tensorflow / tensorflow/recommenders
[Question] How to create embeddings from one-hot-encoded features?
Open
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 2k
- Forks
- 300
- PR merge metrics
- No merged PRs in 30d
Description
Hello!
In my model I have user_id , user_gender, user_age (for query) and movie_id (for candidate) features.
- How to deal with situations where I have a feature with only few options like
user_gender(M, F, -)? What would be the best way to create an embedding for It? - Creating a StringLookup with a 3 item vocab and then an embedding with dimension 32 would be to much?
- How to insert one-hot-encoding this feature when creating the embedding?
Thanks!
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 file, test, or entry point is named; start by clarifying whether this is a documentation request or an implementation change and which example or model is in scope. The issue does not define a concrete completion criterion yet.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, tensorflow
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100