tensorflow / tensorflow/recommenders
Approach for Small Number of Items in Catalog
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 2k
- Forks
- 300
- PR merge metrics
- No merged PRs in 30d
Description
Hi, my training data involves implicit feedback from users, specifically their past purchase history (no ratings).
Our item catalog is small, around 20 items. The goal is to rank these items based on the probability of users making a purchase.
Considering the limited item size, I'm thinking of skipping the retrieval model and using a ranking model directly. Is this the right approach?
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 to inspect. It asks for architectural guidance about ranking a small implicit-feedback catalog, so there is no defined implementation target or completion criterion for a first contribution.
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
- 15/100