It's shown at http://spark.apache.org/docs/latest/mllib-collaborative-filtering.html
It's really not different to use. It's suitable when you have count-like data rather than rating-like data. That's what you have here. I am not sure what you mean that you want to add frequency too but no the model is strictly (user,item,strength). However you can merge many signals into a 'strength' score. On Thu, Feb 19, 2015 at 4:40 PM, poiuytrez <guilla...@databerries.com> wrote: > Hello, > > I would like to use the spark MLlib recommendation filtering library. My > goal will be to predict what a user would like to buy based on what he > bought before. > > I read on the spark documentation that Spark supports implicit feedback. > However there is not example for this application. Would implicit feedback > works on my business case and how? > > Can ALS accept multiple parameters. Currently I have : > (userId,productId,nbPurchased) > I would like to another parameter: > (userId,productId,nbPurchased,frenquency) > > Is it possible with ALS? > > Thank you for your reply > > > > -- > View this message in context: > http://apache-spark-user-list.1001560.n3.nabble.com/Implicit-ALS-with-multiple-features-tp21723.html > Sent from the Apache Spark User List mailing list archive at Nabble.com. > > --------------------------------------------------------------------- > To unsubscribe, e-mail: user-unsubscr...@spark.apache.org > For additional commands, e-mail: user-h...@spark.apache.org > --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
