Re: Large scale ranked recommendation

2016-01-07 Thread xenocyon
(following up a rather old thread:) Hi Christopher, I understand how you might use nearest neighbors for item-item recommendations, but how do you use it for top N items per user? Thanks! Apu -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Large-scale

Re: Large scale ranked recommendation

2014-07-18 Thread m3.sharma
that don't work. I will look into annoy. Thanks. -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Large-scale-ranked-recommendation-tp10098p10212.html Sent from the Apache Spark User List mailing list archive at Nabble.com.

Re: Large scale ranked recommendation

2014-07-18 Thread Christopher Johnson
gt; computations I think few GPU nodes will suffice to serve faster >> recommendation after learning model with SPARK. It will be great to have >> builtin GPU support in SPARK for faster computations to leverage GPU >> capability of nodes for performing these flops faster. >&g

Re: Large scale ranked recommendation

2014-07-18 Thread Nick Pentreath
SPARK for faster computations to leverage GPU > capability of nodes for performing these flops faster. > > > > -- > View this message in context: > http://apache-spark-user-list.1001560.n3.nabble.com/Large-scale-ranked-recommendation-tp10098p10183.html > Sent from the Apache Spark User List mailing list archive at Nabble.com. >

Re: Large scale ranked recommendation

2014-07-18 Thread m3.sharma
rming these flops faster. -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Large-scale-ranked-recommendation-tp10098p10183.html Sent from the Apache Spark User List mailing list archive at Nabble.com.

Re: Large scale ranked recommendation

2014-07-18 Thread Xiangrui Meng
n do all this using >> Breeze (e.g. concatenating vectors to make matrices, iterating and whatnot). >> >> Hope that helps >> >> Nick >> >> >>> On Fri, Jul 18, 2014 at 1:17 AM, m3.sharma wrote: >>> Yes, thats what prediction should be do

Re: Large scale ranked recommendation

2014-07-18 Thread Bertrand Dechoux
Hope that helps > > Nick > > > On Fri, Jul 18, 2014 at 1:17 AM, m3.sharma wrote: > >> Yes, thats what prediction should be doing, taking dot products or sigmoid >> function for each user,item pair. For 1 million users and 10 K items data >> there are 10 billion pai

Re: Large scale ranked recommendation

2014-07-18 Thread Nick Pentreath
Yes, thats what prediction should be doing, taking dot products or sigmoid > function for each user,item pair. For 1 million users and 10 K items data > there are 10 billion pairs. > > > > -- > View this message in context: > http://apache-spark-user-list.1001560.n3.na

Re: Large scale ranked recommendation

2014-07-17 Thread m3.sharma
Yes, thats what prediction should be doing, taking dot products or sigmoid function for each user,item pair. For 1 million users and 10 K items data there are 10 billion pairs. -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Large-scale-ranked

Re: Large scale ranked recommendation

2014-07-17 Thread Shuo Xiang
in context: > http://apache-spark-user-list.1001560.n3.nabble.com/Large-scale-ranked-recommendation-tp10098p10103.html > Sent from the Apache Spark User List mailing list archive at Nabble.com. >

Re: Large scale ranked recommendation

2014-07-17 Thread m3.sharma
We are using RegressionModels that comes with *mllib* package in SPARK. -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Large-scale-ranked-recommendation-tp10098p10103.html Sent from the Apache Spark User List mailing list archive at Nabble.com.

Large scale ranked recommendation

2014-07-17 Thread m3.sharma
ross 800 partitions before doing above steps, still it was of no help. I am using about 100 executor , 2 core, each executor with 2gb RAM. Are there any suggestions to make these predictions fast? -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Large-scal