"FQ_filter were 365 but below in the
debugging part the docfreq used in the payload_score calculation was
3360"
If you are talking about the doc frequency of a term, obviously this is
corpus based ( necessary for the TF /IDF calculations) so it wil not be
affected by the filter queries.
The pay
Hi Alessandro,
"You are talking about weights so I assume you are using a linear Learning
To
Rank model.
Which library are you using to train your model?
Is this library allowing you to limit the summation of the linear weights
and normalise the training set per feature? "
Yes, we're planning
So Prateek :
"You're right it doesn't have to be that accurate to the query time but our
requirement is having a more solid control over our outputs from Solr like
if we have 4 features then we can adjust the weights giving something like
(40,20,20,20) to each feature such that the sum total of fe
Hi Alessandro,
You're right it doesn't have to be that accurate to the query time but our
requirement is having a more solid control over our outputs from Solr like if
we have 4 features then we can adjust the weights giving something like
(40,20,20,20) to each feature such that the sum total o
Hi Preteek,
I would assume you have that feature at training time as well, can't you use
the training set to estabilish the parameters for the normalizer at query
time ?
In the end being a normalization, doesn't have to be that accurate to the
query time state, but it must reflect the relations th
Thanks again Alessandro
I tried with the feature and the Minmax normalizer you told.But then there is a
slight problem with the params in normalization. I don't really know the
range(Min, Max) of values the payload_score outputs and they are different for
different queries.
I even tried lookin
Thanks again Alessandro
I tried with the feature and the Minmax normalizer you told.But then there is a
slight problem with the params in normalization. I don't really know the
range(Min, Max) of values the payload_score outputs and they are different for
different queries.
I even tried lookin
Mmmm, first of all, you know that each Solr feature is calculated per
document right ?
So you want to calculate the payload score for the document you are
re-ranking, based on the query ( your External Feature Information) and
normalize across the different documents?
I would go with this feature
Hi Alessandro,
Thanks for responding.
Let me take a step back and tell you the problem I have been facing with
this.So one of the features in my LTR model is:
{
"store" : "my_feature_store",
"name" : "in_aggregated_terms",
"class" : "org.apache.solr.ltr.feature.SolrFeature",
"params" : { "q" : "
Hi Alessandro,
Thanks for responding.
Let me take a step back and tell you the problem I have been facing with
this.So one of the features in my LTR model is:
{
"store" : "my_feature_store",
"name" : "in_aggregated_terms",
"class" : "org.apache.solr.ltr.feature.SolrFeature",
"params" : { "q" :
Hi Alessandro,
Thanks for responding.
Let me take a step back and tell you the problem I have been facing with
this.So one of the features in my LTR model is:
{
"store" : "my_feature_store",
"name" : "in_aggregated_terms",
"class" : "org.apache.solr.ltr.feature.SolrFeature",
"params" : { "q" :
Hi Alessandro,
Thanks for responding.
Let me take a step back and tell you the problem I have been facing with
this.So one of the features in my LTR model is:
{
"store" : "my_feature_store",
"name" : "in_aggregated_terms",
"class" : "org.apache.solr.ltr.feature.SolrFeature",
"params" : {
Hi Prateek,
with query and FQ Solr is expected to score a document only if that document
is a match of all the FQ results intersected with the query results [1].
Then re-ranking happens, so effectively, only the top K intersected
documents will be re-ranked.
If you are curious about the code, this
Hi all,
I'm new to solr ltr and stuck on this problem for a while.
I wanted to ask why the documents on which the ltr feature score is
calculated doesn't filter out the documents even if we provide the fq
filter in the url like:
&q=juice&rq={!ltr%20model=my_feature_model%20efi.query=$q%
20reRank
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