Tensorflow-recommenders

Added

- `TopK` layers now come with a `query_with_exclusions` method, allowing
certain candidates to be excluded from top-k retrieval.
- `TPUEmbedding` Keras layer for accelerating embedding lookups for large
tables with TPU.

Changed

- `factorized_top_k.Streaming` layer now accepts a query model, like other
`factorized_top_k` layers.

- Updated ScaNN to 1.2.0, which requires TensorFlow 2.4.x. When not using
ScaNN, any TF >= 2.3 is still supported.

Changed

- Pinned TensorFlow to >= 2.3 when ScaNN is not being installed. When ScaNN is
being installed, we pin on >= 2.3, < 2.4. This allows users to use TFRS on
TF 2.4 when they are not using ScaNN.

Changed

- Pinned TensorFlow to 2.3.x and ScaNN to 1.1.1 to ensure TF and ScaNN
versions are in lockstep.

Added

- Deep cross networks: efficient ways of learning feature interactions.
- ScaNN integration: efficient approximate maximum inner product search for
fast retrieval.

Added

- `tfrs.tasks.Ranking.call` now accepts a `compute_metrics` argument to allow
switching off metric computation.
- `tfrs.tasks.Ranking` now accepts label and prediction metrics.
- Add metrics setter/getters on `tfrs.tasks.Retrieval`.

Breaking changes

- Corpus retrieval metrics and layers have been reworked.

`tfrs.layers.corpus.DatasetTopk` has been removed,
`tfrs.layers.corpus.DatasetIndexedTopK` renamed to
`tfrs.layers.factorized_top_k.Streaming`, `tfrs.layers.ann.BruteForce`
renamed to `tfrs.layers.factorized_top_k.BruteForce`. All top-k retrieval
layers (`BruteForce`, `Streaming`) now follow a common interface.

Changed

- `Dataset` parallelism enabled by default in `DatasetTopK` and
`DatasetIndexedTopK` layers, bringing over 2x speed-ups to evaluations
workloads.
- `evaluate_metrics` argument to `tfrs.tasks.Retrieval.call` renamed to
`compute_metrics`.