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CHANGELOG.md

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Changelog

[Unreleased][unreleased]

Changed

  • Fixed the bug in tfrs.layers.loss.SamplingProbablityCorrection that logits should substract the log of item probability.
  • tfrs.experimental.models.RankingModel can be used as DLRM like model with Dot Product feature interaction or DCN like model with Cross layer.
  • 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.

[0.4.0][2021-01-20]

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.

[0.3.2][2020-12-22]

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.

[0.3.1][2020-12-22]

Changed

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

[0.3.0][2020-11-18]

Added

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

[0.2.0][2020-10-15]

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.