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Add documentation links to models in readme (#625)

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...@@ -143,67 +143,67 @@ The table below lists the recommendation models/algorithms featured in Cornac. E ...@@ -143,67 +143,67 @@ The table below lists the recommendation models/algorithms featured in Cornac. E
| Year | Model and Paper | Type | Environment | Example | | Year | Model and Paper | Type | Environment | Example |
| :--: | --------------- | :--: | :---------: | :-----: | | :--: | --------------- | :--: | :---------: | :-----: |
| 2024 | [Hypergraphs with Attention on Reviews (HypAR)](cornac/models/hypar), [paper](https://doi.org/10.1007/978-3-031-56027-9_14)| Hybrid / Sentiment / Explainable | [requirements](cornac/models/hypar/requirements_cu116.txt), CPU / GPU | [quick-start](https://github.com/PreferredAI/HypAR) | 2024 | [Hypergraphs with Attention on Reviews (HypAR)](cornac/models/hypar), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.hypar.recom_hypar), [paper](https://doi.org/10.1007/978-3-031-56027-9_14)| Hybrid / Sentiment / Explainable | [requirements](cornac/models/hypar/requirements_cu116.txt), CPU / GPU | [quick-start](https://github.com/PreferredAI/HypAR)
| 2022 | [Disentangled Multimodal Representation Learning for Recommendation (DMRL)](cornac/models/dmrl), [paper](https://arxiv.org/pdf/2203.05406.pdf) | Content-Based / Text & Image | [requirements](cornac/models/dmrl/requirements.txt), CPU / GPU | [quick-start](examples/dmrl_example.py) | 2022 | [Disentangled Multimodal Representation Learning for Recommendation (DMRL)](cornac/models/dmrl), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.dmrl.recom_dmrl), [paper](https://arxiv.org/pdf/2203.05406.pdf) | Content-Based / Text & Image | [requirements](cornac/models/dmrl/requirements.txt), CPU / GPU | [quick-start](examples/dmrl_example.py)
| 2021 | [Bilateral Variational Autoencoder for Collaborative Filtering (BiVAECF)](cornac/models/bivaecf), [paper](https://dl.acm.org/doi/pdf/10.1145/3437963.3441759) | Collaborative Filtering / Content-Based | [requirements](cornac/models/bivaecf/requirements.txt), CPU / GPU | [quick-start](https://github.com/PreferredAI/bi-vae), [deep-dive](https://github.com/recommenders-team/recommenders/blob/main/examples/02_model_collaborative_filtering/cornac_bivae_deep_dive.ipynb) | 2021 | [Bilateral Variational Autoencoder for Collaborative Filtering (BiVAECF)](cornac/models/bivaecf), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.bivaecf.recom_bivaecf), [paper](https://dl.acm.org/doi/pdf/10.1145/3437963.3441759) | Collaborative Filtering / Content-Based | [requirements](cornac/models/bivaecf/requirements.txt), CPU / GPU | [quick-start](https://github.com/PreferredAI/bi-vae), [deep-dive](https://github.com/recommenders-team/recommenders/blob/main/examples/02_model_collaborative_filtering/cornac_bivae_deep_dive.ipynb)
| | [Causal Inference for Visual Debiasing in Visually-Aware Recommendation (CausalRec)](cornac/models/causalrec), [paper](https://arxiv.org/abs/2107.02390) | Content-Based / Image | [requirements](cornac/models/causalrec/requirements.txt), CPU / GPU | [quick-start](examples/causalrec_clothing.py) | | [Causal Inference for Visual Debiasing in Visually-Aware Recommendation (CausalRec)](cornac/models/causalrec), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.causalrec.recom_causalrec), [paper](https://arxiv.org/abs/2107.02390) | Content-Based / Image | [requirements](cornac/models/causalrec/requirements.txt), CPU / GPU | [quick-start](examples/causalrec_clothing.py)
| | [Explainable Recommendation with Comparative Constraints on Product Aspects (ComparER)](cornac/models/comparer), [paper](https://dl.acm.org/doi/pdf/10.1145/3437963.3441754) | Explainable | CPU | [quick-start](https://github.com/PreferredAI/ComparER) | | [Explainable Recommendation with Comparative Constraints on Product Aspects (ComparER)](cornac/models/comparer), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.comparer.recom_comparer_sub), [paper](https://dl.acm.org/doi/pdf/10.1145/3437963.3441754) | Explainable | CPU | [quick-start](https://github.com/PreferredAI/ComparER)
