Recommendation Algorithm Wants To Show You Something New

Several sources are reporting on a new metric that computer scientists are going after with respect to recommender systems — recommendation diversity. "In a paper that will be released by PNAS, a group of scientists are pushing the limits of recommendation systems, creating new algorithms that will make more tangential recommendations to users, which can help expand their interests, which will increase the longevity and utility of the recommendation system itself. Accuracy has long been the most prized measurement in recommending content, like movies, links, or music. However, computer scientists note that this type of system can narrow the field of interest for each user the more it is used. Improved accuracy can result in a strong filtering based on a user's interests, until the system can only recommend a small subset of all the content it has to offer."

Comments

That's called an "contextual ad engine".

[Animats]

creating new algorithms that will make more tangential recommendations to users, which can help expand their interests,

The advertising industry already has that technology. Their idea of "expand interests" usually involves shopping, of course.

Re:That's called an "contextual ad engine".

[Kjella]

I really think someone should add up all the hours required to experience every movie/game released in a year and compare it to the the average persons free time, a lot of stuff is over-produced and it would be good if someone was out there modelling how many products you could possibly want to experience over a yearly period.

But that makes good recommendations more important, not less. If you go into a library it's highly unlikely you'll be able to read every book in it, but does that matter? You just want to read the good books about things that interest you. If Spotify was on full shuffle and you could get everything from death metal to yodeling in the next song, you wouldn't want it - you'd go back to your own favorites. On the other hand, if everything is interpolated you only get more and more of the same. People don't work like that, you may have your favorite food but it's not something you want to perfect and have every day.

A good search helper should be something in between - keeping to things you're reasonably likely to like but on the other hand challenge you a little to explore and listen to things a little outside your normal repertoire. Yes of course I realize the marketing potential here in sending the masses to their new hit wonder but I don't think the concept is that unreasonable. Think about how your friends are influencing your music taste, they're not interpolating they're gently pulling in the direction they like. If they hit the right mix this would be a real asset because you go to that site because of the good recommendations and that's not such an easy thing to copy.

Tricky Business

[miasmic]

Every recommendation algorithm I've seen does one or both of two things. The first being staying extremely close to things I have already expressed an interest in - never broadening my horizons.

That, or it suggests really popular things, for example with music always getting a string of well known, popular bands and artists like Radiohead or Pink Floyd suggested as bands I might like - because many people who like similar sorts of music to me like Radiohead, the algorithm thinks I would like Radiohead too - they can't seem to figure that I would already know if I liked Radiohead or not at this point. I've never found a way to tell a recommendation algorithm that Pink Floyd is OK but I want something less popular...