tensorflow / tensorflow/recommenders
Has anyone implemented retrieval model in ways other than 2 tower model?
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 2k
- Forks
- 300
- PR merge metrics
- No merged PRs in 30d
Description
I have seen most of the people are implementing just the 2 tower architecture for the retrieval model. I wanted to try other architecture. So, if you have implemented or have an idea about ways to do that, please help me.
Also, Like for this implementation, how can we use TensorFlow recommenders?
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 identified. Start by reviewing the TensorFlow Recommenders retrieval-model documentation and examples, then determine whether an alternative architecture has a defined integration path; done would require a concrete, scoped approach with clear implementation and validation criteria.
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
- 20/100