machine learning - Recommendations without ratings (Azure ML) -

i'm trying build experiment create recommendations (using movie ratings sample database), without using ratings. consider if user has rated movies, interested other movies have been rated users have rated movies.

i can consider, instance, ratings 1 (exists in database) or 0 (does not exist), in case, how transform initial data reflect this?

i couldn't find kind of examples or tutorials kind of scenario, , don't know how proceed. should transform data before injecting algorithm? and/or there kind of specific algorithm should use?

if you're hoping use matchbox recommender in aml, you're correct need identify user-movie pairs are not present in raw dataset, , add these in rating of zero. (i'll assume have set of real user-movie pairs have rating of one, described above.)

i recommend generating random candidate pairs , confirming absence training data in execute r (or python) script module. don't know names of dataset's features, here pseudocode in r that:

library(dplyr) df <- maml.mapinputport(1)  # input dataset of observed user-movie pairs all_movies <- unique(df[['movie']]) all_users <- unique(df[['user']]) n <- 30  # number of random pairs start  negative_observations <- data.frame(movie = sample(all_movies, n, replace=true),                                     user = sample(all_users, n, replace=true),                                     rating = rep(0, n))           acceptable_negative_observations <- anti_join(unique(negative_observations), df, by=c('movie', 'user')) df <- rbind(df, acceptable_negative_observations) maml.mapoutputport("df"); 

alternatively, try method association rule learning not require add in fake 0 ratings. martin machac has posted nice example of how in r/aml in cortana intelligence gallery.