Thank you for providing detailed code. I have your code running well and can make a topn prediction for any existing users. But, i'm stuck trying to get a prediction set for a new user. I believe it should be possible to do without retraining the whole system? Ie I want a prediction in less then 100ms timeline. I thought with the embeddings it could use those to take a new user with their item ratings and make predictions about their missing ratings. Is that possible with NeuMF or NGCF?
I added this method to batch_test for creating a set of n recommendations for an existing user. But I'm not sure how you'd change that around to handle a new user with their set of item ratings.
def recommend(sess, model, user, num,drop_flag=False):
user_batch = [user]
item_batch = range(ITEM_NUM)
if drop_flag == False:
rate_batch = sess.run(model.batch_ratings, {model.users: user_batch,
model.pos_items: item_batch})
else:
rate_batch = sess.run(model.batch_ratings, {model.users: user_batch,
model.pos_items: item_batch,
model.node_dropout: [0.] * len(eval(args.layer_size)),
model.mess_dropout: [0.] * len(eval(args.layer_size))})
rating = rate_batch
try:
training_items = data_generator.train_items[user]
except Exception:
print("**** Failed to lookup user ***")
training_items = []
cols = range(ITEM_NUM)
df = pd.DataFrame(data=rating,columns=cols,index=[0])
df.sort_values(by=[0],inplace=True,axis=1,ascending=False)
icols = list(df)
cnt = 0
items = []
ratings = []
for i in icols:
if i in training_items:
#print("Removing rated: %s at %6.2f" % (data_generator.item_db[i],df[i][0]))
continue
items.append(i)
ratings.append(df[i][0])
cnt = cnt + 1
if cnt > num: break
return items, ratings
Thank you for providing detailed code. I have your code running well and can make a topn prediction for any existing users. But, i'm stuck trying to get a prediction set for a new user. I believe it should be possible to do without retraining the whole system? Ie I want a prediction in less then 100ms timeline. I thought with the embeddings it could use those to take a new user with their item ratings and make predictions about their missing ratings. Is that possible with NeuMF or NGCF?
I added this method to batch_test for creating a set of n recommendations for an existing user. But I'm not sure how you'd change that around to handle a new user with their set of item ratings.