Hi all,
I have table something looks like in DB :
rating table
https://docs.google.com/spreadsheets/d/1PrShX7X70PqnfIQg0Dfv6mIHtX1k7KSZHTBfTPMv_Do/edit?usp=drive_web
Thanks and Regards,
Vinayak B
This was replied to earlier with the details u r looking for, repeating
here again:
See
http://stackoverflow.com/questions/17272296/how-to-use-mahout-streaming-k-means/18090471#18090471
for how to invoke Streaming Kmeans
Also look at examples/bin/cluster-reuters.sh for the Streaming KMeans
You have users, services, and vendors. You should decide what you want to
recommend. Service? Vendor? Service of Vendor?
Assuming the latter combine the services and vendors into a single ID space:
vendor1-service1, vendor1-service2 …
Then decide what method you want to create recs. We are
I would recommend that you look at actions other than ratings as well.
Did a user expand and read 1 review? did they read 3 reviews?
Did they mark a rating as useful?
Did they ask for contact information?
You know your system better than I possibly could, but using other
information in
@Pat and @Ted Thank You so much for the replay. I was looking for the
solution as Pat suggested, here I want to suggest the Vendors to the User
which he not yet used by User taking the history of that User and compare
with other user who have rated the common vendors. If we take the table in
that
How are you using LLR to compute user similarity? It is normally used to
compute item similarity?
Also, what is your scale? how many users? how many items? how many
actions per user?
On Mon, Sep 29, 2014 at 6:24 PM, Parimi Rohit rohit.par...@gmail.com
wrote:
Hi,
I am exploring a
