Staging to main: LF announcement and GRU update

AUTHORS.md

@@ -1,9 +1,14 @@
+<!--
+Copyright (c) Recommenders contributors.
+Licensed under the MIT License.
+-->
+
 Contributors to Recommenders 
 ============================
 Recommenders is developed and maintained by a community of people interested in exploring recommendation algorithms and how best to deploy them in industry settings. The goal is to accelerate the workflow of any individual or organization working on recommender systems. Everyone is encouraged to contribute at any level to add and improve the implemented algorithms, notebooks and utilities.
 
 <p align="center">
-  <img src="https://contributors-img.web.app/image?repo=microsoft/recommenders" width = 500/>
+  <img src="https://contributors-img.web.app/image?repo=recommenders-team/recommenders" width = 500/>
 </p>
 
 Maintainers (sorted alphabetically)

CODE_OF_CONDUCT.md

@@ -1,9 +1,42 @@
-# Microsoft Open Source Code of Conduct
+<!--
+Copyright (c) Recommenders contributors.
+Licensed under the MIT License.
+-->
 
-This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
+# Recommenders Code of Conduct
 
-Resources:
+This code of conduct outlines expectations for participation in the Recommenders open source community, as well as steps for reporting unacceptable behavior. We are committed to providing a welcoming and inspiring community for all. People violating this code of conduct may be banned from the community. Our open source community strives to:
 
-- [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/)
-- [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/)
-- Contact [[email protected]](mailto:[email protected]) with any additional questions or comments.
+* **Be friendly and patient**: Remember you might not be communicating in someone else's primary spoken or programming language, and others may not have your level of understanding.
+
+* **Be welcoming**: Our community welcomes and supports people of all backgrounds and identities. This includes, but is not limited to members of any race, ethnicity, culture, national origin, color, immigration status, social and economic class, educational level, sex, sexual orientation, gender identity and expression, age, size, family status, political belief, religion, and mental and physical ability.
+
+* **Be respectful**: We are a world-wide community of professionals, and we conduct ourselves professionally. Disagreement is no excuse for poor behavior and poor manners. Disrespectful and unacceptable behavior includes, but is not limited to:
+    1. Violent threats or language.
+    1. Discriminatory or derogatory jokes and language.
+    1. Posting sexually explicit or violent material.
+    1. Posting, or threatening to post, people's personally identifying information ("doxing").
+    1. Insults, especially those using discriminatory terms or slurs.
+    1. Behavior that could be perceived as sexual attention.
+    1. Advocating for or encouraging any of the above behaviors.
+
+* **Understand disagreements**: Disagreements, both social and technical, are useful learning opportunities. Seek to understand the other viewpoints and resolve differences constructively.
+
+* **Remember that we’re different**. The strength of our community comes from its diversity, people from a wide range of backgrounds. Different people have different perspectives on issues. Being unable to understand why someone holds a viewpoint doesn’t mean that they’re wrong. Focus on helping to resolve issues and learning from mistakes.
+
+* This code is not exhaustive or complete. It serves to capture our common understanding of a productive, collaborative environment. We expect the code to be followed in spirit as much as in the letter.
+
+## Reporting Code of Conduct Issues
+
+We encourage all communities to resolve issues on their own whenever possible. This builds a broader and deeper understanding and ultimately a healthier interaction. In the event that an issue cannot be resolved locally, please feel free to report your concerns by contacting [email protected]. In your report please include:
+
+1. Your contact information.
+1. Names (real, usernames or pseudonyms) of any individuals involved. If there are additional witnesses, please include them as well.
+1. Your account of what occurred, and if you believe the incident is ongoing. If there is a publicly available record (e.g. a mailing list archive or a public chat log), please include a link or attachment.
+1. Any additional information that may be helpful.
+
+All reports will be reviewed by a multi-person team and will result in a response that is deemed necessary and appropriate to the circumstances. Where additional perspectives are needed, the team may seek insight from others with relevant expertise or experience. The confidentiality of the person reporting the incident will be kept at all times. Involved parties are never part of the review team.
+
+Anyone asked to stop unacceptable behavior is expected to comply immediately. If an individual engages in unacceptable behavior, the review team may take any action they deem appropriate, including a permanent ban from the community.
+
+*This code of conduct is based on the [template](http://todogroup.org/opencodeofconduct) established by the [TODO Group](http://todogroup.org/) and used by numerous other large communities and the Scope section from the [Contributor Covenant version 1.4](http://contributor-covenant.org/version/1/4/).*

CONTRIBUTING.md

@@ -1,3 +1,8 @@
+<!--
+Copyright (c) Recommenders contributors.
+Licensed under the MIT License.
+-->
+
 # Contribution Guidelines
 
 Contributions are welcomed! Here's a few things to know:
@@ -32,9 +37,9 @@ Once the features included in a [milestone](https://github.com/microsoft/recomme
 
 We strive to maintain high quality code to make the utilities in the repository easy to understand, use, and extend. We also work hard to maintain a friendly and constructive environment. We've found that having clear expectations on the development process and consistent style helps to ensure everyone can contribute and collaborate effectively.
 
