tensorflow / tensorflow/recommenders
How to read the FactorizedTopK metric?
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Description
factorized_top_k/top_1_categorical_accuracy: 0.0025 - factorized_top_k/top_5_categorical_accuracy: 0.0200 - factorized_top_k/top_10_categorical_accuracy: 0.0379 - factorized_top_k/top_50_categorical_accuracy: 0.1261 - factorized_top_k/top_100_categorical_accuracy: 0.1962
How should we read these numbers? What are the numbers to be considered as a good model? What are the numbers for the state-of-art model?
Thank you!
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 from the FactorizedTopK metric named in the report and locate the definitions of its top_1, top_5, top_10, top_50, and top_100 categorical accuracy values. Compare those definitions with the reported metrics and document how to interpret them, including appropriate guidance on model quality and state-of-the-art comparisons.
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