tensorflow / tensorflow/recommenders
[Question] Examples of Retrieval and Ranking Models in a Single Sequential Model?
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 2k
- Forks
- 300
- PR merge metrics
- No merged PRs in 30d
Description
Using TensorFlow Recommenders, we often employ a standard process:
- Retrieve candidates using a retrieval model.
- "Explode" the retrieval queries and retrieved candidates into query-candidate pairs to be ranked.
- Rank candidates using a ranking model.
The examples I've seen treat the retrieval and ranking models as separate and perform these steps in sequence, but "manually". Is it possible to combine the three steps into a single Keras sequential model with the retrieval model as the first layer, an "exploder" as the second layer, and a ranking model as the final layer?
If it is possible, are there examples of implementing it? In particular, I am not sure how we might implement the "exploder" layer.
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 by reviewing the TensorFlow Recommenders retrieval and ranking examples and the Keras Sequential model entry point. Determine whether a single model can represent retrieval, query-candidate expansion, and ranking, and document either a working example or the limitation. The issue provides no file or test names.
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