v0.7.0

[0.7.0][2022-07-07]

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.