Staging to main: Documentation update, algorithm classification update and fix error in MIND

.github/PULL_REQUEST_TEMPLATE.md

@@ -15,7 +15,8 @@
 ### Checklist:
 <!--- Go over all the following points, and put an `x` in all the boxes that apply. -->
 <!--- If you're unsure about any of these, don't hesitate to ask. We're here to help! -->
-- [ ] I have followed the [contribution guidelines](../CONTRIBUTING.md) and code style for this project.
+- [ ] I have followed the [contribution guidelines](CONTRIBUTING.md) and code style for this project.
 - [ ] I have added tests covering my contributions.
 - [ ] I have updated the documentation accordingly.
-- [ ] This PR is being made to `staging branch` and not to `main branch`.
+- [ ] I have [signed the commits](https://github.com/recommenders-team/recommenders/wiki/How-to-sign-commits), e.g. `git commit -s -m "your commit message"`. 
+- [ ] This PR is being made to `staging branch` AND NOT TO `main branch`.

CONTRIBUTING.md

@@ -23,15 +23,15 @@ Contributions are welcomed! Here's a few things to know:
 Here are the basic steps to get started with your first contribution. Please reach out with any questions.
 1. Use [open issues](https://github.com/Microsoft/Recommenders/issues) to discuss the proposed changes. Create an issue describing changes if necessary to collect feedback. Also, please use provided labels to tag issues so everyone can easily sort issues of interest.
 1. [Fork the repo](https://help.github.com/articles/fork-a-repo/) so you can make and test local changes.
-1. Create a new branch **from staging branch** for the issue (please do not create a branch from main). We suggest prefixing the branch with your username and then a descriptive title: (e.g. gramhagen/update_contributing_docs)
+1. Create a new branch **from staging branch** for the issue (please do not create a branch from main). We suggest prefixing the branch with your username and then a descriptive title: (e.g. `gramhagen/update_contributing_docs`)
 1. Install recommenders package locally using the right optional dependency for your test and the dev option. (e.g. gpu test: `pip install -e .[gpu,dev]`)
 1. Create a test that replicates the issue.
 1. Make code changes.
 1. Ensure unit tests pass and code style / formatting is consistent (see [wiki](https://github.com/Microsoft/Recommenders/wiki/Coding-Guidelines#python-and-docstrings-style) for more details).
 1. When adding code to the repo, make sure you sign the commits, otherwise the tests will fail (see [how to sign the commits](https://github.com/recommenders-team/recommenders/wiki/How-to-sign-commits)).
 1. Create a pull request against **staging** branch.
 
-Once the features included in a [milestone](https://github.com/microsoft/recommenders/milestones) are completed, we will merge staging into main. See the wiki for more detail about our [merge strategy](https://github.com/microsoft/recommenders/wiki/Strategy-to-merge-the-code-to-main-branch).
+See the wiki for more details about our [merging strategy](https://github.com/microsoft/recommenders/wiki/Strategy-to-merge-the-code-to-main-branch).
 
 ## Coding Guidelines
 

GLOSSARY.md

@@ -9,7 +9,7 @@ Licensed under the MIT License.
 
 * **Click-through rate (CTR)**: Ratio of the number of users who click on a link over the total number of users that visited the page. CTR is a measure of the user engagement.
 
-* **Cold-start problem**: The cold start problem concerns the recommendations for users with no or few past history (new users). Providing recommendations to users with small past history becomes a difficult problem for collaborative filtering models because their learning and predictive ability is limited. Multiple research have been conducted in this direction using content-based filtering models or hybrid models. These models use auxiliary information like user or item metadata to overcome the cold start problem.
+* **Cold-start problem**: The cold start problem concerns the recommendations for users with no or few past history (new users). Providing recommendations to users with small past history becomes a difficult problem for collaborative filtering models because their learning and predictive ability is limited. Multiple research have been conducted in this direction using content-based filtering models. These models use auxiliary information like user or item metadata to overcome the cold start problem.
 
 * **Collaborative filtering algorithms (CF)**: CF algorithms make prediction of what is the likelihood of a user selecting an item based on the behavior of other users [1]. It assumes that if user A likes item X and Y, and user B likes item X, user B would probably like item Y. See the [list of CF examples in Recommenders repository](examples/02_model_collaborative_filtering).
 
@@ -21,8 +21,6 @@ Licensed under the MIT License.
 
 * **Explicit interaction data**: When a user explicitly rate an item, typically between 1-5, the user is giving a value on the likeliness of the item. 
 