-Please review the [coding guidelines](https://github.com/Microsoft/Recommenders/wiki/Coding-Guidelines) wiki page to see more details about the expectations for development approach and style.
+Please review the [coding guidelines](https://github.com/recommenders-team/recommenders/wiki/Coding-Guidelines) wiki page to see more details about the expectations for development approach and style.
 
-Apart from the official [Code of Conduct](CODE_OF_CONDUCT.md) developed by Microsoft, in the Recommenders team we adopt the following behaviors, to ensure a great working environment:
+Apart from the official [Code of Conduct](CODE_OF_CONDUCT.md), in Recommenders team we adopt the following behaviors, to ensure a great working environment:
 
 #### Do not point fingers
 Let’s be constructive.

GLOSSARY.md

@@ -1,3 +1,8 @@
+<!--
+Copyright (c) Recommenders contributors.
+Licensed under the MIT License.
+-->
+
 # Glossary
 
 * **A/B testing**: Methodology to evaluate the performance of a system in production. In the context of Recommendation Systems it is used to measure a machine learning model performance in real-time. It works by randomizing an environment response into two groups A and B, typically half of the traffic goes to the machine learning model output and the other half is left without model. By comparing the metrics from A and B branches, it is possible to evaluate whether it is beneficial the use of the model or not. A test with more than two groups it is named Multi-Variate Test.

NEWS.md

@@ -1,5 +1,14 @@
+<!--
+Copyright (c) Recommenders contributors.
+Licensed under the MIT License.
+-->
+
 # What's New
 
+## Update October 3, 2023
+
+We are pleased to announced that we have joined the [Linux Foundation of AI and Data](https://lfaidata.foundation/) (LF AI & Data). With this move, our objective is to keep building an ecosystem to sustain open source innovation and collaboration in recommendation systems.
+
 ## Update August 18, 2023
 
 We moved to a new organization! Now to access the repo, instead of going to https://github.com/microsoft/recommenders, you need to go to https://github.com/recommenders-team/recommenders. The old URL will still resolve to the new one, but we recommend you to update your bookmarks.

README.md

@@ -1,20 +1,25 @@
+<!--
+Copyright (c) Recommenders contributors.
+Licensed under the MIT License.
+-->
+
 # Recommenders
 
 [![Documentation Status](https://readthedocs.org/projects/microsoft-recommenders/badge/?version=latest)](https://microsoft-recommenders.readthedocs.io/en/latest/?badge=latest)
 
 <img src="https://raw.githubusercontent.com/recommenders-team/artwork/main/color/recommenders_color.svg" width="800">
 
-## What's New (August, 2023)
+## What's New (October, 2023)
 
-We moved to a new organization! Now to access the repo, instead of going to https://github.com/microsoft/recommenders, you need to go to https://github.com/recommenders-team/recommenders. The old URL will still resolve to the new one, but we recommend that you update your bookmarks.
+We are pleased to announced that we have joined the [Linux Foundation of AI and Data](https://lfaidata.foundation/) (LF AI & Data). With this move, our objective is to keep building an ecosystem to sustain open source innovation and collaboration in recommendation systems.
 
-Starting with release 0.6.0, Recommenders has been available on PyPI and can be installed using pip! 
+We moved to a new organization! Now to access the repo, instead of going to https://github.com/microsoft/recommenders, you need to go to https://github.com/recommenders-team/recommenders. The old URL will still resolve to the new one, but we recommend that you update your bookmarks.
 
-Here you can find the PyPi page: https://pypi.org/project/recommenders/
+## Introduction
 
-Here you can find the package documentation: https://microsoft-recommenders.readthedocs.io/en/latest/
+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.
 
-## Introduction
+Recommenders is a project under the [Linux Foundation of AI and Data](https://lfaidata.foundation/projects/). 
 
