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# Copyright (c) Recommenders contributors. | ||
# Licensed under the MIT License. | ||
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# Book settings | ||
# Learn more at https://jupyterbook.org/customize/config.html | ||
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# To build the Jupyter Book: | ||
# $ jupyter-book clean docs | ||
# $ jupyter-book build docs | ||
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title: Recommenders documentation | ||
author: Recommenders contributors | ||
logo: https://raw.githubusercontent.com/recommenders-team/artwork/main/color/recommenders_color.svg | ||
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# Short description about the book | ||
description: >- | ||
Recommenders - Python utilities for building recommendation systems | ||
execute: | ||
execute_notebooks : off | ||
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# Interact link settings | ||
notebook_interface : "notebook" | ||
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# Launch button settings | ||
repository: | ||
url : https://github.com/recommenders-team/recommenders | ||
path_to_book : /docs | ||
branch : main | ||
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launch_buttons: | ||
notebook_interface : classic | ||
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# HTML-specific settings | ||
html: | ||
favicon : https://raw.githubusercontent.com/recommenders-team/artwork/main/icon/recommenders_color_icon.svg | ||
home_page_in_navbar : false | ||
use_repository_button : true | ||
use_issues_button : true | ||
baseurl : https://recommenders-team.github.io/recommenders/ | ||
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sphinx: | ||
extra_extensions: | ||
- sphinx_inline_tabs | ||
- sphinx.ext.autodoc | ||
- sphinx.ext.napoleon | ||
- sphinx.ext.viewcode | ||
config: | ||
napoleon_google_docstring: True | ||
autodoc_member_order: groupwise | ||
autoclass_content: both | ||
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# Copyright (c) Recommenders contributors. | ||
# Licensed under the MIT License. | ||
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# Table of contents | ||
# Learn more at https://jupyterbook.org/customize/toc.html | ||
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format: jb-book | ||
root: intro | ||
defaults: | ||
numbered: false | ||
parts: | ||
- caption: Recommenders API Documentation | ||
chapters: | ||
- file: datasets | ||
- file: evaluation | ||
- file: models | ||
- file: tuning | ||
- file: utils |
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<!-- | ||
Copyright (c) Recommenders contributors. | ||
Licensed under the MIT License. | ||
--> | ||
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# Welcome to Recommenders | ||
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Recommenders objective is to assist researchers, developers and enthusiasts in prototyping, experimenting with and bringing to production a range of classic and state-of-the-art recommendation systems. | ||
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````{margin} | ||
```sh | ||
pip install recommenders | ||
``` | ||
<a class="github-button" href="https://github.com/recommenders-team/recommenders" data-icon="octicon-star" style="margin:auto" data-size="large" data-show-count="true" aria-label="Star Recommenders on GitHub">Star Us</a><script async defer src="https://buttons.github.io/buttons.js"></script> | ||
```` | ||
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Recommenders is a project under the [Linux Foundation of AI and Data](https://lfaidata.foundation/projects/). | ||
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This repository contains examples and best practices for building recommendation systems, provided as Jupyter notebooks. | ||
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The examples detail our learnings on five key tasks: | ||
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- Prepare Data: Preparing and loading data for each recommendation algorithm. | ||
- Model: Building models using various classical and deep learning recommendation 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)). | ||
- Evaluate: Evaluating algorithms with offline metrics. | ||
- Model Select and Optimize: Tuning and optimizing hyperparameters for recommendation models. | ||
- Operationalize: Operationalizing models in a production environment. | ||
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Several utilities are provided in the `recommenders` library 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. | ||
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<!-- ```{tableofcontents} | ||
``` --> | ||
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jupyter-book | ||
sphinx | ||
sphinx_inline_tabs | ||
ghp-import |
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