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From Pat Ferrel <>
Subject Re: User based recommender
Date Mon, 08 Dec 2014 01:19:32 GMT
If there is some “filter” column that flags one type of item or another then yes. Otherwise
you’ll have to preprocess your data for input.

On Dec 7, 2014, at 2:27 PM, Yash Patel <> wrote:

Will cross recommendation still work considering item similarity checks
multiple columns for items and my dataset has only one column for items;it
contains different item ids.

On Sun, Dec 7, 2014 at 5:26 PM, Pat Ferrel <> wrote:

> 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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