3 ms·

Has anyone actually done some studies about what the best predictors of interestingness are? We tend to assume that if our friends like something that we'll lik

by samd 16y ago

Has anyone actually done some studies about what the best predictors of interestingness are? We tend to assume that if our friends like something that we'll like something, but is that really the case?

inerte 16y ago
Interesting question! A quick Google search for "predictors of interestingness" shows that several studies were made to see what makes something interesting, and on different areas. For example, what makes a piece of text interesting, what makes a picture interesting, what makes a song interesting, etc... Now, interesting is different than "like", which is different than passion or love, which are all different than studying for school or work, or an obligation. But anyway, a very good question, you gave me inspiration to delve on this topic and come up with lots of different approaches to determine what people will thing it's interesting.
- samd 16y ago
  Interestingness is quite complex, and those studies seem to be about absolute interestingness. I wonder what predictors would determine relative interestingness. For example, given a set of articles that a person found interesting in the past how well could you predict that some new article will be interesting to that person? How well could you predict that given articles their friends found interesting? What about articles that came from a source that produced interesting articles in the past, or articles that were tagged with the same words, and so on. Furthermore, what algorithm would be best? A type of Bayesian filtering is really effective at separating spam from non-spam, would it be effective at filtering interesting from non-interesting?
anthuswilliams 16y ago
That was precisely what I was thinking while reading the article. Facebook already does a bit of multidimensionality, e.g. 'So and so and 12 others like this.' The problem is, being in Utah, a huge portion of the social scene is Mormon. So vast swathes of my social graph jumped on board the LDS fan page, with the result that I get PMs and ads from Mormon.org. A lot. Perhaps I am an outlier here, and everyone else's social profile coincides nicely with their own personal interests. But I doubt it. Freelancers end up friending their clients, employees friend their coworkers, tenants friend their landlords, and we expect all these varied interests to work as predictors for one another? The problem with using our friends' activities as predictors of interest is that Facebook has no way to distinguish between strong and weak friendships[1]. There is a marked difference in my life between Facebook friends and meatspace friends. [1] - Sure, they can determine which of my friends I spend the most time talking with on Facebook. But that says more about our relative Facebook activity than the quality of our friendship.
- dkasper 16y ago
  What about click-through rate? I'm pretty sure Facebook uses this in determining what to show on your newsfeed, and it's a percentage, so it's not based on quantity.
  - rorymarinich 16y ago
    Click-through rate might be slightly more accurate than simply pushing everything, but it's not an ultimate solution because it too makes assumptions about your usage. The one assumes you want everything equally; the other assumes that your desires can be perfectly correlated to your clickthroughs. But that's not true for anybody; what's worse, the reason why it's false is different for everybody. (What if the people who interact with me are more active than the people I actively seek out, and Facebook misinterprets my responses as active interest? What if I'm simply innocently stalking a girl obsessively but actually find her updates bland and individually unworthwhile? One must think of the stalkers, especially if one is hoping to make money on the Internet.) The real issue is that cases like those of anthuswilliams above you are unique and difficult to replicate. That's one of the noted problems with music recommendation services like Pandora too: What matters isn't what users tend towards but why, and why is a question that can't be effectively computed. It's more freeform. I think the real solution would be a site that actively helps a user be responsible for himself. Something that doesn't simply indicate what features are available, but why users might want to use them to sort out their friends. Contrary to what the OP says in his blog post, that is possible today, but it requires responsibility on both the parts of the reader (who must sort his inputs neatly) and the publisher (who must figure out where to publish what). And web sites presently don't feel it's worth their time to help you with that task.
rorrr 16y ago
None of my facebook "friends" ever write anything remotely interesting. It's all the social crap. That's why I barely use facebook. I don't even know why I really use it. Most interesting stuff happens on hacker news and reddit.
m0th87 16y ago
There is the TANGENT algorithm: http://www.cc.gatech.edu/~zha/CSE8801/CF/p657-onuma.pdf http://www.cc.gatech.edu/~zha/CSE8801/CF/p657-onuma.pdf Applied to a social graph, it would (hypothetically) find content that is both recommended by friends and probably new to you.
deleted 16y ago
[deleted]