| 2020 | [Adversarial Multimedia Recommendation (AMR)](cornac/models/amr), [paper](https://ieeexplore.ieee.org/document/8618394) | Content-Based / Image | [requirements](cornac/models/amr/requirements.txt), CPU / GPU | [quick-start](examples/amr_clothing.py) | 2020 | [Adversarial Multimedia Recommendation (AMR)](cornac/models/amr), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.amr.recom_amr), [paper](https://ieeexplore.ieee.org/document/8618394) | Content-Based / Image | [requirements](cornac/models/amr/requirements.txt), CPU / GPU | [quick-start](examples/amr_clothing.py)
| | [Hybrid Deep Representation Learning of Ratings and Reviews (HRDR)](cornac/models/hrdr), [paper](https://www.sciencedirect.com/science/article/abs/pii/S0925231219313207) | Content-Based / Text | [requirements](cornac/models/hrdr/requirements.txt), CPU / GPU | [quick-start](examples/hrdr_example.py) | | [Hybrid Deep Representation Learning of Ratings and Reviews (HRDR)](cornac/models/hrdr), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.hrdr.recom_hrdr), [paper](https://www.sciencedirect.com/science/article/abs/pii/S0925231219313207) | Content-Based / Text | [requirements](cornac/models/hrdr/requirements.txt), CPU / GPU | [quick-start](examples/hrdr_example.py)
| | [LightGCN: Simplifying and Powering Graph Convolution Network](cornac/models/lightgcn), [paper](https://arxiv.org/pdf/2002.02126.pdf) | Collaborative Filtering | [requirements](cornac/models/lightgcn/requirements.txt), CPU / GPU | [quick-start](examples/lightgcn_example.py) | | [LightGCN: Simplifying and Powering Graph Convolution Network](cornac/models/lightgcn), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.lightgcn.recom_lightgcn), [paper](https://arxiv.org/pdf/2002.02126.pdf) | Collaborative Filtering | [requirements](cornac/models/lightgcn/requirements.txt), CPU / GPU | [quick-start](examples/lightgcn_example.py)
| | [Predicting Temporal Sets with Deep Neural Networks (DNNTSP)](cornac/models/dnntsp), [paper](https://arxiv.org/pdf/2006.11483.pdf) | Next-Basket | [requirements](cornac/models/dnntsp/requirements.txt), CPU / GPU | [quick-start](examples/dnntsp_tafeng.py) | | [Predicting Temporal Sets with Deep Neural Networks (DNNTSP)](cornac/models/dnntsp), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.dnntsp.recom_dnntsp), [paper](https://arxiv.org/pdf/2006.11483.pdf) | Next-Basket | [requirements](cornac/models/dnntsp/requirements.txt), CPU / GPU | [quick-start](examples/dnntsp_tafeng.py)
| | [Recency Aware Collaborative Filtering (UPCF)](cornac/models/upcf), [paper](https://dl.acm.org/doi/abs/10.1145/3340631.3394850) | Next-Basket | [requirements](cornac/models/upcf/requirements.txt), CPU | [quick-start](examples/upcf_tafeng.py) | | [Recency Aware Collaborative Filtering (UPCF)](cornac/models/upcf), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.upcf.recom_upcf), [paper](https://dl.acm.org/doi/abs/10.1145/3340631.3394850) | Next-Basket | [requirements](cornac/models/upcf/requirements.txt), CPU | [quick-start](examples/upcf_tafeng.py)
| | [Temporal-Item-Frequency-based User-KNN (TIFUKNN)](cornac/models/tifuknn), [paper](https://arxiv.org/pdf/2006.00556.pdf) | Next-Basket | CPU | [quick-start](examples/tifuknn_tafeng.py) | | [Temporal-Item-Frequency-based User-KNN (TIFUKNN)](cornac/models/tifuknn), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.tifuknn.recom_tifuknn), [paper](https://arxiv.org/pdf/2006.00556.pdf) | Next-Basket | CPU | [quick-start](examples/tifuknn_tafeng.py)
| | [Variational Autoencoder for Top-N Recommendations (RecVAE)](cornac/models/recvae), [paper](https://doi.org/10.1145/3336191.3371831) | Collaborative Filtering | [requirements](cornac/models/recvae/requirements.txt), CPU / GPU | [quick-start](examples/recvae_example.py) | | [Variational Autoencoder for Top-N Recommendations (RecVAE)](cornac/models/recvae), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.recvae.recom_recvae), [paper](https://doi.org/10.1145/3336191.3371831) | Collaborative Filtering | [requirements](cornac/models/recvae/requirements.txt), CPU / GPU | [quick-start](examples/recvae_example.py)
