tensorflow / tensorflow/recommenders
What is the benefit of having two separate class one for user and one for query?
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- Python
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Description
Hello
I see in this tutorial, there are separate classes for user model and query model. The same for the movie model and candidate model. Can you please explain what is the rationale? In other words, why is it important to do that, and in what situations? Why not just one class for the Query (including user info) and one class for the Candidate?
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 with docs/examples/deep_recommenders.ipynb and review the separate user/query and movie/candidate classes described in the issue. Explain the rationale for separating them, identify when combining the information is appropriate, and update the tutorial or its accompanying documentation so the distinction and completion criteria are clear.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, tensorflow
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100