 This repository contains examples and best practices for building recommendation systems, provided as Jupyter notebooks. The examples detail our learnings on five key tasks:
 
@@ -82,7 +87,7 @@ The table below lists the recommender algorithms currently available in the repo
 | LightGBM/Gradient Boosting Tree<sup>*</sup> | Content-Based Filtering | Gradient Boosting Tree algorithm for fast training and low memory usage in content-based problems. It works in the CPU/GPU/PySpark environments. | [Quick start in CPU](examples/00_quick_start/lightgbm_tinycriteo.ipynb) / [Deep dive in PySpark](examples/02_model_content_based_filtering/mmlspark_lightgbm_criteo.ipynb) |
 | LightGCN | Collaborative Filtering | Deep learning algorithm which simplifies the design of GCN for predicting implicit feedback. It works in the CPU/GPU environment. | [Deep dive](examples/02_model_collaborative_filtering/lightgcn_deep_dive.ipynb) |
 | GeoIMC<sup>*</sup> | Hybrid | Matrix completion algorithm that has into account user and item features using Riemannian conjugate gradients optimization and following a geometric approach. It works in the CPU environment. | [Quick start](examples/00_quick_start/geoimc_movielens.ipynb) |
-| GRU4Rec | Collaborative Filtering | Sequential-based algorithm that aims to capture both long and short-term user preferences using recurrent neural networks. It works in the CPU/GPU environment. | [Quick start](examples/00_quick_start/sequential_recsys_amazondataset.ipynb) |
+| GRU | Collaborative Filtering | Sequential-based algorithm that aims to capture both long and short-term user preferences using recurrent neural networks. It works in the CPU/GPU environment. | [Quick start](examples/00_quick_start/sequential_recsys_amazondataset.ipynb) |
 | Multinomial VAE | Collaborative Filtering | Generative model for predicting user/item interactions. It works in the CPU/GPU environment. | [Deep dive](examples/02_model_collaborative_filtering/multi_vae_deep_dive.ipynb) |
 | Neural Recommendation with Long- and Short-term User Representations (LSTUR)<sup>*</sup> | Content-Based Filtering | Neural recommendation algorithm for recommending news articles with long- and short-term user interest modeling. It works in the CPU/GPU environment. | [Quick start](examples/00_quick_start/lstur_MIND.ipynb) |
 | Neural Recommendation with Attentive Multi-View Learning (NAML)<sup>*</sup> | Content-Based Filtering | Neural recommendation algorithm for recommending news articles with attentive multi-view learning. It works in the CPU/GPU environment. | [Quick start](examples/00_quick_start/naml_MIND.ipynb) |

SECURITY.md

@@ -0,0 +1,18 @@
+<!--
+Copyright (c) Recommenders contributors.
+Licensed under the MIT License.
+-->
+
+# Security Policy
+
+## Reporting a Vulnerability
+If you think you have found a security vulnerability, please send a report to [email protected]. 
+
+We don't currently have a PGP key, unfortunately.
+
+A Recommenders committer will send you a response indicating the next steps in handling your report. After the initial reply to your report, the committer will keep you informed of the progress towards a fix and full announcement, and may ask for additional information or guidance.
+
+Important: Please don't disclose the vulnerability before it has been fixed and announced, to protect our users.
+
+## Security announcements
+Please subscribe to the [announcements mailing list](https://lists.lfaidata.foundation/g/recommenders-announce), where we post notifications and remediation details for security vulnerabilities.

SETUP.md

@@ -1,3 +1,8 @@
+<!--
+Copyright (c) Recommenders contributors.
+Licensed under the MIT License.
+-->
+
 # Setup Guide
 
 The repo, including this guide, is tested on Linux. Where applicable, we document differences in [Windows](#windows-specific-instructions) and [MacOS](#macos-specific-instructions) although 

docs/source/models.rst

@@ -57,9 +57,9 @@ Caser
 .. automodule:: recommenders.models.deeprec.models.sequential.caser
     :members:
 
-GRU4Rec
+GRU
 --------------
-.. automodule:: recommenders.models.deeprec.models.sequential.gru4rec
+.. automodule:: recommenders.models.deeprec.models.sequential.gru
     :members:
 
