tensorflow / tensorflow/recommenders
How to make recommended items that are totally new without dupliactes to the old user?
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- Dominant language
- Python
- Stars
- 2k
- Forks
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Description
I trained a retrieval model but the model recommended items that were used by the user before. Do you guys have any ways to make sure that the recommended items that are new without duplicates to the users. 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
The issue names no files, tests, or entry points and only describes a trained retrieval model returning previously used items. Start by identifying the retrieval model and how each user's prior items are represented; completion would require an agreed approach and validation that recommendations contain no duplicates, but the issue does not define either.
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