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I'm using lightfm recommendation for usecase where i have to predict user interested app screens for an android app.For this i'm using implicit data - how many times user visited particular screen (count based).There are total 10 screens/items which we need recommend.
I have created train data as first appeared records in intial months and test data has only last 2 weeks records .
My problem is for all users in the test data its predicting same screens/items and even same scores for all the items.
Assuming that model is under fitting; I tried few things
Normalizing the visited count of screens -sample weights(tied various ways like subtracting the values from mean,l1,l2)
Increased user features
dataset1 = Dataset()
dataset1.fit((interactions['user_id'].values),
(interactions['firebase_screen'].values),
user_features=uf)
# plugging in the interactions and their weights
(interactions_csr, weights) = dataset1.build_interactions([(x[0], x[1], x[2]) for x in interactions.values ])
user_id_map, user_feature_map, item_id_map, item_feature_map = dataset1.mapping()
model = LightFM(loss='warp',
random_state=2016,
learning_rate=0.08,
no_components=200,
user_alpha=0.0000000001)
model.fit(interactions_csr, # spase matrix representing whether user u and item i interacted
user_features= user_features_csr, # we have built the sparse matrix above
sample_weight= weights, # spase matrix representing how much value to give to user u and item i inetraction: i.e ratings
epochs=20,num_threads=2)
precision = precision_at_k(model,interactions_csr,user_features=user_features_csr,k=5).mean()
recall = recall_at_k(model,interactions_csr,user_features=user_features_csr,k=5).mean()
print('Precision at k:',precision)
print('recall at k:',recall)
Precision at k: 0.6130435
recall at k: 0.9521348394430235
_
My total users - 1262
train data users - 976
test data users - 558
no of users who are only in test but not train - 203 (cold start) but we have user features
test_res are same even if i give different features for other users
What are the reasons for same scores what can i do to overcome it.
The text was updated successfully, but these errors were encountered:
Harika-3196
changed the title
Prediction Scores not varying its same for all users
Prediction Scores not varying its same for all test users
Mar 15, 2023
Hi All,
I'm using lightfm recommendation for usecase where i have to predict user interested app screens for an android app.For this i'm using implicit data - how many times user visited particular screen (count based).There are total 10 screens/items which we need recommend.
I have created train data as first appeared records in intial months and test data has only last 2 weeks records .
My problem is for all users in the test data its predicting same screens/items and even same scores for all the items.
Assuming that model is under fitting; I tried few things
Precision at k: 0.6130435
recall at k: 0.9521348394430235
_
_
test_res are same even if i give different features for other users
What are the reasons for same scores what can i do to overcome it.
The text was updated successfully, but these errors were encountered: