guide or exhibition brochure. But most of your preferences for one museum experience over another go unregistered and untracked. This means there's very little data on which museums can automatically offer recommendations for further experiences.
If we really want the explicit data, there are ways to encourage visitors to provide it. Consider the case of Netflix, the dominant US online movie rental company. Netflix makes movie recommendations based on your ratings of films you've watched. There is no reason in the life cycle of movie rental that a user should be expected to rate a movie. Pre-Netflix, there was never a history of people giving something "four stars" when they dropped it in the return slot. But Netflix realized that their ability to sell subscriptions was directly related to their ability to provide users with a steady stream of good movie recommendations, so they invested heavily in creating a rating system that is fun and easy to use.
Rating content from one to five stars may seem like a frivolous activity, but for Netflix, it's serious business. Netflix knows that good recommendations are key to their bottom line. If Netflix suggests too many movies that you don't like, you will either start ignoring the recommendation system or cancel your subscription altogether. The underlying message of the recommendation system is that there is always a movie you'll love on Netflix, so you should never stop subscribing.
This implicit promise is also the key to why people willingly rate hundreds of movies on Netflix. Netflix promises to give you better recommendations if you rate more movies. Your user profile is functionally an aggregate of the movies you have rated, and the more finely tuned the profile, the more useful the recommendations. The more you use it, the better it gets--and that symbiotic relationship serves customer and vendor alike. This promise is what is missing from so many museum rating systems. When museums allow visitors to rate objects or express preferences, the visitors' expressions are rarely, if ever, fed back into a system that improves the museum experience. The presumption on the part of museums is that rating things is a fun activity onto itself and that's why people use them on Netflix and other sites. But they aren't just fun ways to express yourself. They have direct personal impact. Whether you are panning a movie or gushing over a book, your explicit action is tracked and used to provide you with better subsequent experiences.
Problem #2: Designing the Value System
But what's "better" in the museum context? One of the biggest concerns about deploying recommendation systems in museums is that visitors will only be exposed to the narrow window of things they like and will not have "off path" experiences that are surprising, uncomfortable, and valuable.
Fortunately, not everyone is in the business of selling movie rental subscriptions (or woks, or books, or whatever). While online retail recommendation engines are unsurprisingly optimized to present you with things you will like, there are other ways to filter information based on preference.
For example, Librarything, a social network for sharing books, has a "books you'll hate" feature called the Unsuggester. Type in How Children Fail by John Holt, and you'll find its antithesis: Digital Fortress by Dan Brown. This is an undoubtably silly exercise.
When the BookSuggester was released in November of 2006, programmer Tim Spaulding wrote a blog post about the addition of the Unsuggester. After noting the patterns of opposition between philosophy and chick lit, programming manuals and literature, Tim writes:
"These disconnects sadden me. Of course readers have tastes, and nearly everyone has books they'd never read. But, as serious readers, books make our world. A shared book is a sort of shared space between two people. As far as I'm concerned, the more of these the better. So, in the spirit of unity and understanding, why not enter your favorite book, then read its opposite?"
The Unsuggester is based on different values than Netflix's Movies You'll Love and the BookSuggester. It's also based on different data. Whereas Netflix bases its recommendations on ratings, Librarything bases its recommendations on the books you have in your library (read why here). Instead of saying, "if you like this, you'll like that," Librarything says, "if you have this, you'll like that."
This may sound like a trivial difference, but it leads to a real value shift when it comes to the Unsuggester. The Unsuggester doesn't give you books you'll hate; it gives you books that you'd never otherwise encounter. The format is "if you have read this, you are unlikely to read this." The value system for the Unsuggester is based on the idea that we can learn something from things that are foreign to our experience. The books on the list are the ones that are least likely to be found in your Librarything collection or the collections of other users who also have your books. It's a window into a distant and somewhat unknowable world... not unlike the world of wild and disparate artifacts that curators would like to reveal to visitors.
And users have responded positively. When Tim suggested that few people were likely to actually read books on the Unsuggester list, an anonymous user responded,
"You underestimate Thingamabrarians. Some of us are just looking for new ways to branch out from our old ruts... and something flagged as 'opposite' to our normal reading might just be what we're all looking for. (Besides, a lot of the 'niche' books are throwing up classics in the unowned lists, and many people like to improve their lit-cred.)"
In other words, recommendation systems don't have to be optimized to give you something you like. They just have to be responsive to your personal inputs in some understandable and meaningful way.
The Unsuggester is based on the value of finding enjoyment in highly incongruous things. What other values might we want to base recommendation systems on, in museums or otherwise?