Tensorflow-recommenders

- Improved support for using TPUEmbedding under parameter server strategy.

A number of changes to make factorized top-K metric computation more accurate
and less prone to user error.

Changed

- `tfrs.layers.embedding.TPUEmbedding` now supports input features with
dynamic shape. `batch_size` argument is deprecated and no longer required.

- `tfrs.layers.embedding.TPUEmbedding` now supports running on different
versions of TPU.

- Pinned TensorFlow to >= 2.9.0 which works with Scann 1.2.7.

- `tfrs.tasks.Ranking.call` now accepts a `compute_batch_metrics` argument to
allow switching off batch metric computation. Following this change,
'compute_metrics'argument does not impact computation of batch metrics.

Breaking changes

- `tfrs.metrics.FactorizedTopK` requires the candidate ids for positive
candidates to be supplied when using approximate top-K sources. Each top-K
layer now has an `exact` method to broadcast its ability to return exact or
approximate top-K results.
- Removed `metrics` constructor parameter for `tfrs.metrics.FactorizedTopK`.
`FactorizedTopK` only makes sense with top-k metrics, and this change
enforces this.
- Replaced the `k` constructor argument in `tfrs.metrics.FactorizedTopK` with
`ks`: a list of `k` values at which to compute the top k metric.

Changed

- The `tfrs.metrics.FactorizedTopK` metric can now compute candidate-id based
metrics when given the `true_candidate_ids` argument in its `call` method.

Added

- The `Retrieval` task now also accepts a `loss_metrics` argument.

Changed

- Pinned TensorFlow to >= 2.6.0, which works with Scann 1.2.3.

Breaking changes

- `TopK` layer indexing API changed. Indexing with datasets is now done via
the `index_from_dataset` method. This change reduces the possibility of
misaligning embeddings and candidate identifiers when indexing via
indeterministic datasets.

Fixed

- Fixed error in default arguments to `tfrs.experimental.models.Ranking`
(https://github.com/tensorflow/recommenders/issues/311).
- Fix TPUEmbedding layer to use named parameters.

Added

- Added `batch_metrics` to `tfrs.tasks.Retrieval` for measuring how good the
model is at picking out the true candidate for a query from other candidates
in the batch.
- Added `tfrs.experimental.layers.embedding.PartialTPUEmbedding` layer, which
uses `tfrs.layers.embedding.TPUEmbedding` for large embedding lookups and
`tf.keras.layers.Embedding` for smaller embedding lookups.

Changed

- Supplying incompatibly-shaped candidates and identifiers inputs to
`factorized_top_k` layers will now raise (to prevent issues similar to
https://github.com/tensorflow/recommenders/issues/286).

Changed

- Fixed the bug in `tfrs.layers.loss.SamplingProbablityCorrection` that logits
should subtract the log of item probability.
- `tfrs.experimental.optimizers.CompositeOptimizer`: an optimizer that
composes multiple individual optimizers which can be applied to different
subsets of the model's variables.
- `tfrs.layers.dcn.Cross` and `DotInteraction` layers have been moved to
`tfrs.layers.feature_interaction` package.

Added

- `tfrs.experimental.models.Ranking`, an experimental pre-built model for
ranking tasks. Can be used as DLRM like model with Dot Product feature
interaction or DCN like model with Cross layer.