 NextItNet

examples/00_quick_start/README.md

@@ -20,7 +20,7 @@ In this directory, notebooks are provided to perform a quick demonstration of di
 | [sar_azureml_designer](sar_movieratings_with_azureml_designer.ipynb) | MovieLens | Python CPU | An example of how to implement SAR on [AzureML Designer](https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer). |
 | [a2svd](sequential_recsys_amazondataset.ipynb) | Amazon | Python CPU, GPU | Use A2SVD [11] to predict a set of movies the user is going to interact in a short time. |
 | [caser](sequential_recsys_amazondataset.ipynb) | Amazon | Python CPU, GPU | Use Caser [12] to predict a set of movies the user is going to interact in a short time. |
-| [gru4rec](sequential_recsys_amazondataset.ipynb) | Amazon | Python CPU, GPU | Use GRU4Rec [13] to predict a set of movies the user is going to interact in a short time. |
+| [gru](sequential_recsys_amazondataset.ipynb) | Amazon | Python CPU, GPU | Use GRU [13] to predict a set of movies the user is going to interact in a short time. |
 | [nextitnet](sequential_recsys_amazondataset.ipynb) | Amazon | Python CPU, GPU | Use NextItNet [14] to predict a set of movies the user is going to interact in a short time. |
 | [sli-rec](sequential_recsys_amazondataset.ipynb) | Amazon | Python CPU, GPU | Use SLi-Rec [11] to predict a set of movies the user is going to interact in a short time. |
 | [wide-and-deep](wide_deep_movielens.ipynb) | MovieLens | Python CPU, GPU |  Utilizing Wide-and-Deep Model (Wide-and-Deep) [5] to predict movie ratings in a Python+GPU (TensorFlow) environment.
@@ -38,5 +38,5 @@ In this directory, notebooks are provided to perform a quick demonstration of di
 [10] _NPA: Neural News Recommendation with Personalized Attention_, Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang and Xing Xie. KDD 2019, ADS track.<br>
 [11] _Adaptive User Modeling with Long and Short-Term Preferences for Personailzed Recommendation_, Zeping Yu, Jianxun Lian, Ahmad Mahmoody, Gongshen Liu and Xing Xie, IJCAI 2019.<br>
 [12] _Personalized top-n sequential recommendation via convolutional sequence embedding_, Jiaxi Tang and Ke Wang, ACM WSDM 2018.<br>
-[13] _Session-based Recommendations with Recurrent Neural Networks_, Balazs Hidasi, Alexandros Karatzoglou, Linas Baltrunas and Domonkos Tikk, ICLR 2016.<br>
+[13] _Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation_, Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio, arXiv preprint arXiv:1406.1078. 2014.<br>
 [14] _A Simple Convolutional Generative Network for Next Item Recommendation_, Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose and Xiangnan He, WSDM 2019. <br>

examples/00_quick_start/sasrec_amazon.ipynb

@@ -15,7 +15,7 @@
     "\n",
     "![image.png](attachment:image.png)\n",
     "\n",
-    "This is a class of sequential recommendation that uses Transformer \\[2\\] for encoding the users preference represented in terms of a sequence of items purchased/viewed before. Instead of using CNN (Caser \\[3\\]) or RNN (GRU4Rec \\[4\\], SLI-Rec \\[5\\] etc.) the approach relies on Transformer based encoder that generates a new representation of the item sequence. Two variants of this Transformer based approaches are included here, \n",
+    "This is a class of sequential recommendation that uses Transformer \\[2\\] for encoding the users preference represented in terms of a sequence of items purchased/viewed before. Instead of using CNN (Caser \\[3\\]) or RNN (GRU \\[4\\], SLI-Rec \\[5\\] etc.) the approach relies on Transformer based encoder that generates a new representation of the item sequence. Two variants of this Transformer based approaches are included here, \n",
     "\n",
     "- Self-Attentive Sequential Recommendation (or SASRec [1]) that is based on vanilla Transformer and models only the item sequence and\n",
     "- Stochastic Shared Embedding based Personalized Transformer or SSE-PT [6], that also models the users along with the items. \n",
@@ -456,7 +456,7 @@
     "\n",
     "\\[3\\] Jiaxi Tang and Ke Wang. 2018. Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. ACM, 565–573.\n",
     "\n",
-    "\\[4\\] Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015)\n",
+    "\\[4\\] Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. arXiv preprint arXiv:1406.1078. 2014.\n",
     "\n",
     "\\[5\\] Zeping Yu, Jianxun Lian, Ahmad Mahmoody, Gongshen Liu, Xing Xie. Adaptive User Modeling with Long and Short-Term Preferences for Personailzed Recommendation. In Proceedings of the 28th International Joint Conferences on Artificial Intelligence, IJCAI’19, Pages 4213-4219. AAAI Press, 2019.\n",
     "\n",