Prediction Scores not varying its same for all test users

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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

1. Normalizing the visited count of screens -sample weights(tied various ways like subtracting the values from mean,l1,l2)
2. 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

_

```
def format_newuser_input_test(user_feature_map, user_feature_list):
#user_feature_map = user_feature_map
num_features = len(user_feature_list)
normalised_val = 1.0
target_indices = []
for feature in user_feature_list:
try:
target_indices.append(user_feature_map[feature])
except KeyError:
# print("new user feature encountered '{}'".format(feature))
pass
#print("target indices: {}".format(target_indices))
new_user_features = np.zeros(len(user_feature_map.keys()))
for i in target_indices:
new_user_features[i] = normalised_val
new_user_features = sparse.csr_matrix(new_user_features)
return(new_user_features)

new_user_features=format_newuser_input_test(user_feature_map,[f1.value,f2.value,f3.value,f4.value,f5.value,f6.value,f7.value,f8.value])
test_res=model.predict(0, np.arange(n_items),user_features=new_user_features)
print(test_res)
[ 1.4126569 0.74451625 -0.68796086 -0.88912046 -0.20068406 -0.37704834
0.81743217 -1.0723714 -0.80331606 -0.8504304 ]
```

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.

Start with the model.fit and model.predict calls shown in the issue, checking how user_features_csr and the new_user_features matrix are constructed and passed. Compare predictions for the supplied feature lists and verify that the feature mappings contain the intended values. Done means identifying the cause of identical scores and documenting a reproducible correction or confirmed expected behavior.

Written by the indexing model from the issue text.