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From Pat Ferrel <pat.fer...@gmail.com>
Subject Re: Solr-recommender for Mahout 0.9
Date Fri, 08 Nov 2013 21:05:57 GMT
not including Solr in the project so it should work with any recent version. I’m actually
using 4.2

On Nov 8, 2013, at 9:24 AM, Suneel Marthi <suneel_marthi@yahoo.com> wrote:

Pat,

Would all of this be on Solr/Lucene 4.5.1?

Mahout's presently at Lucene 4.3.1 (as of 0.8) , but we should be moving to the latest stable
release at time of 0.9 release.







On Thursday, November 7, 2013 10:33 PM, Pat Ferrel <pat.ferrel@gmail.com> wrote:

Another approach would be to weight the terms in the docs by there Mahout similarity strength.
But that will be for another day. 

My current question is whether Lucene looks at word proximity. I see the query syntax supports
proximity but I don’t see that it is default so that’s good.



On Nov 7, 2013, at 12:41 PM, Dyer, James <James.Dyer@ingramcontent.com> wrote:

Best to my knowledge, Lucene does not care about the position of a keyword within a document.

You could bucket the ids into several fields.  Then use a dismax query to boost the top-tier
ids more than then second, etc.

A more fine-grained approach would probably involve a custom Similarity class that scales
the score based on its position in the document.  If we did this, it might be simpler to index
as 1 single-valued field so each id was position+1 rather than position+100, etc.

James Dyer
Ingram Content Group
(615) 213-4311


-----Original Message-----
From: Pat Ferrel [mailto:pat.ferrel@gmail.com] 
Sent: Thursday, November 07, 2013 1:46 PM
To: user@mahout.apache.org
Subject: Re: Solr-recommender for Mahout 0.9

Interesting to think about ordering and adjacentness. The index ids are sorted by Mahout strength
so the first id is the most similar to the row key and so forth. But the query is ordered
buy recency. In both cases the first id is in some sense the most important. Does Solr/Lucene
care about closeness to the top of doc for queries or indexed docs? I don't recall any mention
of this.

However adjacentness has no meaning in recommendations though I think it's used in default
queries so I may have to account for that.

The object returned is an ordered list of ids. I use only the IDs now but there are cases
when the contents are also of interest; shopping cart/watchlist queries for example.

On Nov 7, 2013, at 10:00 AM, Dyer, James <James.Dyer@ingramcontent.com> wrote:

The multivalued field will obey the "positionIncrementGap" value you specify (default=100).
 So for querying purposes, those id's will be 100 (or whatever you specified) positions apart.
 So a phrase search for adjacent ids would not match, unless you set the slop for >= positionIncrementGap.
 Other than this, both scenarios index the same.

For stored fields, solr returns an array of values for multivalued fields, which is convienent
when writing a UI.

James Dyer
Ingram Content Group
(615) 213-4311


-----Original Message-----
From: Dominik Hübner [mailto:contact@dhuebner.com] 
Sent: Thursday, November 07, 2013 11:23 AM
To: user@mahout.apache.org
Subject: Re: Solr-recommender for Mahout 0.9

Does anyone know what the difference is between keeping the ids in a space delimited string
and indexing a multivalued field of ids? I recently tried the latter since ... it felt right,
however I am not sure which of both has which advantages.

On 07 Nov 2013, at 18:18, Pat Ferrel <pat.ferrel@gmail.com> wrote:

> I have dismax (no edismax) but am not using it yet, using the default query, which does
use 'AND'. I had much the same though as I slept on it. Changing to OR is now working much
much better. So obvious it almost bit me, not good in this case...
> 
> With only a trivially small amount of testing I'd say we have a new recommender on the
block.
> 
> If anyone would like to help eyeball test the thing let me know off-list. There are a
few instructions I'll need to give. And it can't handle much load right now due to intentional
design limits.
> 
> 
> On Nov 7, 2013, at 6:11 AM, Dyer, James <James.Dyer@ingramcontent.com> wrote:
> 
> Pat,
> 
> Can you give us the query it generates when you enter "vampire werewolf zombie", q/qt/defType
?
> 
> My guess is you're using the default query parser with "q.op=AND" , or, you're using
dismax/edismax with a high "mm" (min-must-match) value.
> 
> James Dyer
> Ingram Content Group
> (615) 213-4311
> 
> 
> -----Original Message-----
> From: Pat Ferrel [mailto:pat.ferrel@gmail.com] 
> Sent: Wednesday, November 06, 2013 5:53 PM
> To: ssc@apache.org Schelter; user@mahout.apache.org
> Subject: Re: Solr-recommender for Mahout 0.9
> 
> Done,
> 
> BTW I have the thing running on a demo site but am getting very poor results that I think
are related to the Solr setup. I'd appreciate any ideas.
> 
> The sample data has 27,000 items and something like 4000 users. The preference data is
fairly dense since the users are professional reviewers and the items videos.
> 
> 1) The number of item-item similarities that are kept is 100. Is this a good starting
point? Ted, do you recall how many you used before?
> 2) The query is a simple text query made of space delimited video id strings. These are
the same ids as are stored in the item-item similarity docs that Solr indexes.
> 
> Hit thumbs up on one video you you get several recommendations. Hit thumbs up on several
videos you get no recs. I'm either using the wrong query type or have it set up to be too
restrictive. As I read through the docs if someone has a suggestion or pointer I'd appreciate
it. 
> 
> BTW the same sort of thing happens with Title search. Search for "vampire werewolf zombie"
you get no results, search for "zombie" you get several.
> 
> On Nov 6, 2013, at 2:18 PM, Sebastian Schelter <ssc@apache.org> wrote:
> 
> Hi Pat,
> 
> can you create issues for 1) and 2) ? Then I will try to get this into
> trunk asap.
> 
> Best,
> Sebastian
> 
> On 06.11.2013 19:13, Pat Ferrel wrote:
>> Trying to integrate the Solr-recoemmender with the latest Mahout snapshot. The project
uses a modified RecommenderJob because it needs SequenceFile output and to get the location
of the preparePreferenceMatrix directory. If #1 and #2 are addressed I can remove the modified
Mahout code from the project and rely on the default implementations in Mahout 0.9. #3 is
a longer term issue related to the creation of a CrossRowSimilarityJob. 
>> 
>> I have dropped the modified code from the Solr-recommender project and have a modified
build of the current Mahout 0.9 snapshot. If the following changes are made to Mahout I can
test and release a Mahout 0.9 version of the Solr-recommender.
>> 
>> 1. Option to change RecommenderJob output format
>> 
>> Can someone add an option to output a SequenceFile. I modified the code to do the
following, note the SequenceFileOutputFormat.class as the last parameter but this should really
be determined with an option I think.
>> 
>>   Job aggregateAndRecommend = prepareJob(
>>           new Path(aggregateAndRecommendInput), outputPath, SequenceFileInputFormat.class,
>>           PartialMultiplyMapper.class, VarLongWritable.class, PrefAndSimilarityColumnWritable.class,
>>           AggregateAndRecommendReducer.class, VarLongWritable.class, RecommendedItemsWritable.class,
>>           SequenceFileOutputFormat.class);
>> 
>> 2. Visibility of preparePreferenceMatrix directory location
>> 
>> The Solr-recommender needs to find where the RecommenderJob is putting it's output.

>> 
>> Mahout 0.8 RecommenderJob code was:
>> public static final String DEFAULT_PREPARE_DIR = "preparePreferenceMatrix";
>> 
>> Mahout 0.9 RecommenderJob code just puts "preparePreferenceMatrix" inline in the
code:
>> Path prepPath = getTempPath("preparePreferenceMatrix");
>> 
>> This change to Mahout 0.9 works:
>> public static final String DEFAULT_PREPARE_DIR = "preparePreferenceMatrix";
>> and
>> Path prepPath = getTempPath(DEFAULT_PREPARE_DIR);
>> 
>> You could also make this a getter method on the RecommenderJob Class instead of using
a public constant.
>> 
>> 3. Downsampling
>> 
>> The downsampling for maximum prefs per user has been moved from PreparePreferenceMatrixJob
to RowSimilarityJob. The XRecommenderJob uses matrix math instead of RSJ so it will no longer
support downsampling until there is a hypothetical CrossRowSimilairtyJob with downsampling
in it.
>> 
>> 
> 
> 
> 
> 


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