| 2019 | [Correlation-Sensitive Next-Basket Recommendation (Beacon)](cornac/models/beacon), [paper](https://www.ijcai.org/proceedings/2019/0389.pdf) | Next-Basket | [requirements](cornac/models/beacon/requirements.txt), CPU / GPU | [quick-start](examples/beacon_tafeng.py) | 2019 | [Correlation-Sensitive Next-Basket Recommendation (Beacon)](cornac/models/beacon), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#correlation-sensitive-next-basket-recommendation-beacon), [paper](https://www.ijcai.org/proceedings/2019/0389.pdf) | Next-Basket | [requirements](cornac/models/beacon/requirements.txt), CPU / GPU | [quick-start](examples/beacon_tafeng.py)
| | [Embarrassingly Shallow Autoencoders for Sparse Data (EASEᴿ)](cornac/models/ease), [paper](https://arxiv.org/pdf/1905.03375.pdf) | Collaborative Filtering | CPU | [quick-start](examples/ease_movielens.py) | | [Embarrassingly Shallow Autoencoders for Sparse Data (EASEᴿ)](cornac/models/ease), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.ease.recom_ease), [paper](https://arxiv.org/pdf/1905.03375.pdf) | Collaborative Filtering | CPU | [quick-start](examples/ease_movielens.py)
| | [Neural Graph Collaborative Filtering (NGCF)](cornac/models/ngcf), [paper](https://arxiv.org/pdf/1905.08108.pdf) | Collaborative Filtering | [requirements](cornac/models/ngcf/requirements.txt), CPU / GPU | [quick-start](examples/ngcf_example.py) | | [Neural Graph Collaborative Filtering (NGCF)](cornac/models/ngcf), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.ngcf.recom_ngcf), [paper](https://arxiv.org/pdf/1905.08108.pdf) | Collaborative Filtering | [requirements](cornac/models/ngcf/requirements.txt), CPU / GPU | [quick-start](examples/ngcf_example.py)
| 2018 | [Collaborative Context Poisson Factorization (C2PF)](cornac/models/c2pf), [paper](https://www.ijcai.org/proceedings/2018/0370.pdf) | Content-Based / Graph | CPU | [quick-start](examples/c2pf_example.py) | 2018 | [Collaborative Context Poisson Factorization (C2PF)](cornac/models/c2pf), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.c2pf.recom_c2pf), [paper](https://www.ijcai.org/proceedings/2018/0370.pdf) | Content-Based / Graph | CPU | [quick-start](examples/c2pf_example.py)
| | [Graph Convolutional Matrix Completion (GCMC)](cornac/models/gcmc), [paper](https://www.kdd.org/kdd2018/files/deep-learning-day/DLDay18_paper_32.pdf) | Collaborative Filtering | [requirements](cornac/models/gcmc/requirements.txt), CPU / GPU | [quick-start](examples/gcmc_example.py) | | [Graph Convolutional Matrix Completion (GCMC)](cornac/models/gcmc), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.gcmc.recom_gcmc), [paper](https://www.kdd.org/kdd2018/files/deep-learning-day/DLDay18_paper_32.pdf) | Collaborative Filtering | [requirements](cornac/models/gcmc/requirements.txt), CPU / GPU | [quick-start](examples/gcmc_example.py)
| | [Multi-Task Explainable Recommendation (MTER)](cornac/models/mter), [paper](https://arxiv.org/pdf/1806.03568.pdf) | Explainable | CPU | [quick-start](examples/mter_example.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/07_explanations.ipynb) | | [Multi-Task Explainable Recommendation (MTER)](cornac/models/mter), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.mter.recom_mter), [paper](https://arxiv.org/pdf/1806.03568.pdf) | Explainable | CPU | [quick-start](examples/mter_example.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/07_explanations.ipynb)
