No. You seem to be describing combined vendor+service recommendations. So you
will be creating input of
user ID, combined ID, rating
The way you are creating a combined ID is fine but it must still be mapped to a
Mahout ID. The user and combined IDs must _each_ be mapped to 0-N. Think of
First I agree with Ted that LLR is better. I've tried all of the similarity
methods in Mahout on exactly the same dataset and got far higher
cross-validation scores for LLR. You may still use pearson with Mahout 0.9 and
1.0 but it is not supported in the Mahout 1.0 Spark jobs.
If you have
Hi Pat,
If I am wrong plz correct me, if we take table 2 (user2) then he rated for
vendor 1 - vendor 3,
1. I am going assign for each user an ID starting from 1 - N.
2. Vendors will have the ID with 601,602,603
3. Services will have the ID with 501,502,503.
4. If I translate
Thank you @Ted, but my guide is suggesting to go with what Pat is
suggesting. @Pat could you plz tell, if I want to recommend vendors to the
user from the table how they should be grouped and you mentioned *your
recs will be returned using the same integer IDs so you will have to
translate your
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
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
