Hi,
This is tricky.
You could build a separate suggester per category/zip code (or,
possibly prefix-code each suggestion with the category/zip code into
one suggester), but likely this will blow up (ie, if the same
suggestion often appears across zip codes / categories). If your
suggestions are already highly orthogonal across category / zip code
then it may not blow up...
Alternatively maybe you could store some info per-suggestion about
which zip code / category it appears in, using upcoming payloads
addition (see LUCENE-4820), and use that to filter each suggestion as
it arrives.
But: have you confirmed this is really a problem in practice? Ie,
typically suggestions have a strong a-priori rank based on eg how
often that query was asked (if suggestions come from your query logs,
like Google) or based on how popular that item is (if your suggestions
come from your content, like Netflix), in which case, if suggestions
are not that orthogonal, the risk of a bad suggestion may be very low?
Maybe we had a misconception of the intended use case of the
AnalyzingSuggester or the auto-suggest feature in general.
Our suggestions should come solely from the index and not from a query
log. I haven't even thought about using a query log as source. I think,
in this case, it would be better to work on the index directly (using a
PrefixTermEnum or so)...
--
--
Nils Knappmeier | Software Engineer
intelligent views gmbh
Julius-Reiber-Str. 17 |64293 Darmstadt
Tel ++49(0)6151 - 5006-228 | Fax ++49(0)6151 - 5006-138
e-mail: n.knappme...@i-views.de | www.i-views.de
Geschäftsführer: Achim Gärtner, Jörg Kleinz, Klaus Reichenberger Die
Gesellschaft ist eingetragen beim Amtsgericht Darmstadt (Sitz der
Gesellschaft) Nr. HRB 7965
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