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Hi. Sorry for the late response. You are right. In the current implementation the Candidate Sampling generation and the features retrieval for negative samples occurs within the train / eval loop. To be able to provide predictions (e.g. deploying this model in production), it would be necessary to move the candidate sampling outside the train/eval loop, and the model would receive the features for both positive and negative samples as input, and would output the predicted scores for each of the negative samples.
I have been drafted that implementation, and was able to deploy the CHAMELEON on TF Serving. But need to clean that code before pushing to this repo, as it required a major refactory.
The code shows how to train and evaluate model ,but there seems is not enough information to predict with this model.
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