tensorflow / tensorflow/recommenders
Explainability of TFRS-retrieval model
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- Dominant language
- Python
- Stars
- 2k
- Forks
- 300
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Description
We are using TFRS-retrieval solution for our problem statement. Now we are looking for the model explainability part of TFRS-retrieval model. is there any way we can backtrack from the output and find what feature column leads to the predictions?
For Instance, in a movie recommendation scenario, we more often predict cartoon movies to the users whose age are under 10. Here we can see the age of the user which cause the model to predict cartoon movies.
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
No repository file, test, or entry point is named. Start by reviewing how the TFRS retrieval model produces predictions and how user feature columns enter the model; clarify whether the expected result is an explainability API, implementation, or documentation, with the age-feature example as the acceptance case.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, tensorflow
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100