-* **Hybrid filtering algorithms**: This type of recommendation system can implement a combination of collaborative and content-based filtering models. See the [list of examples in Recommenders repository](examples/02_model_hybrid).
-
 * **Implicit interaction data**: Implicit interactions are views or clicks that show a certain interest of the user about a specific items. These kind of data is more common but it doesn't define the intention of the user as clearly as the explicit data.
 
 * **Item information**: These include information about the item, some examples can be name, description, price, etc.

README.md

@@ -83,12 +83,12 @@ The table below lists the recommender algorithms currently available in the repo
 | Cornac/Bilateral Variational Autoencoder (BiVAE) | Collaborative Filtering | Generative model for dyadic data (e.g., user-item interactions). It works in the CPU/GPU environment. | [Deep dive](examples/02_model_collaborative_filtering/cornac_bivae_deep_dive.ipynb) |
 | Convolutional Sequence Embedding Recommendation (Caser) | Collaborative Filtering | Algorithm based on convolutions that aim to capture both user’s general preferences and sequential patterns. It works in the CPU/GPU environment. | [Quick start](examples/00_quick_start/sequential_recsys_amazondataset.ipynb) |
 | Deep Knowledge-Aware Network (DKN)<sup>*</sup> | Content-Based Filtering | Deep learning algorithm incorporating a knowledge graph and article embeddings for providing news or article recommendations. It works in the CPU/GPU environment. | [Quick start](examples/00_quick_start/dkn_MIND.ipynb) / [Deep dive](examples/02_model_content_based_filtering/dkn_deep_dive.ipynb) |
-| Extreme Deep Factorization Machine (xDeepFM)<sup>*</sup> | Hybrid | Deep learning based algorithm for implicit and explicit feedback with user/item features. It works in the CPU/GPU environment. | [Quick start](examples/00_quick_start/xdeepfm_criteo.ipynb) |
+| Extreme Deep Factorization Machine (xDeepFM)<sup>*</sup> | Collaborative Filtering | Deep learning based algorithm for implicit and explicit feedback with user/item features. It works in the CPU/GPU environment. | [Quick start](examples/00_quick_start/xdeepfm_criteo.ipynb) |
 | FastAI Embedding Dot Bias (FAST) | Collaborative Filtering | General purpose algorithm with embeddings and biases for users and items. It works in the CPU/GPU environment. | [Quick start](examples/00_quick_start/fastai_movielens.ipynb) |
-| LightFM/Hybrid Matrix Factorization | Hybrid | Hybrid matrix factorization algorithm for both implicit and explicit feedbacks. It works in the CPU environment. | [Quick start](examples/02_model_hybrid/lightfm_deep_dive.ipynb) |
+| LightFM/Factorization Machine | Collaborative Filtering | Factorization Machine algorithm for both implicit and explicit feedbacks. It works in the CPU environment. | [Quick start](examples/02_model_collaborative_filtering/lightfm_deep_dive.ipynb) |
 | 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) |
+| GeoIMC<sup>*</sup> | Collaborative Filtering | 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) |
 | 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) |
@@ -108,8 +108,8 @@ The table below lists the recommender algorithms currently available in the repo
 | Surprise/Singular Value Decomposition (SVD) | Collaborative Filtering | Matrix factorization algorithm for predicting explicit rating feedback in small datasets. It works in the CPU/GPU environment. | [Deep dive](examples/02_model_collaborative_filtering/surprise_svd_deep_dive.ipynb) |
 | Term Frequency - Inverse Document Frequency (TF-IDF) | Content-Based Filtering | Simple similarity-based algorithm for content-based recommendations with text datasets. It works in the CPU environment. | [Quick  start](examples/00_quick_start/tfidf_covid.ipynb) |
 | Vowpal Wabbit (VW)<sup>*</sup> | Content-Based Filtering | Fast online learning algorithms, great for scenarios where user features / context are constantly changing. It uses the CPU for online learning. | [Deep dive](examples/02_model_content_based_filtering/vowpal_wabbit_deep_dive.ipynb) |
-| Wide and Deep | Hybrid | Deep learning algorithm that can memorize feature interactions and generalize user features. It works in the CPU/GPU environment. | [Quick start](examples/00_quick_start/wide_deep_movielens.ipynb) |
-| xLearn/Factorization Machine (FM) & Field-Aware FM (FFM) | Hybrid | Quick and memory efficient algorithm to predict labels with user/item features. It works in the CPU/GPU environment. | [Deep dive](examples/02_model_hybrid/fm_deep_dive.ipynb) |
+| Wide and Deep | Collaborative Filtering | Deep learning algorithm that can memorize feature interactions and generalize user features. It works in the CPU/GPU environment. | [Quick start](examples/00_quick_start/wide_deep_movielens.ipynb) |
+| xLearn/Factorization Machine (FM) & Field-Aware FM (FFM) | Collaborative Filtering | Quick and memory efficient algorithm to predict labels with user/item features. It works in the CPU/GPU environment. | [Deep dive](examples/02_model_collaborative_filtering/fm_deep_dive.ipynb) |
 