| | [Neural Attention Rating Regression with Review-level Explanations (NARRE)](cornac/models/narre), [paper](http://www.thuir.cn/group/~YQLiu/publications/WWW2018_CC.pdf) | Explainable / Content-Based | [requirements](cornac/models/narre/requirements.txt), CPU / GPU | [quick-start](examples/narre_example.py) | | [Neural Attention Rating Regression with Review-level Explanations (NARRE)](cornac/models/narre), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.narre.recom_narre), [paper](http://www.thuir.cn/group/~YQLiu/publications/WWW2018_CC.pdf) | Explainable / Content-Based | [requirements](cornac/models/narre/requirements.txt), CPU / GPU | [quick-start](examples/narre_example.py)
| | [Probabilistic Collaborative Representation Learning (PCRL)](cornac/models/pcrl), [paper](http://www.hadylauw.com/publications/uai18.pdf) | Content-Based / Graph | [requirements](cornac/models/pcrl/requirements.txt), CPU / GPU | [quick-start](examples/pcrl_example.py) | | [Probabilistic Collaborative Representation Learning (PCRL)](cornac/models/pcrl), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.pcrl.recom_pcrl), [paper](http://www.hadylauw.com/publications/uai18.pdf) | Content-Based / Graph | [requirements](cornac/models/pcrl/requirements.txt), CPU / GPU | [quick-start](examples/pcrl_example.py)
| | [Variational Autoencoder for Collaborative Filtering (VAECF)](cornac/models/vaecf), [paper](https://arxiv.org/pdf/1802.05814.pdf) | Collaborative Filtering | [requirements](cornac/models/vaecf/requirements.txt), CPU / GPU | [quick-start](examples/vaecf_citeulike.py), [param-search](tutorials/param_search_vaecf.ipynb), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/09_deep_learning.ipynb) | | [Variational Autoencoder for Collaborative Filtering (VAECF)](cornac/models/vaecf), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.vaecf.recom_vaecf), [paper](https://arxiv.org/pdf/1802.05814.pdf) | Collaborative Filtering | [requirements](cornac/models/vaecf/requirements.txt), CPU / GPU | [quick-start](examples/vaecf_citeulike.py), [param-search](tutorials/param_search_vaecf.ipynb), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/09_deep_learning.ipynb)
| 2017 | [Collaborative Variational Autoencoder (CVAE)](cornac/models/cvae), [paper](http://eelxpeng.github.io/assets/paper/Collaborative_Variational_Autoencoder.pdf) | Content-Based / Text | [requirements](cornac/models/cvae/requirements.txt), CPU / GPU | [quick-start](examples/cvae_example.py) | 2017 | [Collaborative Variational Autoencoder (CVAE)](cornac/models/cvae), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.cvae.recom_cvae), [paper](http://eelxpeng.github.io/assets/paper/Collaborative_Variational_Autoencoder.pdf) | Content-Based / Text | [requirements](cornac/models/cvae/requirements.txt), CPU / GPU | [quick-start](examples/cvae_example.py)
| | [Conditional Variational Autoencoder for Collaborative Filtering (CVAECF)](cornac/models/cvaecf), [paper](https://dl.acm.org/doi/10.1145/3132847.3132972) | Content-Based / Text | [requirements](cornac/models/cvaecf/requirements.txt), CPU / GPU | [quick-start](examples/cvaecf_filmtrust.py) | | [Conditional Variational Autoencoder for Collaborative Filtering (CVAECF)](cornac/models/cvaecf), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.cvaecf.recom_cvaecf), [paper](https://dl.acm.org/doi/10.1145/3132847.3132972) | Content-Based / Text | [requirements](cornac/models/cvaecf/requirements.txt), CPU / GPU | [quick-start](examples/cvaecf_filmtrust.py)
| | [Generalized Matrix Factorization (GMF)](cornac/models/ncf), [paper](https://arxiv.org/pdf/1708.05031.pdf) | Collaborative Filtering | [requirements](cornac/models/ncf/requirements.txt), CPU / GPU | [quick-start](examples/ncf_example.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/09_deep_learning.ipynb) | | [Generalized Matrix Factorization (GMF)](cornac/models/ncf), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.ncf.recom_gmf), [paper](https://arxiv.org/pdf/1708.05031.pdf) | Collaborative Filtering | [requirements](cornac/models/ncf/requirements.txt), CPU / GPU | [quick-start](examples/ncf_example.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/09_deep_learning.ipynb)
