if finalArtistID is None:
finalArtistID = artistID
return Rating(userID, finalArtistID, count)
trainData = rawUserArtistData.map(lambda x: ratinglookup(x))
tranData.cache()
bArtistAlias = sc.broadcast(artistAlias)
#build model
model = ALS.trainImplicit(trainData, 10, 5)
#test artist
spotcheckingID = 2093760
bArtistByID = sc.braodcast(artistByID)
rawArtistForUser = (trainData
.filter(lambda x: x.user == spotcheckingID)
.map(lambda x: bArtistByID.value.get(x.product))
.collect())
print(rawArtistForUser)
#output recommendations
recommendations = map(lambda x: artistByID.get(x.product),
model.call("recommendProducts", spotcheckingID, 10))
print(recommendations)
