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From Pat Ferrel <>
Subject Re: User based recommender
Date Sun, 07 Dec 2014 16:26:54 GMT
To use cross-recommendations with multiple actions you may be able to get away with using the
pre-packaged command line job “spark-itemsimilarity". At one point you said you were more
interested in the Mahout Hadoop Mapreduce recommender, which cannot create these cross-recommendations.

I don’t see any need to use the interactive Mahout or Spark shell. Calling Scala from Java
is pretty complex so I’d recommend starting from the running driver so you have a base of
Scala code to start from. Calling Java from Scala is dead simple, it’s done throughout Mahout
code. This should help make Scala a little less daunting. I use IntelliJ and there should
be no problem using Eclipse in the same manner. 

On Dec 6, 2014, at 3:55 PM, Yash Patel <> wrote:

i have something that shows the user locations,however is it possible to
implement this without using apache spark shell as i found it quite
confusing to use without no examples.

I have a windows environment and i am using java in eclipse luna to code
the recommender.
On Dec 6, 2014 9:09 PM, "Pat Ferrel" <> wrote:

> You can often think of or re-phase a piece of data (a column in your
> interaction data) as an action, like “being at a location”. Then use
> cross-cooccurrence to calculate a cross-indicator. So the location can be
> used to recommend purchases.
> If you do this, the location should be something that can have
> cooccurrence, so instead of lat-lon some part of an address. Maybe
> country+postal-code would be good. Something unique that identifies a
> location where other users can be.
> On Dec 5, 2014, at 11:10 AM, Ted Dunning <> wrote:
> Cross recommendation can apply if you use the multiple kinds of columns to
> impute actions relative to characteristics.  That is, people at this
> location buy this item.  Then when you do the actual query, the query
> contains detailed history of the person, but also recent location history.
> On Thu, Dec 4, 2014 at 7:17 AM, Yash Patel <>
> wrote:
>> Cross Recommendors dont seem applicable because this dataset doesn't
>> represent different actions by a user,it just contains transaction
>> history.(ie.customer id,item id,shipping location,sales amount of that
>> item,item category etc)
>> Maybe location,sales per item(similarity might lead to knowledge of
> people
>> who share same purchasing patterns) etc.
>> On Wed, Dec 3, 2014 at 5:28 PM, Ted Dunning <>
> wrote:
>>> On Wed, Dec 3, 2014 at 6:22 AM, Yash Patel <>
>>> wrote:
>>>> I have multiple different columns such as category,shipping
>> location,item
>>>> price,online user, etc.
>>>> How can i use all these different columns and improve recommendation
>>>> quality(ie.calculate more precise similarity between users by use of
>>>> location,item price) ?
>>> For some kinds of information, you can build cross recommenders off of
>> that
>>> other information.  That incorporates this other information in an
>>> item-based system.
>>> Simply hand coding a similarity usually doesn't work well.  The problem
>> is
>>> that you don't really know which factors really represent actionable and
>>> non-redundant user similarity.

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