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I don't think computers can help anyone find truly interesting things. An algorithm would have to be really complicated to discover the fact that just because

I don't think computers can help anyone find truly interesting things. An algorithm would have to be really complicated to discover the fact that just because so many people are interested in New Moon, or Tiger Woods, that doesn't mean I am. When "relevancy" algorithms are attempted they don't do nearly as good a job at serving niche groups as "community" moderated algorithms such as HN. HN is an algorithm, in which each person's brain is an equation that estimates the "worth" of piece of information. As a result the final product is an extremely complicated result of "equations", as it were, that are good at finding relevant information. I don't expect computers to be good at doing that for another ten years or so.

joeyo 17y ago
I don't think computers can help anyone find truly interesting things. An algorithm would have to be really complicated to discover the fact that just because so many people are interested in New Moon, or Tiger Woods, that doesn't mean I am. I don't see why, in principle, the full Bayesian inference couldn't be done. In other words, automatically compute how interesting the item is based on how the masses liked it in aggregate, how individuals with similar tastes to you liked it, how similar the item is to other items that you have liked, etc.
- loganfrederick 17y ago
  The problem with this approach is feasibility. Some companies like Netflix and Amazon have done some complex recommendation engines, and I still don't think they're processing as much data as an all-encompassing social network data aggregating recommendation engine would. I believe Reddit was at one point going to try and create a news recommendation engine but found it impractical as the number of users grew. The number of permutations for what people in just one user's network like and dislike is staggering.
  - joeyo 17y ago
    Given that it's presently possible to offload data processing to a number of cloud-based computing services, I'm not sure that we should be afraid of the computation cost. If you are willing to make some assumptions about independence (for example) you can do a lot of the computations in parallel. A more subtle issue would be how much the use of social net data would gain Amazon or Netflix over their current recommendation engines. I suspect it would be quite a lot, but that's just a guess.
jgrahamc 17y ago
An algorithm would have to be really complicated to discover the fact that just because so many people are interested in New Moon, or Tiger Woods, that doesn't mean I am. A Naive Bayesian text classifier could easily learn that. If you fed it news stories that you are interested in it would quickly discover that you never click on stories about Tiger Woods. It would then classify incoming news for you. In fact, my old email project POPFile has been adapted to support things like RSS and NNTP filtering with ease.
- NathanKP 17y ago
  That would work to eliminate "noise" info but such algorithms still do a poor job of helping one find new content and subjects that you have never expressed interest in before. I still trust groups of real people to help me in this regard more than I would trust an aggregator.
  - derefr 17y ago
    Theoretically, you could have a (lazily-evaluated) subscription to the entire universe of discourse. It would start out completely hidden, but would gradually become visible in your reader as your filters trim down the stream to a tractable level. Thus, you wouldn't so much be saying "tell me something I don't know," but rather "tell me everything, and leave out the 99.9999% I don't care about."
deleted 17y ago
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joeyo 17y ago

I don't think computers can help anyone find truly interesting things. An algorithm would have to be really complicated to discover the fact that just because so many people are interested in New Moon, or Tiger Woods, that doesn't mean I am. I don't see why, in principle, the full Bayesian inference couldn't be done. In other words, automatically compute how interesting the item is based on how the masses liked it in aggregate, how individuals with similar tastes to you liked it, how similar the item is to other items that you have liked, etc.

loganfrederick 17y ago
The problem with this approach is feasibility. Some companies like Netflix and Amazon have done some complex recommendation engines, and I still don't think they're processing as much data as an all-encompassing social network data aggregating recommendation engine would. I believe Reddit was at one point going to try and create a news recommendation engine but found it impractical as the number of users grew. The number of permutations for what people in just one user's network like and dislike is staggering.
- joeyo 17y ago
  Given that it's presently possible to offload data processing to a number of cloud-based computing services, I'm not sure that we should be afraid of the computation cost. If you are willing to make some assumptions about independence (for example) you can do a lot of the computations in parallel. A more subtle issue would be how much the use of social net data would gain Amazon or Netflix over their current recommendation engines. I suspect it would be quite a lot, but that's just a guess.

jgrahamc 17y ago

An algorithm would have to be really complicated to discover the fact that just because so many people are interested in New Moon, or Tiger Woods, that doesn't mean I am. A Naive Bayesian text classifier could easily learn that. If you fed it news stories that you are interested in it would quickly discover that you never click on stories about Tiger Woods. It would then classify incoming news for you. In fact, my old email project POPFile has been adapted to support things like RSS and NNTP filtering with ease.

NathanKP 17y ago
That would work to eliminate "noise" info but such algorithms still do a poor job of helping one find new content and subjects that you have never expressed interest in before. I still trust groups of real people to help me in this regard more than I would trust an aggregator.
- derefr 17y ago
  Theoretically, you could have a (lazily-evaluated) subscription to the entire universe of discourse. It would start out completely hidden, but would gradually become visible in your reader as your filters trim down the stream to a tractable level. Thus, you wouldn't so much be saying "tell me something I don't know," but rather "tell me everything, and leave out the 99.9999% I don't care about."

deleted 17y ago

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