tensorflow / tensorflow/recommenders
recommendation results not making sense
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- Python
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Description
`model = tf.saved_model.load(model_path)
request = {"userid":user_id}
scores,rec_item_ids = model(request)
`
here rec_item_ids are all same and their scores as well .
any help why this is happening
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start from the shown tf.saved_model.load(model_path) call and the subsequent model(request) invocation, reproducing the identical rec_item_ids and scores if the saved model is available. Inspect the saved model inputs and outputs, including the userid request, to determine what information is missing from the report. Done means identifying the cause of the repeated recommendations or documenting the additional model details needed to diagnose it.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, tensorflow
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100