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From Pat Ferrel <>
Subject Re: Solr-recommender
Date Wed, 09 Oct 2013 19:08:58 GMT
Solr uses cosine similarity for it's queries. The implementation on github uses Mahout LLR
for calculating the item-item similarity matrix but when you do the more-like-this query at
runtime Solr uses cosine. This can be fixed in Solr, not sure how much work.

It sounds like you are doing item-item similarities for recommendations, not actually calculating
user-history based recs, is that true? 

You bring up a point that we're finding. I'm not so sure we need or want a recommender query
API that is separate from the Solr query API. What we are doing on our demo site is putting
the output of the Solr-recommender where Solr can index it. Our web app framework then allows
very flexible queries against Solr, using simple user history, producing the typical user-history
based recommendations, or mixing/boosting based on metadata or contextual data. If we leave
the recommender query API in Solr we get web app framework integration for free.

Another point is where the data is stored for the running system. If we allow Solr to index
from any storage service that it supports then we also get free integration with most any
web app framework and storage service. For the demo site we put the data in a DB and have
Solr index it from there. We also store the user history and metadata there. This is supported
by most web app frameworks out of the box. You could go a different route and use almost any
storage system/file system/content format since Solr supports a wide variety.

Given a fully flexible Solr standard query and indexing scheme all you need do is tweak the
query or data source a bit and you have an item-set recommender (shopping cart) or a contextual
recommender (for example boost recs from a category) or a pure metadata/content based recommender.

If the query and storage is left to Solr+web app framework then the github version is complete
if not done. Solr still needs LLR in the more-like-this queries. Term weights to encode strength
scores would also be nice and I agree that both of these could use some work.

BTW lest we forget this does not imply the Solr-recommender is better than Myrrix or the Mahout-only
recommenders. There needs to be some careful comparison of results. Michael, did you do offline
or A/B tests during your implementation?

On Oct 9, 2013, at 6:13 AM, Michael Sokolov <> wrote:

Just to add a note of encouragement for the idea of better integration between Mahout and

On, we've recently converted our recommender, which computes similarity scores
w/Mahout, from storing scores and running queries w/Postgres, to doing all that in Solr. 
It's been a big improvement, both in terms of indexing speed, and more importantly, the flexibility
of the queries we can write.  I believe that having scoring built in to the query engine is
a key feature for recommendations.  More and more I am coming to believe that recommendation
should just be considered as another facet of search: as one among many variables the system
may take into account when presenting relevant information to the user.  In our system, we
still clearly separate search from recommendations, and we probably will always do that to
some extent, but I think we will start to blend the queries more so that there will be essentially
a continuum of query options including more or less "user preference" data.

I think what I'm talking about may be a bit different than what Pat is describing (in implementation
terms), since we do LLR calculations off-line in Mahout and then bulk load them into Solr.
 We took one of Ted's earlier suggestions to heart, and simply ignored the actual numeric
scores: we index the top N similar items for each item.  Later we may incorporate numeric
scores in Solr as term weights.  If people are looking for things to do :) I think that would
be a great software contribution that could spur this effort onward since it's difficult to
accomplish right now given the Solr/Lucene indexing interfaces, but is already supported by
the underlying data model and query engine.


On 10/2/13 12:19 PM, Pat Ferrel wrote:
> Excellent. From Ellen's description the first Music use may be an implicit preference
based recommender using synthetic  data? I'm quickly discovering how flexible Solr use is
in many of these cases.
> Here's another use you may have thought of:
> Shopping cart recommenders, as goes the intuition, are best modeled as recommending from
similar item-sets. If you store all shopping carts as your training data (play lists, watch
lists etc.) then as a user adds things to their cart you query for the most similar past carts.
Combine the results intelligently and you'll have an item set recommender. Solr is built to
do this item-set similarity. We tried to do this for a ecom site with pure Mahout but the
similarity calc in real time stymied us. We knew we'd need Solr but couldn't devote the resources
to spin it up.
> On the Con-side Solr has a lot of stuff you have to work around. It also does not have
the ideal similarity measure for many uses (cosine is ok but llr would probably be better).
You don't want stop word filtering, stemming, white space based tokenizing or n-grams. You
would like explicit weighting. A good thing about Solr is how well it integrates with virtually
any doc store independent of the indexing and query. A bit of an oval peg for a round hole.
> It looks like the similarity code is replaceable if not pluggable. Much of the rest could
be trimmed away by config or adherence to conventions I suspect. In the demo site I'm working
on I've had to adopt some slightly hacky conventions that I'll describe some day.
> On Oct 1, 2013, at 10:38 PM, Ted Dunning <> wrote:
> Pat,
> Ellen and some folks in Britain have been working with some data I produced from synthetic
music fans.
> On Tue, Oct 1, 2013 at 2:22 PM, Pat Ferrel <> wrote:
> Hi Ellen,
> On Oct 1, 2013, at 12:38 PM, Ted Dunning <> wrote:
> As requested,
> Pat, meet Ellen.
> Ellen, meet Pat.
> On Tue, Oct 1, 2013 at 8:46 AM, Pat Ferrel <> wrote:
> Tunneling (rat-holing?) into the cross-recommender and Solr+Mahout version.
> Things to note:
> 1) The pure Mahout XRecommenderJob needs a cross-LLR or a cross-similairty job. Currently
there is only cooccurrence for sparsification, which is far from optimal. This might take
the form of a cross RSJ with two DRMs as input. I can't commit to this but would commit to
adding it to the XRecommenderJob.
> 2) output to Solr needs a lot of options implemented and tested. The hand-run test should
be made into some junits. I'm slowly doing this.
> 3) the Solr query API is unimplemented unless someone else is working on that. I'm building
one in a demo site but it looks to me like a static recommender API is not going to be all
that useful and maybe a document describing how to do it with the Solr query interface would
be best, especially for a first step. The reasoning here is that it is so tempting to mix
in metadata to the recommendation query that a static API is not so obvious. For the demo
site the recommender API will be prototyped in a bunch of ways using models and controllers
in Rails. If I'm the one to do the a Java Solr-recommender query API it will be after experimenting
a bit.
> Can someone introduce me to Ellen and Tim?
> On Sep 28, 2013, at 10:59 AM, Ted Dunning <> wrote:
> The one large-ish feature that I think would find general use would be a high performance
classifier trainer.
> Flor cleanup sort of thing it would be good to fully integrate the streaming k-means
into the normal clustering commands while revamping the command line API.
> Dmitriy's recent scala work would help quite a bit before 1.0. Not sure it can make 0.9.
> For recommendations, I think that the demo system that pat started with the elaborations
by Ellen an Tim would be very good to have.
> I would be happy to collaborate with somebody on these but am not at all likely to have
time to actually do them end to end.
> Sent from my iPhone
> On Sep 28, 2013, at 12:40, Grant Ingersoll <> wrote:
>> Moving closer to 1.0, removing cruft, etc.  Do we have any more major features planned
for 1.0?  I think we said during 0.8 that we would try to follow pretty quickly w/ another
>> -Grant
>> On Sep 28, 2013, at 12:33 PM, Ted Dunning <> wrote:
>>> Sounds right in principle but perhaps a bit soon.
>>> What would define the release?
>>> Sent from my iPhone
>>> On Sep 27, 2013, at 7:48, Grant Ingersoll <> wrote:
>>>> Anyone interested in thinking about 0.9 in the early Nov. time frame?
>>>> -Grant
>> --------------------------------------------
>> Grant Ingersoll | @gsingers

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