| | [Indexable Bayesian Personalized Ranking (IBPR)](cornac/models/ibpr), [paper](http://www.hadylauw.com/publications/cikm17a.pdf) | Collaborative Filtering | [requirements](cornac/models/ibpr/requirements.txt), CPU / GPU | [quick-start](examples/ibpr_example.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/08_retrieval.ipynb) | | [Indexable Bayesian Personalized Ranking (IBPR)](cornac/models/ibpr), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.ibpr.recom_ibpr), [paper](http://www.hadylauw.com/publications/cikm17a.pdf) | Collaborative Filtering | [requirements](cornac/models/ibpr/requirements.txt), CPU / GPU | [quick-start](examples/ibpr_example.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/08_retrieval.ipynb)
| | [Matrix Co-Factorization (MCF)](cornac/models/mcf), [paper](https://dsail.kaist.ac.kr/files/WWW17.pdf) | Content-Based / Graph | CPU | [quick-start](examples/mcf_office.py), [cross-modality](https://github.com/lgabs/cornac/blob/luan/describe-gpu-supported-models-readme/tutorials/text_to_graph.ipynb) | | [Matrix Co-Factorization (MCF)](cornac/models/mcf), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.mcf.recom_mcf), [paper](https://dsail.kaist.ac.kr/files/WWW17.pdf) | Content-Based / Graph | CPU | [quick-start](examples/mcf_office.py), [cross-modality](https://github.com/lgabs/cornac/blob/luan/describe-gpu-supported-models-readme/tutorials/text_to_graph.ipynb)
| | [Multi-Layer Perceptron (MLP)](cornac/models/ncf), [paper](https://arxiv.org/pdf/1708.05031.pdf) | Collaborative Filtering | [requirements](cornac/models/ncf/requirements.txt), CPU / GPU | [quick-start](examples/ncf_example.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/09_deep_learning.ipynb) | | [Multi-Layer Perceptron (MLP)](cornac/models/ncf), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.ncf.recom_mlp), [paper](https://arxiv.org/pdf/1708.05031.pdf) | Collaborative Filtering | [requirements](cornac/models/ncf/requirements.txt), CPU / GPU | [quick-start](examples/ncf_example.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/09_deep_learning.ipynb)
| | [Neural Matrix Factorization (NeuMF) / Neural Collaborative Filtering (NCF)](cornac/models/ncf), [paper](https://arxiv.org/pdf/1708.05031.pdf) | Collaborative Filtering | [requirements](cornac/models/ncf/requirements.txt), CPU / GPU | [quick-start](examples/ncf_example.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/09_deep_learning.ipynb) | | [Neural Matrix Factorization (NeuMF) / Neural Collaborative Filtering (NCF)](cornac/models/ncf), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.ncf.recom_neumf), [paper](https://arxiv.org/pdf/1708.05031.pdf) | Collaborative Filtering | [requirements](cornac/models/ncf/requirements.txt), CPU / GPU | [quick-start](examples/ncf_example.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/09_deep_learning.ipynb)
| | [Online Indexable Bayesian Personalized Ranking (Online IBPR)](cornac/models/online_ibpr), [paper](http://www.hadylauw.com/publications/cikm17a.pdf) | Collaborative Filtering | [requirements](cornac/models/online_ibpr/requirements.txt), CPU / GPU | [quick-start](examples/ibpr_example.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/08_retrieval.ipynb) | | [Online Indexable Bayesian Personalized Ranking (Online IBPR)](cornac/models/online_ibpr), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.online_ibpr.recom_online_ibpr), [paper](http://www.hadylauw.com/publications/cikm17a.pdf) | Collaborative Filtering | [requirements](cornac/models/online_ibpr/requirements.txt), CPU / GPU | [quick-start](examples/ibpr_example.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/08_retrieval.ipynb)
| | [Visual Matrix Factorization (VMF)](cornac/models/vmf), [paper](https://dsail.kaist.ac.kr/files/WWW17.pdf) | Content-Based / Image | [requirements](cornac/models/vmf/requirements.txt), CPU / GPU | [quick-start](examples/vmf_clothing.py) | | [Visual Matrix Factorization (VMF)](cornac/models/vmf), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.vmf.recom_vmf), [paper](https://dsail.kaist.ac.kr/files/WWW17.pdf) | Content-Based / Image | [requirements](cornac/models/vmf/requirements.txt), CPU / GPU | [quick-start](examples/vmf_clothing.py)
