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I imagine there are loads of variations on this theme (i.e. trust networks) that could produce useful recommendations, without the need for unwieldy computation
by dood 19y ago
I 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...]