Hey everyone,
\nI would like to share this great repository that implements many many different algorithms to tackle recommendation systems. The full list of algorithms is here
\nhttps://github.com/microsoft/recommenders
\nFrom the repository:
\nThis repository contains examples and best practices for building recommendation systems, provided as Jupyter notebooks. The examples detail our learnings on five key tasks:
\nSeveral utilities are provided in recommenders to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting training/test data. Implementations of several state-of-the-art algorithms are included for self-study and customization in your own applications. See the Recommenders documentation.
\nFor a more detailed overview of the repository, please see the documents on the wiki page.
", "messages": [ { "id": "2014853", "postDate": "11/02/2022 22:58:54", "content": "Hey everyone,
\nI would like to share this great repository that implements many many different algorithms to tackle recommendation systems. The full list of algorithms is here
\nhttps://github.com/microsoft/recommenders
\nFrom the repository:
\nThis repository contains examples and best practices for building recommendation systems, provided as Jupyter notebooks. The examples detail our learnings on five key tasks:
\nSeveral utilities are provided in recommenders to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting training/test data. Implementations of several state-of-the-art algorithms are included for self-study and customization in your own applications. See the Recommenders documentation.
\nFor a more detailed overview of the repository, please see the documents on the wiki page.
", "rawMarkdown": "Hey everyone, \n\nI would like to share this great repository that implements many many different algorithms to tackle recommendation systems. The full list of algorithms is [here](https://github.com/microsoft/recommenders#algorithms)\n\nhttps://github.com/microsoft/recommenders\n\n*From the repository:*\n\nThis repository contains examples and best practices for building recommendation systems, provided as Jupyter notebooks. The examples detail our learnings on five key tasks:\n\n- [Prepare Data](examples/01_prepare_data): Preparing and loading data for each recommender algorithm\n- [Model](examples/00_quick_start): Building models using various classical and deep learning recommender algorithms such as Alternating Least Squares ([ALS](https://spark.apache.org/docs/latest/api/python/_modules/pyspark/ml/recommendation.html#ALS)) or eXtreme Deep Factorization Machines ([xDeepFM](https://arxiv.org/abs/1803.05170)).\n- [Evaluate](examples/03_evaluate): Evaluating algorithms with offline metrics\n- [Model Select and Optimize](examples/04_model_select_and_optimize): Tuning and optimizing hyperparameters for recommender models\n- [Operationalize](examples/05_operationalize): Operationalizing models in a production environment on Azure\n\nSeveral utilities are provided in [recommenders](recommenders) to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting training/test data. Implementations of several state-of-the-art algorithms are included for self-study and customization in your own applications. See the [Recommenders documentation](https://readthedocs.org/projects/microsoft-recommenders/).\n\nFor a more detailed overview of the repository, please see the documents on the [wiki page](https://github.com/microsoft/recommenders/wiki/Documents-and-Presentations).", "votes": null }, { "id": "2016626", "postDate": "11/04/2022 06:39:24", "content": "Me gusta ;-)
", "rawMarkdown": "Me gusta ;-)", "votes": null } ], "comments": [ { "id": 2016626, "author_name": "hoaphumanoid", "author_url": "", "post_date": "11/04/2022 06:39:24", "content": "Me gusta ;-)
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "2014853": "Hey everyone, \n\nI would like to share this great repository that implements many many different algorithms to tackle recommendation systems. The full list of algorithms is [here](https://github.com/microsoft/recommenders#algorithms)\n\nhttps://github.com/microsoft/recommenders\n\n*From the repository:*\n\nThis repository contains examples and best practices for building recommendation systems, provided as Jupyter notebooks. The examples detail our learnings on five key tasks:\n\n- [Prepare Data](examples/01_prepare_data): Preparing and loading data for each recommender algorithm\n- [Model](examples/00_quick_start): Building models using various classical and deep learning recommender algorithms such as Alternating Least Squares ([ALS](https://spark.apache.org/docs/latest/api/python/_modules/pyspark/ml/recommendation.html#ALS)) or eXtreme Deep Factorization Machines ([xDeepFM](https://arxiv.org/abs/1803.05170)).\n- [Evaluate](examples/03_evaluate): Evaluating algorithms with offline metrics\n- [Model Select and Optimize](examples/04_model_select_and_optimize): Tuning and optimizing hyperparameters for recommender models\n- [Operationalize](examples/05_operationalize): Operationalizing models in a production environment on Azure\n\nSeveral utilities are provided in [recommenders](recommenders) to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting training/test data. Implementations of several state-of-the-art algorithms are included for self-study and customization in your own applications. See the [Recommenders documentation](https://readthedocs.org/projects/microsoft-recommenders/).\n\nFor a more detailed overview of the repository, please see the documents on the [wiki page](https://github.com/microsoft/recommenders/wiki/Documents-and-Presentations).", "2016626": "Me gusta ;-)" }, "source": "meta" }