FAST_TFRS recommendation engine

fast_tfrs is a deep learning recommendation engine for fast experimentations and productionization.

Why fast_tfrs

Built on Google Tensorflow Recommendation Systems tfrs, fast_tfrs aims to be:

• easy-to-use: no boiler-plate coding required, simple API to follow
• easy-to-rebuild (custom): modules are lego-like, ready to be mixed and match
• end-to-end: input raw data output predictions
• stable and scalable for production

To be specific, I contrast the feature differences between tfrs and fast_tfrs as follow:

• feature encoding

  • tfrs requires writting code to encode each feature used in the recommendation, making it not scalable and error-prone in case we have many features
  • fast_tfrs automates the encoding for all selected features, making it a bleeze for large scale experimentations.

• feature dtype

  • tfrs: requires expertise to encode feature of different data-types; multimodal data is usually handled under different modeling frameworks and each embedding takes on different dimensionalities.
  • fast_tfrs: provides helpers to automatically separate and encode features of different data-types including cnt, cat, txt, img, dt under the same modeling framework, with techniques enabling features of different dtypes being embedded with the same dimensionality.

• feature selection

  • tfrs provides no tools
  • fast_tfrs provides tools to select relevant features; this release includes L1 and VSN

• feature crossing

  • tfrs provides no tools
  • fast_tfrs provides tools to feature crossing (i.e. feature interactions); this release includes DeepCrossLayer, vsn_DeepCrossLayer

• multitask

  • tfrs provides 2 tasks -- ranking and retrieval
  • fast_tfrs provides 3 tasks -- ranking, retrieval and classification (imbalanced multiclass multimodal classification)

fast_tfrs is under active development and welcomes community contribution. For recent updates, please visit https://github.com/wjlgatech/fast_tfrs

Installation

url = f"git+https://github.com/wjlgatech/fast_tfrs.git"
!pip install --upgrade {url}

Quick Start

• Check out fast_tfrs_quickstart.ipynb

• More details: fast_tfrs_all_code_walkthrough.ipynb

Known Issues

As you will see in fast_tfrs_all_code_walkthrough.ipynb:

• "StopIteration: Queue is empty, no more objects to retrieve" in section Get the best fast_tfrs model by Bayesian tuning

• "TuneError: ('Trials did not complete'...)" in section FLAML + Ray tuning

• "feature importances plot lacks symetric" in function plot_cross_feature_importances(...) in section Model understanding

• The integration of classification_task with retrieval_task and rating_task under the tfrs framework in section 3-task w. L1/VSN features selection

Please let me know if you have any idea on how to fix them.