The lack of global TF/IDF has been answered in the past, in the sharded case, by "usually you have similar enough stats that it doesn't matter". This pre-supposes a fairly evenly distributed set of documents.
But if you're talking about federated search across different types of documents, then what would you "rescore" with? How would you even consider scoring docs that are somewhat/ totally different? Think magazine articles an meta-data associated with pictures. What I've usually found is that one can use grouping to show the top N of a variety of results. Or show tabs with different types. Or have the app intelligently combine the different types of documents in a way that "makes sense". But I don't know how you'd just get "the right thing" to happen with some kind of scoring magic. Best Erick On Fri, Aug 16, 2013 at 4:07 PM, Dan Davis <dansm...@gmail.com> wrote: > I've thought about it, and I have no time to really do a meta-search during > evaluation. What I need to do is to create a single core that contains > both of my data sets, and then describe the architecture that would be > required to do blended results, with liberal estimates. > > From the perspective of evaluation, I need to understand whether any of the > solutions to better ranking in the absence of global IDF have been > explored? I suspect that one could retrieve a much larger than N set of > results from a set of shards, re-score in some way that doesn't require > IDF, e.g. storing both results in the same priority queue and *re-scoring* > before *re-ranking*. > > The other way to do this would be to have a custom SearchHandler that works > differently - it performs the query, retries all results deemed relevant by > another engine, adds them to the Lucene index, and then performs the query > again in the standard way. This would be quite slow, but perhaps useful > as a way to evaluate my method. > > I still welcome any suggestions on how such a SearchHandler could be > implemented. >