| 2016 | [Collaborative Deep Ranking (CDR)](cornac/models/cdr), [paper](http://inpluslab.com/chenliang/homepagefiles/paper/hao-pakdd2016.pdf) | Content-Based / Text | [requirements](cornac/models/cdr/requirements.txt), CPU / GPU | [quick-start](examples/cdr_example.py) | 2016 | [Collaborative Deep Ranking (CDR)](cornac/models/cdr), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.cdr.recom_cdr), [paper](http://inpluslab.com/chenliang/homepagefiles/paper/hao-pakdd2016.pdf) | Content-Based / Text | [requirements](cornac/models/cdr/requirements.txt), CPU / GPU | [quick-start](examples/cdr_example.py)
| | [Collaborative Ordinal Embedding (COE)](cornac/models/coe), [paper](http://www.hadylauw.com/publications/sdm16.pdf) | Collaborative Filtering | [requirements](cornac/models/coe/requirements.txt), CPU / GPU | | | [Collaborative Ordinal Embedding (COE)](cornac/models/coe), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.coe.recom_coe), [paper](http://www.hadylauw.com/publications/sdm16.pdf) | Collaborative Filtering | [requirements](cornac/models/coe/requirements.txt), CPU / GPU |
| | [Convolutional Matrix Factorization (ConvMF)](cornac/models/conv_mf), [paper](http://uclab.khu.ac.kr/resources/publication/C_351.pdf) | Content-Based / Text | [requirements](cornac/models/conv_mf/requirements.txt), CPU / GPU | [quick-start](examples/conv_mf_example.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/09_deep_learning.ipynb) | | [Convolutional Matrix Factorization (ConvMF)](cornac/models/conv_mf), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.conv_mf.recom_convmf), [paper](http://uclab.khu.ac.kr/resources/publication/C_351.pdf) | Content-Based / Text | [requirements](cornac/models/conv_mf/requirements.txt), CPU / GPU | [quick-start](examples/conv_mf_example.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/09_deep_learning.ipynb)
| | [Learning to Rank Features for Recommendation over Multiple Categories (LRPPM)](cornac/models/lrppm), [paper](https://www.yongfeng.me/attach/sigir16-chen.pdf) | Explainable | CPU | [quick-start](examples/lrppm_example.py) | | [Learning to Rank Features for Recommendation over Multiple Categories (LRPPM)](cornac/models/lrppm), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#learn-to-rank-user-preferences-based-on-phrase-level-sentiment-analysis-across-multiple-categories-lrppm), [paper](https://www.yongfeng.me/attach/sigir16-chen.pdf) | Explainable | CPU | [quick-start](examples/lrppm_example.py)
| | [Session-based Recommendations With Recurrent Neural Networks (GRU4Rec)](cornac/models/gru4rec), [paper](https://arxiv.org/pdf/1511.06939.pdf) | Next-Item | [requirements](cornac/models/gru4rec/requirements.txt), CPU / GPU | [quick-start](examples/gru4rec_yoochoose.py) | | [Session-based Recommendations With Recurrent Neural Networks (GRU4Rec)](cornac/models/gru4rec), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.gru4rec.recom_gru4rec), [paper](https://arxiv.org/pdf/1511.06939.pdf) | Next-Item | [requirements](cornac/models/gru4rec/requirements.txt), CPU / GPU | [quick-start](examples/gru4rec_yoochoose.py)
| | [Spherical K-means (SKM)](cornac/models/skm), [paper](https://www.sciencedirect.com/science/article/pii/S092523121501509X) | Collaborative Filtering | CPU | [quick-start](examples/skm_movielens.py) | | [Spherical K-means (SKM)](cornac/models/skm), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.skm.recom_skmeans), [paper](https://www.sciencedirect.com/science/article/pii/S092523121501509X) | Collaborative Filtering | CPU | [quick-start](examples/skm_movielens.py)
