I am talking about the implementations available in Mahout where you can
find similarity between users by analyzing some datamodel and then
recommend items based on that.
If this can solve your problem. I see this implemented in Mahout. And
its very easy to use.
On 27-11-2011 15:47, Nishant Chandra wrote:
Are you talking about CF? Can you please explain a bit?
To be clear, for my use case, temporal sequence is important.
Nishant
On Sun, Nov 27, 2011 at 3:44 PM, Paritosh Ranjan<[email protected]> wrote:
Have you checked out the recommendation algorithms? I think this can be
easily done using them.
Paritosh
On 27-11-2011 15:39, Nishant Chandra wrote:
Use case is related to purchase transactions.
Sample data set:
Customer ID Acquisition time Products
101 30 June 2007 Product 1
101 12 August 2007 Product 3
101 20 December 2008 Product 4
102 10 September 2008 Product 3
102 12 September 2008 Product 5
102 20 January 2009 Product 5.....
Sample rule:
Rule ID Consequent Antecedents Support %
Confidence %
Rule 1 Product 4 Product 1 then Product 3 57.1
75.0
I want to identify rules such as: after acquiring product 1 and then
product 3, customers have an increased likelihood
(75%) of purchasing product 4 next.
Thanks,
Nishant
On Sun, Nov 27, 2011 at 3:27 PM, Paritosh Ranjan<[email protected]>
wrote:
Can you tell something about your use case?
Paritosh
On 27-11-2011 15:14, Nishant Chandra wrote:
Hi,
Is there any implementation for Sequential Pattern Mining in Mahout? I
see there is an implementation of Sequential Pattern Mining but I am
unsure if it can be used for my use case.
Thanks,
Nishant
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