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From Pat Ferrel <>
Subject Re: User based recommender
Date Sat, 06 Dec 2014 20:07:18 GMT
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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