by samd 16y ago

inerte 16y ago

Interesting question! A quick Google search for "predictors of interestingness" shows that several studies were made to see what makes something interesting, and on different areas. For example, what makes a piece of text interesting, what makes a picture interesting, what makes a song interesting, etc... Now, interesting is different than "like", which is different than passion or love, which are all different than studying for school or work, or an obligation. But anyway, a very good question, you gave me inspiration to delve on this topic and come up with lots of different approaches to determine what people will thing it's interesting.

samd 16y ago
Interestingness is quite complex, and those studies seem to be about absolute interestingness. I wonder what predictors would determine relative interestingness. For example, given a set of articles that a person found interesting in the past how well could you predict that some new article will be interesting to that person? How well could you predict that given articles their friends found interesting? What about articles that came from a source that produced interesting articles in the past, or articles that were tagged with the same words, and so on. Furthermore, what algorithm would be best? A type of Bayesian filtering is really effective at separating spam from non-spam, would it be effective at filtering interesting from non-interesting?

anthuswilliams 16y ago

That was precisely what I was thinking while reading the article. Facebook already does a bit of multidimensionality, e.g. 'So and so and 12 others like this.' The problem is, being in Utah, a huge portion of the social scene is Mormon. So vast swathes of my social graph jumped on board the LDS fan page, with the result that I get PMs and ads from Mormon.org. A lot. Perhaps I am an outlier here, and everyone else's social profile coincides nicely with their own personal interests. But I doubt it. Freelancers end up friending their clients, employees friend their coworkers, tenants friend their landlords, and we expect all these varied interests to work as predictors for one another? The problem with using our friends' activities as predictors of interest is that Facebook has no way to distinguish between strong and weak friendships[1]. There is a marked difference in my life between Facebook friends and meatspace friends. [1] - Sure, they can determine which of my friends I spend the most time talking with on Facebook. But that says more about our relative Facebook activity than the quality of our friendship.

dkasper 16y ago
What about click-through rate? I'm pretty sure Facebook uses this in determining what to show on your newsfeed, and it's a percentage, so it's not based on quantity.
- rorymarinich 16y ago
  Click-through rate might be slightly more accurate than simply pushing everything, but it's not an ultimate solution because it too makes assumptions about your usage. The one assumes you want everything equally; the other assumes that your desires can be perfectly correlated to your clickthroughs. But that's not true for anybody; what's worse, the reason why it's false is different for everybody. (What if the people who interact with me are more active than the people I actively seek out, and Facebook misinterprets my responses as active interest? What if I'm simply innocently stalking a girl obsessively but actually find her updates bland and individually unworthwhile? One must think of the stalkers, especially if one is hoping to make money on the Internet.) The real issue is that cases like those of anthuswilliams above you are unique and difficult to replicate. That's one of the noted problems with music recommendation services like Pandora too: What matters isn't what users tend towards but why, and why is a question that can't be effectively computed. It's more freeform. I think the real solution would be a site that actively helps a user be responsible for himself. Something that doesn't simply indicate what features are available, but why users might want to use them to sort out their friends. Contrary to what the OP says in his blog post, that is possible today, but it requires responsibility on both the parts of the reader (who must sort his inputs neatly) and the publisher (who must figure out where to publish what). And web sites presently don't feel it's worth their time to help you with that task.

rorrr 16y ago

None of my facebook "friends" ever write anything remotely interesting. It's all the social crap. That's why I barely use facebook. I don't even know why I really use it. Most interesting stuff happens on hacker news and reddit.

m0th87 16y ago

There is the TANGENT algorithm: http://www.cc.gatech.edu/~zha/CSE8801/CF/p657-onuma.pdf http://www.cc.gatech.edu/~zha/CSE8801/CF/p657-onuma.pdf Applied to a social graph, it would (hypothetically) find content that is both recommended by friends and probably new to you.

deleted 16y ago

[deleted]