Hi Alessandro,
Exactly. The response time varies but let's have a concrete other example.
This is my call: http://localhost:8983/solr/bandwpl/mlt?q=id:10812=id
This is my result:
{
"responseHeader":{
"status":0,
"QTime":6232},
"response":{"numFound":4564,"start":0,"docs":[
{
Hi Alessandro,
You are right. I forget to mention one important factor. For 3000 hungarian
e-books the approach you mentioned is absolutely fine as the response time
is some 0.7 sec. But when I use the same mlt for 5600 polish e-books the
response time is 7 sec which is definetely not acceptable
I am still missing why you quote the number of the documents...
If you have 5600 polish books, but you use the MLT only when you land in
the page of a specific book ...
I think i still miss the point !
MLT on 1 polish book, takes 7 secs ?
2015-09-30 9:10 GMT+01:00 Szűcs Roland
Hello Upayavira,
We use the ajax call and it can work when it takes only some seconds (even
the 7 sec can be acceptable in this case) as the customers first focus on
the product page and if they are not satisfied with the e-book they will
need the offer. I am just started to scare what will
Could you do the MLT as a separate (AJAX) request? They appear a little
afterwards, whilst the user is already reading the page?
Or, you could do offline clustering, in which case, overnight, you
compare every document with every other, using a (likely non-solr)
clustering algorithm, and store
This query time is still suspicious ...
Have you tried to play with MLT params ?
Min term frequency ? Min Doc Freq ?
You can reduce the terms to query,
Parameter
Description
mlt.qf
Query fields and their boosts using the same format as that used by the
DisMaxRequestHandler. These fields must
Let's take a step back. So, you have 3000 or so docs, and you want to
know which documents are similar to these.
Why do you want to know this? What feature do you need to build that
will use that information? Knowing this may help us to arrive at the
right technology for you.
For example, you
Hi Alessandro,
My original goal was to get offline suggestsion on content based similarity
for every e-book we have . We wanted to run a bulk more like this
calculation in the evening when the usage of our site is low and we submit
a new e-book. Real time more like this can take a while as we
If MoreLikeThis is slow for large documents that are indexed, have you
enabled term vectors on the similarity fields?
Basically, what more like this does is this:
* decide on what terms in the source doc are "interesting", and pick the
25 most interesting ones
* build and execute a boolean query
Hi Roland,
what is your exact requirement ?
Do you want to basically build a "description" for a set of documents and
then find documents in the index, similar to this description ?
By default , based on my experience ( and on the code) this is the entry
point for the Lucene More Like This :
>
Hello Upayavira,
Thanks dealing with my issue. I have applied already the termVectors=true
to all fileds involved in the more like this calculation. I have just 3 000
documents each of them is represented by a relativly big term vector with
more than 20 000 unique terms. If I run the more like
Hi all,
Is it possible to feed multiple solr id for a MoreLikeThisHandler?
false
details
title,content
4
title^12 content^1
2
10
true
json
true
when I call this: http://localhost:8983/solr/bandwhu/mlt?q=id:8=id
it works fine. Is there any way to have a kind of "bulk" call of more like
Hi Roland,
you said "The main goal is that when a customer is on the pruduct page ".
But if you are in a product page, I guess you have the product Id.
If you have the product id , you can simply execute the MLT request with
the single Doc Id in input.
Why do you need to calculate beforehand?
Hello Upayavira,
The main goal is that when a customer is on the pruduct page on an e-book
and he does not like it somehow I want to immediately offer her/him
alternative e-books in the same topic. If I expect from the customer to
click on a button like "similar e-books" I lose half of them as
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