| | [Visual Bayesian Personalized Ranking (VBPR)](cornac/models/vbpr), [paper](https://arxiv.org/pdf/1510.01784.pdf) | Content-Based / Image | [requirements](cornac/models/vbpr/requirements.txt), CPU / GPU | [quick-start](examples/vbpr_tradesy.py), [cross-modality](tutorials/vbpr_text.ipynb), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/05_multimodality.ipynb) | | [Visual Bayesian Personalized Ranking (VBPR)](cornac/models/vbpr), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.vbpr.recom_vbpr), [paper](https://arxiv.org/pdf/1510.01784.pdf) | Content-Based / Image | [requirements](cornac/models/vbpr/requirements.txt), CPU / GPU | [quick-start](examples/vbpr_tradesy.py), [cross-modality](tutorials/vbpr_text.ipynb), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/05_multimodality.ipynb)
| 2015 | [Collaborative Deep Learning (CDL)](cornac/models/cdl), [paper](https://arxiv.org/pdf/1409.2944.pdf) | Content-Based / Text | [requirements](cornac/models/cdl/requirements.txt), CPU / GPU | [quick-start](examples/cdl_example.py), [deep-dive](https://github.com/lgabs/cornac/blob/luan/describe-gpu-supported-models-readme/tutorials/working_with_auxiliary_data.md) | 2015 | [Collaborative Deep Learning (CDL)](cornac/models/cdl), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.cdl.recom_cdl), [paper](https://arxiv.org/pdf/1409.2944.pdf) | Content-Based / Text | [requirements](cornac/models/cdl/requirements.txt), CPU / GPU | [quick-start](examples/cdl_example.py), [deep-dive](https://github.com/lgabs/cornac/blob/luan/describe-gpu-supported-models-readme/tutorials/working_with_auxiliary_data.md)
| | [Hierarchical Poisson Factorization (HPF)](cornac/models/hpf), [paper](http://jakehofman.com/inprint/poisson_recs.pdf) | Collaborative Filtering | CPU | [quick-start](examples/hpf_movielens.py) | | [Hierarchical Poisson Factorization (HPF)](cornac/models/hpf), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.hpf.recom_hpf), [paper](http://jakehofman.com/inprint/poisson_recs.pdf) | Collaborative Filtering | CPU | [quick-start](examples/hpf_movielens.py)
| | [TriRank: Review-aware Explainable Recommendation by Modeling Aspects](cornac/models/trirank), [paper](https://wing.comp.nus.edu.sg/wp-content/uploads/Publications/PDF/TriRank-%20Review-aware%20Explainable%20Recommendation%20by%20Modeling%20Aspects.pdf) | Explainable | CPU | [quick-start](examples/trirank_example.py) | | [TriRank: Review-aware Explainable Recommendation by Modeling Aspects](cornac/models/trirank), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.trirank.recom_trirank), [paper](https://wing.comp.nus.edu.sg/wp-content/uploads/Publications/PDF/TriRank-%20Review-aware%20Explainable%20Recommendation%20by%20Modeling%20Aspects.pdf) | Explainable | CPU | [quick-start](examples/trirank_example.py)
| 2014 | [Explicit Factor Model (EFM)](cornac/models/efm), [paper](https://www.yongfeng.me/attach/efm-zhang.pdf) | Explainable | CPU | [quick-start](examples/efm_example.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/07_explanations.ipynb) | 2014 | [Explicit Factor Model (EFM)](cornac/models/efm), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.efm.recom_efm), [paper](https://www.yongfeng.me/attach/efm-zhang.pdf) | Explainable | CPU | [quick-start](examples/efm_example.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/07_explanations.ipynb)
| | [Social Bayesian Personalized Ranking (SBPR)](cornac/models/sbpr), [paper](https://cseweb.ucsd.edu/~jmcauley/pdfs/cikm14.pdf) | Content-Based / Social | CPU | [quick-start](examples/sbpr_epinions.py) | | [Social Bayesian Personalized Ranking (SBPR)](cornac/models/sbpr), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#social-bayesian-personalized-ranking-sbpr), [paper](https://cseweb.ucsd.edu/~jmcauley/pdfs/cikm14.pdf) | Content-Based / Social | CPU | [quick-start](examples/sbpr_epinions.py)
| 2013 | [Hidden Factors and Hidden Topics (HFT)](cornac/models/hft), [paper](https://cs.stanford.edu/people/jure/pubs/reviews-recsys13.pdf) | Content-Based / Text | CPU | [quick-start](examples/hft_example.py) | 2013 | [Hidden Factors and Hidden Topics (HFT)](cornac/models/hft), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.hft.recom_hft), [paper](https://cs.stanford.edu/people/jure/pubs/reviews-recsys13.pdf) | Content-Based / Text | CPU | [quick-start](examples/hft_example.py)
