Cornac

Cornac is a comparative framework for multimodal recommender systems. It focuses on making it convenient to work with models leveraging auxiliary data (e.g., item descriptive text and image, social network, etc). Cornac enables fast experiments and straightforward implementations of new models. It is highly compatible with existing machine learning libraries (e.g., TensorFlow, PyTorch).

Cornac is one of the frameworks recommended by ACM RecSys 2023 for the evaluation and reproducibility of recommendation algorithms.

Quick Links

Installation

Currently, we are supporting Python 3. There are several ways to install Cornac:

From PyPI (recommended):
```
pip3 install cornac
```
From Anaconda:
```
conda install cornac -c conda-forge
```

From the GitHub source (for latest updates):

pip3 install Cython numpy scipy
pip3 install git+https://github.com/PreferredAI/cornac.git

Note:

Additional dependencies required by models are listed here.

Some algorithm implementations use OpenMP to support multi-threading. For Mac OS users, in order to run those algorithms efficiently, you might need to install gcc from Homebrew to have an OpenMP compiler:

brew install gcc | brew link gcc

Getting started: your first Cornac experiment

Flow of an Experiment in Cornac

import cornac
from cornac.eval_methods import RatioSplit
from cornac.models import MF, PMF, BPR
from cornac.metrics import MAE, RMSE, Precision, Recall, NDCG, AUC, MAP

# load the built-in MovieLens 100K and split the data based on ratio
ml_100k = cornac.datasets.movielens.load_feedback()
rs = RatioSplit(data=ml_100k, test_size=0.2, rating_threshold=4.0, seed=123)

# initialize models, here we are comparing: Biased MF, PMF, and BPR
mf = MF(k=10, max_iter=25, learning_rate=0.01, lambda_reg=0.02, use_bias=True, seed=123)
pmf = PMF(k=10, max_iter=100, learning_rate=0.001, lambda_reg=0.001, seed=123)
bpr = BPR(k=10, max_iter=200, learning_rate=0.001, lambda_reg=0.01, seed=123)
models = [mf, pmf, bpr]

# define metrics to evaluate the models
metrics = [MAE(), RMSE(), Precision(k=10), Recall(k=10), NDCG(k=10), AUC(), MAP()]

# put it together in an experiment, voilà!
cornac.Experiment(eval_method=rs, models=models, metrics=metrics, user_based=True).run()

Output:

	MAE	RMSE	AUC	MAP	NDCG@10	Precision@10	Recall@10	Train (s)	Test (s)
MF	0.7430	0.8998	0.7445	0.0548	0.0761	0.0675	0.0463	0.13	1.57
PMF	0.7534	0.9138	0.7744	0.0671	0.0969	0.0813	0.0639	2.18	1.64
BPR	N/A	N/A	0.8695	0.1042	0.1500	0.1110	0.1195	3.74	1.49

Model serving

Here, we provide a simple way to serve a Cornac model by launching a standalone web service with Flask. It is very handy for testing or creating a demo application. First, we install the dependency:

$ pip3 install Flask

Supposed that we want to serve the trained BPR model from previous example, we need to save it:

bpr.save("save_dir", save_trainset=True)

After that, the model can be deployed easily by running Cornac serving app as follows:

$ FLASK_APP='cornac.serving.app' \
  MODEL_PATH='save_dir/BPR' \
  MODEL_CLASS='cornac.models.BPR' \
  flask run --host localhost --port 8080

# Running on http://localhost:8080

Here we go, our model service is now ready. Let's get top-5 item recommendations for the user "63":

$ curl -X GET "http://localhost:8080/recommend?uid=63&k=5&remove_seen=false"

# Response: {"recommendations": ["50", "181", "100", "258", "286"], "query": {"uid": "63", "k": 5, "remove_seen": false}}

If we want to remove seen items during training, we need to provide TRAIN_SET which has been saved with the model earlier, when starting the serving app. We can also leverage WSGI server for model deployment in production. Please refer to this guide for more details.

Efficient retrieval with ANN search

One important aspect of deploying recommender model is efficient retrieval via Approximate Nearest Neighbor (ANN) search in vector space. Cornac integrates several vector similarity search frameworks for the ease of deployment. This example demonstrates how ANN search will work seamlessly with any recommender models supporting it (e.g., matrix factorization).

Supported Framework	Cornac Wrapper	Example
spotify/annoy	AnnoyANN	quick-start, deep-dive
meta/faiss	FaissANN	quick-start, deep-dive
nmslib/hnswlib	HNSWLibANN	quick-start, hnsw-lib, deep-dive
google/scann	ScaNNANN	quick-start, deep-dive

Models

The table below lists the recommendation models/algorithms featured in Cornac. Examples are provided as quick-start showcasing an easy to run script, or as deep-dive explaining the math and intuition behind each model. Why don't you join us to lengthen the list?

Year	Model and Paper	Type	Environment	Example
2024	Hypergraphs with Attention on Reviews (HypAR), docs, paper	Hybrid / Sentiment / Explainable	requirements, CPU / GPU	quick-start
2022	Disentangled Multimodal Representation Learning for Recommendation (DMRL), docs, paper	Content-Based / Text & Image	requirements, CPU / GPU	quick-start
2021	Bilateral Variational Autoencoder for Collaborative Filtering (BiVAECF), docs, paper	Collaborative Filtering / Content-Based	requirements, CPU / GPU	quick-start, deep-dive
	Causal Inference for Visual Debiasing in Visually-Aware Recommendation (CausalRec), docs, paper	Content-Based / Image	requirements, CPU / GPU	quick-start
	Explainable Recommendation with Comparative Constraints on Product Aspects (ComparER), docs, paper	Explainable	CPU	quick-start
2020	Adversarial Multimedia Recommendation (AMR), docs, paper	Content-Based / Image	requirements, CPU / GPU	quick-start
	Hybrid Deep Representation Learning of Ratings and Reviews (HRDR), docs, paper	Content-Based / Text	requirements, CPU / GPU	quick-start
	LightGCN: Simplifying and Powering Graph Convolution Network, docs, paper	Collaborative Filtering	requirements, CPU / GPU	quick-start
	Predicting Temporal Sets with Deep Neural Networks (DNNTSP), docs, paper	Next-Basket	requirements, CPU / GPU	quick-start
	Recency Aware Collaborative Filtering (UPCF), docs, paper	Next-Basket	requirements, CPU	quick-start
	Temporal-Item-Frequency-based User-KNN (TIFUKNN), docs, paper	Next-Basket	CPU	quick-start
	Variational Autoencoder for Top-N Recommendations (RecVAE), docs, paper	Collaborative Filtering	requirements, CPU / GPU	quick-start
2019	Correlation-Sensitive Next-Basket Recommendation (Beacon), docs, paper	Next-Basket	requirements, CPU / GPU	quick-start
	[Embarrassingly Shallow Autoencoders for Sparse Data