I am implementing recommendation techniques in Mahout. However, I have a
requirement for a custom evaluation metrics other than predefined or
built-in ones. So,
Q: Can someone please point me to sample custom Evaluator or metric
implementation in Mahout?
Thanks
Vikas
Hi,
New to mahout and fp growth. I havefollowed this
example:https://chimpler.wordpress.com/2013/05/02/finding-association-rules-with-mahout-frequent-pattern-mining/
I generated nice output informationlike this (as an example):
[abc,def,ghi] => klm,confidence:0.597, support:0.01, lift: 57.415, con
Hi Jeff, as I recall the map-reduce-based fp-growth solution was
problematic, and it's been either deprecated or removed.
There are better solutions under the "recommendations" tab at
http://mahout.apache.org
And I would encourage your updating your version of Mahout to 0.9 or to the
master branc
If you check out the latest master branch from
https://github.com/apache/mahout you'll find classes like this in the
map-reduce legacy
package:
/home/akm/mahout/mrlegacy/src/main/java/org/apache/mahout/common/distance/CosineDistanceMeasure.java
I'm not sure if/where new ones are being written..
Evaluation metric? You mean like the old recommender evaluator? I’d use MAP
mean average precision, but none are implemented in the new Spark recommender
code.
On Mar 2, 2015, at 3:12 PM, Vikas Kumar wrote:
I am implementing recommendation techniques in Mahout. However, I have a
requirement f
Sorry for the confusion. Yes, I meant the recommender evaluation metric
such as RMSE, Precision, Recall etc which are inbuilt. But, I am planning
(or reusing - let me know if already done) to write the metrics such as
nDCG, Popularity, Avg. Rating, diversity etc.
Thanks
Vikas
On Mon, Mar 2, 2015
Jeff, are you trying to build a general recommender? Or a shopping cart
recommender? FP was used to find things often bought together, which means
recommendations based on some partial group of items (watchlist, wishlist,
shopping cart). FPG has been deprecated in favor of newer methods.
There
MAP is what I use, not precision. Mean average precision accounts for ranking
better and ranking is usually what you want to optimize. As I said none are in
the current iteration of the recommender code including RMSE, etc.
http://mahout.apache.org/users/recommender/intro-cooccurrence-spark.html
Hi Pat,
Thanks for the spark-itemsimilarity. I just ran it against my csv file which
looks like below. My two actions are Liked and AddToCart. AddToCart is the main
action.
usr000d3ca6655-c132-11e4-ac0a-0cc47a03334d Liked
prdb5bc44b-fca8-4462-ba19-b9b2b823beb6
usr000e615
