Implicit data is that which you refer to as “effect”. Observing what people do, essentially. And you’re right, this doesn’t really get to intention, or likes and dislikes very well.

Explicit data is the type in which people make their preferences explicit, like a ratings or a review system that is aggregated. Many of the movie recommendation systems do this. This type of data does get to intention, as it explicitly maps what some likes and dislikes. For example, Netflix knows not only what I watch (implicit) but what I like (my ratings).

Taking this distinction further, and agreeing with your observations about implicit data, I think explicit data can help solve the issue you bring up. With both implicit and explicit data, we can get real insight into both the subjective opinions of users as well as the objective observations.

makes reference to recommendation systems - “In an effort to help users “competitively produce” an answer, Sun’s Labs have come up with a lingusitics analysis technology that the company’s researchers affectionately refer to as the “Blurbalyzer” and the example given cited the way Amazon recommends books on its Web site today.”

“If for example, it suggests Holy Blood Holy Grail to buyers of The DaVinci Code and those customers act on that recommendation which in turn helps Holy Blood Holy Grail stay on the list of recommended reading, does that mean you’ll like the book just because you liked The DaVinci Code? It’s hard to say, but Sun thinks there’s a better way based on a linguistic analysis of the reviews that people have written for the The DaVinci Code and other books.”

Like so many things, the more typical one’s request, the more accurate they are likely to be. They reward common behaviour. As such, the further one is from being in the middle of the bell curve, the less effective they are.

And they are a reflection of effect, not cause. So comments like “the intimate knowledge of what we like and don’t like” make me nervous. There is nothing intimate or insightful about the process.

As others have pointed out, recommendation systems will tell you what most people do, most of the time. And most of the time that’s useful. Sometimes there will be patterns in those data that reveal interesting things, but is the story always what it appears to be? As the saying goes… it depends.

Suppose, for instance you go to Amazon and look for the DVDs that correlate with the book “You: On A Diet”. You’ll find that they include The Da Vinci Code, Cars and the second Pirates of the Caribbean movie. Now look for the DVDs that correlate with Barack Obama’s The Audacity of Hope. What you’ll find is that it correlates with The DaVinci Code, Cars, and Pirates of the Carribean. And in fact, if you look for books that correlate with You on a Diet, you’ll find Obama’s book on the first page. Grisham’s The Innocent Man also correlates to the same three DVDs.

So, what’s common to the three DVDs and the 3 books? They are all big time hits at the moment. Being interested in “You on a Diet” probably doesn’t really indicate that you’ll be interested in Obama’s book or the DaVinci code except to the extent that any one of these indicates that you are aware of American popular culture.

The difficulty is that unless you are very careful in how you set up the correlations (and Amazon actually does a pretty good job of this, so their correlations are better than many, and why my examples above are cross-media, within media they do quite well) you end up saying “If you like X, then you’ll like “, solely because a noticible fraction of any random selection of buyers of anything is likely to by the huge hit.

Systems that allow users to tag each item or which have a built-in classification scheme can often avoid this, by providing multi-dimensional correlations. Thus, for instance, Pandora makes some very interesting recommendations.

If recommendation systems don’t take this issue into account, they just push people towards the hits, but if they do, they can really help individuals find the little gems that delight them and bring them back.

JimB.

The people scoring, or recommending, the hotels are Tablet Hotel guests who have stayed at the hotels, not hotel General Managers or concierges.

User trust in the recommendation system is key.

You can never tell if you will like a restaurant by a typical Yahoo search or menu portal, just by seeing what food they serve. Chowhound.com lets people publish details of what makes places special, or a great experience. So you can search your region for a type of food and find out what place other people think have the best fried dumplings or tastiest gnudi for example.

So there is a person on Chowhound.com that raves about this special spicy beef noodle soup and other dishes at a local Vietnamese restaurant. We go and end up enjoying it a lot, but I know for certain that I would have never gone there by looking at the menu or seeing the décor. It was the story. It was the experience. It was the anonymous recommendation from a fellow, ahem, Foodie.

You’re right about Beatles and Radiohead. It could be that this is simply an odd correlation that happens to exist because lots of Radiohead fans use lastfm. But, as more bands we expect to see show up, such as Bob Dylan, Led Zeppelin, etc., the more Radiohead stands out.

Now, as I mentioned in the piece, recommendation systems aren’t always right. And this might be a case where the system is somehow skewed in the wrong direction. But maybe it isn’t, and maybe Radiohead fans do have something in common with Beatles fans. That’s why it’s a recommendation, not a declaration that you have to like them. Just a suggestion.

Also, I’m not exactly sure what your claim is about the Beatles always being popular. What Last.fm is saying, and *all* it is saying, is that for those people who play the Beatles, they also play a lot of Radiohead. This doesn’t have to do with the overall popularity of the bands, but more to do with the frequency with which each are played by the same person.

In the end, the aggregation is about behavior, as you say. And that’s actually the strength of recommendation systems. It only looks at behavior, so isn’t skewed by a person’s biases about what they *think* they like. (social psychology is littered with biases that humans exhibit). There will always be anomolies, but for the most part, past behavior is a fantastic indicator of future behavior.

The system is dependent on the frequency of an act being directly related to a specific intent. People have a tendency to make the leap from “this happens a lot” to “this happening because….”

Look at the the last.fm example. The Beatles are going to come up as being popular in pretty much any tool that measures popular musical acts. Now if last.fm has a tendency to be popular with the demographic where Radiohead has their strongest representation then they will also appear in the top ten list. This does not mean that, in general, people who like Radiohead will also like the Beatles. It means that amongst that sampling there is a coincidence.

Look at the rest of the list. It reads like a shopping list of the standard classic rock acts. Now is it possible that there’s another reason why Radiohead shows up as number two? Occam’s Razor would suggest that the reason Radiohead is number two in that list is -not- that they are the second most popular rock music act in history. Starting with that assumption, I think you need to dig a little deeper to understand the results, rather than taking the assumption that it’s all correct and then making another leap to create a reason for their presence in that list (particularly a reason that assumes that the system does what you’d like it to be doing.)

Recommendation systems do nothing to understand why a person chose one thing over another or what the connection is between two things that they’ve chosen, they simply report on the frequency, in a given situation, of that happening.

As such, recommendation systems are really only good at reliably telling you what the majority of people do most of the time. Sometimes that’s all you need. They will be good at recommending Pearl Jam if you like Nirvana, but they are, by definition, incapable of understanding -why- you like Nirvana and making any kind of intelligent recommendation.

That’s the kind of thing you still need a human being to do.