tensorflow / tensorflow/recommenders
TFRS Retrieval Model with softmax for converting score of each items into probabilities
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- Dominant language
- Python
- Stars
- 2k
- Forks
- 300
- PR merge metrics
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Description
We are using TFRS as the recommendation solution for our usecase. Instead of returning the score associated with each item we passed the scores into the softmax and returning probability score of each item. Is there any another way to figure out the relative probability of items?
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 TFRS retrieval-model question and the linked TFRS_with_softmax image to identify the scoring behavior being requested. Check the retrieval-model documentation and relevant examples for supported probability or relative-score outputs; the issue is complete only when the supported approach and its limitations are documented.
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