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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 s
by 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 agoThe 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 agoGiven 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.