Hi Mitch, thanks for the answer and the link.
The use case is to provide content based recommendations for a single item no matter where that came from. So, this input (match) item is "the best match", all "more like this" items compare to it, and the ones that are the most alike would have the highest scores. (Meaning also that the most similar are probably not as good as recommendations because they are too similar. But that is a different story.) Again, I don't want to compare the scores of regular search results (e.g. from dismax) with those of mlt. I only want a way to show to the user a kind of relevancy or similarity indicator (for example using a range of 10 stars) that would give a hint on how similar the mlt hit is to the input (match) item. Greetings from Munich ;-) Chantal On Thu, 2010-06-24 at 17:06 +0200, MitchK wrote: > Chantal, > > have a look at > http://lucene.apache.org/java/3_0_1/api/all/org/apache/lucene/search/similar/MoreLikeThis.html > More like this to have a guess what the MLT's score concerns. > > The problem is that you can't compare scores. > The query for the "normal" result-response was maybe something like > "Bill Gates featuring Linus Torvald - The perfect OS song". > The user picks now one of the responsed documents and says he wants "More > like this" - maybe, because the concerned topic was okay, but the content > was not enough or whatever... > But the sent query is totaly different (as you can see in the link) - so > that would be like comparing apples and oranges, since they do not use the > same base. > > What would be the use case? Why is score-normalization needed? > > Kind regards from Germany, > - Mitch