 **NOTE**: <sup>*</sup> indicates algorithms invented/contributed by Microsoft.
 
@@ -130,7 +130,7 @@ We provide a [benchmark notebook](examples/06_benchmarks/movielens.ipynb) to ill
 | [BPR](examples/02_model_collaborative_filtering/cornac_bpr_deep_dive.ipynb) | 0.132478	| 0.441997 |	0.388229 |	0.212522 | N/A |	N/A |	N/A |	N/A |
 | [FastAI](examples/00_quick_start/fastai_movielens.ipynb) | 0.025503 |	0.147866 |	0.130329 |	0.053824 | 0.943084 |	0.744337 |	0.285308 |	0.287671 |
 | [LightGCN](examples/02_model_collaborative_filtering/lightgcn_deep_dive.ipynb) | 0.088526 | 0.419846 | 0.379626 | 0.144336 | N/A | N/A | N/A | N/A |
-| [NCF](examples/02_model_hybrid/ncf_deep_dive.ipynb) | 0.107720	| 0.396118 |	0.347296 |	0.180775 | N/A | N/A | N/A | N/A |
+| [NCF](examples/02_model_collaborative_filtering/ncf_deep_dive.ipynb) | 0.107720	| 0.396118 |	0.347296 |	0.180775 | N/A | N/A | N/A | N/A |
 | [SAR](examples/00_quick_start/sar_movielens.ipynb) | 0.110591 |	0.382461 | 	0.330753 | 0.176385 | 1.253805 | 1.048484 |	-0.569363 |	0.030474 |
 | [SVD](examples/02_model_collaborative_filtering/surprise_svd_deep_dive.ipynb) | 0.012873	| 0.095930 |	0.091198 |	0.032783 | 0.938681 | 0.742690 | 0.291967 | 0.291971 |
 
@@ -142,7 +142,7 @@ This project adheres to [Microsoft's Open Source Code of Conduct](CODE_OF_CONDUC
 
 ## Build Status
 
-These tests are the nightly builds, which compute the asynchronous tests. `main` is our principal branch and `staging` is our development branch. We use [pytest](https://docs.pytest.org/) for testing python utilities in [recommenders](recommenders) and [Papermill](https://github.com/nteract/papermill) and [Scrapbook](https://nteract-scrapbook.readthedocs.io/en/latest/) for the [notebooks](examples). 
+These tests are the nightly builds, which compute the asynchronous tests. `main` is our principal branch and `staging` is our development branch. We use [pytest](https://docs.pytest.org/) for testing python utilities in [recommenders](recommenders) and the Recommenders [notebook executor](recommenders/utils/notebook_utils.py) for the [notebooks](examples). 
 
 For more information about the testing pipelines, please see the [test documentation](tests/README.md).
 

examples/02_model_collaborative_filtering/README.md

@@ -8,6 +8,8 @@ In this directory, notebooks are provided to give a deep dive of collaborative f
 | [baseline_deep_dive](baseline_deep_dive.ipynb) | --- | Deep dive on baseline performance estimation.
 | [cornac_bivae_deep_dive](cornac_bivae_deep_dive.ipynb) | Python CPU, GPU | Deep dive on the BiVAE algorithm and implementation.
 | [cornac_bpr_deep_dive](cornac_bpr_deep_dive.ipynb) | Python CPU | Deep dive on the BPR algorithm and implementation.
+| [fm_deep_dive](fm_deep_dive.ipynb) | Python CPU | Deep dive into factorization machine (FM) and field-aware FM (FFM) algorithm.
+| [lightfm_deep_dive](lightfm_deep_dive.ipynb) | Python CPU | Deep dive into matrix factorization model with LightFM.
 | [lightgcn_deep_dive](lightgcn_deep_dive.ipynb) | Python CPU, GPU | Deep dive on a LightGCN algorithm and implementation.
 | [multi_vae_deep_dive](multi_vae_deep_dive.ipynb) | Python CPU, GPU | Deep dive on the Multinomial VAE algorithm and implementation.
 | [ncf_deep_dive](ncf_deep_dive.ipynb) | Python CPU, GPU | Deep dive on a NCF algorithm and implementation.