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A Comparative Framework for Multimodal Recommender Systems

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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

Website | Documentation | Tutorials | Examples | Models | Datasets | Paper | Preferred.AI

.github/workflows/python-package.yml CircleCI AppVeyor Codecov Docs
Release PyPI Conda Conda Recipe Downloads
Python Conda Platforms License

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

For more details, please take a look at our examples as well as tutorials. For learning purposes, this list of tutorials on recommender systems will be more organized and comprehensive.

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 Neighor (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., MF).

Supported framework Cornac wrapper Examples
spotify/annoy AnnoyANN ann_example.py, ann_all.ipynb
meta/faiss FaissANN ann_example.py, ann_all.ipynb
nmslib/hnswlib HNSWLibANN ann_example.py, ann_hnswlib.ipynb, ann_all.ipynb
google/scann ScaNNANN ann_example.py, ann_all.ipynb

Models

The recommender models supported by Cornac are listed below. Why don't you join us to lengthen the list?

Year Model and paper Additional dependencies Examples
2021 Bilateral Variational Autoencoder for Collaborative Filtering (BiVAECF), paper requirements.txt PreferredAI/bi-vae
Causal Inference for Visual Debiasing in Visually-Aware Recommendation (CausalRec), paper requirements.txt causalrec_clothing.py
Explainable Recommendation with Comparative Constraints on Product Aspects (ComparER), paper N/A PreferredAI/ComparER
2020 Adversarial Training Towards Robust Multimedia Recommender System (AMR), paper requirements.txt amr_clothing.py
Hybrid neural recommendation with joint deep representation learning of ratings and reviews (HRDR), paper requirements.txt hrdr_example.py
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation, paper requirements.txt lightgcn_example.py
Temporal-Item-Frequency-based User-KNN (TIFUKNN), paper N/A tifuknn_tafeng.py
2019 Embarrassingly Shallow Autoencoders for Sparse Data (EASEᴿ), paper N/A ease_movielens.py
Neural Graph Collaborative Filtering (NGCF), paper requirements.txt ngcf_example.py
2018 Collaborative Context Poisson Factorization (C2PF), paper N/A c2pf_exp.py
Graph Convolutional Matrix Completion (GCMC), paper requirements.txt gcmc_example.py
Multi-Task Explainable Recommendation (MTER), paper N/A mter_exp.py
Neural Attention Rating Regression with Review-level Explanations (NARRE), paper requirements.txt narre_example.py
Probabilistic Collaborative Representation Learning (PCRL), paper requirements.txt pcrl_exp.py
Variational Autoencoder for Collaborative Filtering (VAECF), paper requirements.txt vaecf_citeulike.py
2017 Collaborative Variational Autoencoder (CVAE), paper requirements.txt cvae_exp.py
Conditional Variational Autoencoder for Collaborative Filtering (CVAECF), paper requirements.txt cvaecf_filmtrust.py
Generalized Matrix Factorization (GMF), paper requirements.txt ncf_exp.py
Indexable Bayesian Personalized Ranking (IBPR), paper requirements.txt ibpr_exp.py
Matrix Co-Factorization (MCF), paper N/A mcf_office.py
Multi-Layer Perceptron (MLP), paper requirements.txt ncf_exp.py
Neural Matrix Factorization (NeuMF) / Neural Collaborative Filtering (NCF), paper requirements.txt ncf_exp.py
Online Indexable Bayesian Personalized Ranking (Online IBPR), paper requirements.txt
Visual Matrix Factorization (VMF), paper requirements.txt vmf_clothing.py
2016 Collaborative Deep Ranking (CDR), paper requirements.txt cdr_exp.py
Collaborative Ordinal Embedding (COE), paper requirements.txt
Convolutional Matrix Factorization (ConvMF), paper requirements.txt convmf_exp.py
Learn to Rank user Preferences based on Phrase-level sentiment analysis across Multiple categories (LRPPM), paper N/A lrppm_example.py
Spherical K-means (SKM), paper N/A skm_movielens.py
Visual Bayesian Personalized Ranking (VBPR), paper requirements.txt vbpr_tradesy.py
2015 Collaborative Deep Learning (CDL), paper requirements.txt cdl_exp.py
Hierarchical Poisson Factorization (HPF), paper N/A hpf_movielens.py
TriRank: Review-aware Explainable Recommendation by Modeling Aspects, paper N/A trirank_example.py
2014 Explicit Factor Model (EFM), paper N/A efm_example.py
Social Bayesian Personalized Ranking (SBPR), paper N/A sbpr_epinions.py
2013 Hidden Factors and Hidden Topics (HFT), paper N/A hft_exp.py
2012 Weighted Bayesian Personalized Ranking (WBPR), paper N/A bpr_netflix.py
2011 Collaborative Topic Regression (CTR), paper N/A ctr_citeulike.py
Earlier Baseline Only, paper N/A svd_exp.py
Bayesian Personalized Ranking (BPR), paper N/A bpr_netflix.py
Factorization Machines (FM), paper Linux only fm_example.py
Global Average (GlobalAvg), paper N/A biased_mf.py
Global Personalized Top Frequent (GPTop), paper N/A gp_top_tafeng.py
Item K-Nearest-Neighbors (ItemKNN), paper N/A knn_movielens.py
Matrix Factorization (MF), paper N/A biased_mf.py, given_data.py
Maximum Margin Matrix Factorization (MMMF), paper N/A mmmf_exp.py
Most Popular (MostPop), paper N/A bpr_netflix.py
Non-negative Matrix Factorization (NMF), paper N/A nmf_exp.py
Probabilistic Matrix Factorization (PMF), paper N/A pmf_ratio.py
Singular Value Decomposition (SVD), paper N/A svd_exp.py
Social Recommendation using PMF (SoRec), paper N/A sorec_filmtrust.py
User K-Nearest-Neighbors (UserKNN), paper N/A knn_movielens.py
Weighted Matrix Factorization (WMF), paper requirements.txt wmf_exp.py

Contributing

This project welcomes contributions and suggestions. Before contributing, please see our contribution guidelines.

Citation

If you use Cornac in a scientific publication, we would appreciate citations to the following papers:

  • Cornac: A Comparative Framework for Multimodal Recommender Systems, Salah et al., Journal of Machine Learning Research, 21(95):1–5, 2020.

    @article{salah2020cornac,
      title={Cornac: A Comparative Framework for Multimodal Recommender Systems},
      author={Salah, Aghiles and Truong, Quoc-Tuan and Lauw, Hady W},
      journal={Journal of Machine Learning Research},
      volume={21},
      number={95},
      pages={1--5},
      year={2020}
    }
    
  • Exploring Cross-Modality Utilization in Recommender Systems, Truong et al., IEEE Internet Computing, 25(4):50–57, 2021.

    @article{truong2021exploring,
      title={Exploring Cross-Modality Utilization in Recommender Systems},
      author={Truong, Quoc-Tuan and Salah, Aghiles and Tran, Thanh-Binh and Guo, Jingyao and Lauw, Hady W},
      journal={IEEE Internet Computing},
      year={2021},
      publisher={IEEE}
    }
    
  • Multi-Modal Recommender Systems: Hands-On Exploration, Truong et al., ACM Conference on Recommender Systems, 2021.

    @inproceedings{truong2021multi,
      title={Multi-modal recommender systems: Hands-on exploration},
      author={Truong, Quoc-Tuan and Salah, Aghiles and Lauw, Hady},
      booktitle={Fifteenth ACM Conference on Recommender Systems},
      pages={834--837},
      year={2021}
    }
    

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Apache License 2.0

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