You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Describe the bug
I am running several context-aware models using several float-sequence item features. However, the results that I get are for each model always the same, irrespective of the feature loaded.
While inspecting the code of Context Aware recommenders, I noticed that float sequences are always embedded with a number of embeddings of float_seq_field_dim. However, float_seq_field_dim is always 1 for float sequences, as can be seen here and here. This means that every item is assigned the same embedding. This would explain why I get the same result, no matter which item feature I am loading.
Expected behavior
The model performance should vary depending on the float seq feature used.
My guess on the origin of the problem
float sequences are always embedded with a number of embeddings of 1, which means that every item is assigned the same embedding. This would explain why I get the same result, no matter which item feature I am loading.
Are my hypotheses correct? If so, I think I found a bug :)
Best,
Marta
The text was updated successfully, but these errors were encountered:
Hello!
Thank you for this library.
Describe the bug
I am running several context-aware models using several float-sequence item features. However, the results that I get are for each model always the same, irrespective of the feature loaded.
While inspecting the code of Context Aware recommenders, I noticed that float sequences are always embedded with a number of embeddings of
float_seq_field_dim
. However,float_seq_field_dim
is always 1 for float sequences, as can be seen here and here. This means that every item is assigned the same embedding. This would explain why I get the same result, no matter which item feature I am loading.Expected behavior
The model performance should vary depending on the float seq feature used.
My guess on the origin of the problem
float sequences are always embedded with a number of embeddings of 1, which means that every item is assigned the same embedding. This would explain why I get the same result, no matter which item feature I am loading.
Are my hypotheses correct? If so, I think I found a bug :)
Best,
Marta
The text was updated successfully, but these errors were encountered: