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There is plenty of research into this, try a search on ACM [http://portal.acm.org/ http://portal.acm.org/] or google scholar for 'trust reputation recommendatio
by dood 19y ago
There is plenty of research into this, try a search on ACM [http://portal.acm.org/ http://portal.acm.org/] or google scholar for 'trust reputation recommendation network'.
Reddit could/should have been using this kind of approach for ages (I don't know if they have or not).
- palish 19y agoAcademic work is hard to translate into a good implementation though. For example, if you trust one user more than another user, that means that you would see totally different karma point values than any other user sees. That sounds really expensive to compute.
- dood 19y agoI imagine there are loads of variations on this theme (i.e. trust networks) that could produce useful recommendations, without the need for unwieldy computation. Sure there'll be a lot of number crunching, its just a matter of choosing the right ones to crunch, and when and how to crunch them. Also, I'm just talking about delivering a good set of recommendations, anything else is gravy.
- palish 19y agoRight, but this sounds like a "Beat your head through a concrete wall" sort of problem. Sure, you might be able to do it.. But.. Why? Is the value add really that big?
- dood 19y agoIt depends on the context you are using it in! For social news (e.g. reddit et al.), this sort of thing might be a great help at providing personal recommendations. Here is a fun recent paper from google, about 'Scalable Online Collaborative Filtering' for their personalised news service [http://www2007.org/paper570.php http://www2007.org/paper570.php], using MapReduce to run Expectation Maximization. via [http://www.datawrangling.com/google-paper-on-parallel-em-algorithm-using-mapreduce.html http://www.datawrangling.com/google-paper-on-parallel-em-alg...]