Hello, I use EasyRec DSSM model config for matching model and split the saved model by https://github.com/alibaba/EasyRec/blob/master/easy_rec/python/tools/split_model_pai.py after train_eval and export. However, the saved user tower model requires product_id which is the feature of the item tower. I tried some combinations of features for the user tower:
No product_id_seq feature = the saved user tower model doesn't require product_id as expected. Normal values for recall_at_k metrics
With product_id_seq feature = the saved user tower model requires product_id in the inference and ~0.99 values for recall_at_k
Question: Using the sequence feature, could the user tower have access to the target item and learn to predict itself?
How can I use the history sequence feature and split the user and item tower successfully?
Notes: I'm using tf1.12 to be able to use split_model_pai.py
Hello, I use EasyRec DSSM model config for matching model and split the saved model by https://github.com/alibaba/EasyRec/blob/master/easy_rec/python/tools/split_model_pai.py after train_eval and export. However, the saved user tower model requires product_id which is the feature of the item tower. I tried some combinations of features for the user tower:
product_id_seq
feature = the saved user tower model doesn't requireproduct_id
as expected. Normal values for recall_at_k metricsproduct_id_seq
feature = the saved user tower model requiresproduct_id
in the inference and ~0.99 values for recall_at_kQuestion: Using the sequence feature, could the user tower have access to the target item and learn to predict itself?
How can I use the history sequence feature and split the user and item tower successfully?
Notes: I'm using tf1.12 to be able to use split_model_pai.py
Config: