tensorflow / tensorflow/recommenders
[Question] Difference between ncf and two-tower-model
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 2k
- Forks
- 300
- PR merge metrics
- No merged PRs in 30d
Description
Hi all,
as I delved further into the topic of recommender systems, the question came up of what the difference is between the two-tower model used here "https://www.tensorflow.org/recommenders/examples/basic_retrieval" and neural collaborative filtering (https://arxiv.org/abs/1708.05031).
Many thanks in advance.
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 basic retrieval example and the linked neural collaborative filtering paper. Compare their architectures and training objectives, then provide a clear explanation of the difference and how each approach is used; completion means the question is answered for readers without requiring further project context.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, tensorflow
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100