tensorflow / tensorflow/recommenders
candidates argument for FactorizedTopK
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Description
Hi,
metrics = tfrs.metrics.FactorizedTopK(
candidates=movies.batch(128).map(movie_model)
)
I'm trying to figure out how 'candidates' argument works for FactorizedTopK metric from the retrieval tutorial.
The tutorial uses 'movies' dataset, and I found the dataset includes some duplicates.
I tested using an array of unique movies for that argument and I got different accuracy compared to using 'movies' dataset.
Can anyone help me to understand how the candidates are used to calculate accuracy and how I should create this from the dataset I have (order of items and batch size)?
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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.
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Research direction
Start with the retrieval tutorial's FactorizedTopK example and the candidates argument in the metric's documentation or implementation. Compare the duplicate-containing movies dataset with the unique array, then document how candidate order, batching, and duplicates affect accuracy and how users should prepare their dataset.
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
- 35/100