tensorflow / tensorflow/recommenders
lack of learning resources and examples for tf.recommenders
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 2k
- Forks
- 300
- PR merge metrics
- No merged PRs in 30d
Description
hi guys.
I'm currently using TensorFlow recommender for building a product recommender system. but, the only recourses that I have found was TensorFlow official examples, but some aspect of these examples is ambiguous for me. for example, how could we stick the ranking model to the retrieval model and build an end-to-end recommender, or how can we create a deep multitask model or how can we make predictions with a multitask model.
can anybody help me or introduce me to useful resources.
thank you so much.
best. Ramin.
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 with the TensorFlow Recommenders official examples and compare them with the requested workflows: connecting ranking and retrieval models, building an end-to-end recommender, creating a deep multitask model, and making multitask predictions. Clarify the documentation scope first; the work is done when these workflows have accessible resources or examples.
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