| 2012 | [Weighted Bayesian Personalized Ranking (WBPR)](cornac/models/bpr), [paper](http://proceedings.mlr.press/v18/gantner12a/gantner12a.pdf) | Collaborative Filtering | CPU | [quick-start](examples/bpr_netflix.py) | 2012 | [Weighted Bayesian Personalized Ranking (WBPR)](cornac/models/bpr), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#weighted-bayesian-personalized-ranking-wbpr), [paper](http://proceedings.mlr.press/v18/gantner12a/gantner12a.pdf) | Collaborative Filtering | CPU | [quick-start](examples/bpr_netflix.py)
| 2011 | [Collaborative Topic Regression (CTR)](cornac/models/ctr), [paper](http://www.cs.columbia.edu/~blei/papers/WangBlei2011.pdf) | Content-Based / Text | CPU | [quick-start](examples/ctr_example_citeulike.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/05_multimodality.ipynb) | 2011 | [Collaborative Topic Regression (CTR)](cornac/models/ctr), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.ctr.recom_ctr), [paper](http://www.cs.columbia.edu/~blei/papers/WangBlei2011.pdf) | Content-Based / Text | CPU | [quick-start](examples/ctr_example_citeulike.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/05_multimodality.ipynb)
| Earlier | [Baseline Only](cornac/models/baseline_only), [paper](http://courses.ischool.berkeley.edu/i290-dm/s11/SECURE/a1-koren.pdf) | Baseline | CPU | [quick-start](examples/svd_example.py) | Earlier | [Baseline Only](cornac/models/baseline_only), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#baseline-only), [paper](http://courses.ischool.berkeley.edu/i290-dm/s11/SECURE/a1-koren.pdf) | Baseline | CPU | [quick-start](examples/svd_example.py)
| | [Bayesian Personalized Ranking (BPR)](cornac/models/bpr), [paper](https://arxiv.org/ftp/arxiv/papers/1205/1205.2618.pdf) | Collaborative Filtering | CPU | [quick-start](examples/bpr_netflix.py), [deep-dive](https://github.com/recommenders-team/recommenders/blob/main/examples/02_model_collaborative_filtering/cornac_bpr_deep_dive.ipynb) | | [Bayesian Personalized Ranking (BPR)](cornac/models/bpr), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#bayesian-personalized-ranking-bpr) [paper](https://arxiv.org/ftp/arxiv/papers/1205/1205.2618.pdf) | Collaborative Filtering | CPU | [quick-start](examples/bpr_netflix.py), [deep-dive](https://github.com/recommenders-team/recommenders/blob/main/examples/02_model_collaborative_filtering/cornac_bpr_deep_dive.ipynb)
| | [Factorization Machines (FM)](cornac/models/fm), [paper](https://www.csie.ntu.edu.tw/~b97053/paper/Factorization%20Machines%20with%20libFM.pdf) | Collaborative Filtering / Content-Based | Linux, CPU | [quick-start](examples/fm_example.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/06_contextual_awareness.ipynb) | | [Factorization Machines (FM)](cornac/models/fm), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#factorization-machines-fm), [paper](https://www.csie.ntu.edu.tw/~b97053/paper/Factorization%20Machines%20with%20libFM.pdf) | Collaborative Filtering / Content-Based | Linux, CPU | [quick-start](examples/fm_example.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/06_contextual_awareness.ipynb)
| | [Global Average (GlobalAvg)](cornac/models/global_avg), [paper](https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf) | Baseline | CPU | [quick-start](examples/biased_mf.py) | | [Global Average (GlobalAvg)](cornac/models/global_avg), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.global_avg.recom_global_avg), [paper](https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf) | Baseline | CPU | [quick-start](examples/biased_mf.py)
| | [Global Personalized Top Frequent (GPTop)](cornac/models/gp_top), [paper](https://dl.acm.org/doi/pdf/10.1145/3587153) | Next-Basket | CPU | [quick-start](examples/gp_top_tafeng.py) | | [Global Personalized Top Frequent (GPTop)](cornac/models/gp_top), [paper](https://dl.acm.org/doi/pdf/10.1145/3587153) | Next-Basket | CPU | [quick-start](examples/gp_top_tafeng.py)
| | [Item K-Nearest-Neighbors (ItemKNN)](cornac/models/knn), [paper](https://dl.acm.org/doi/pdf/10.1145/371920.372071) | Neighborhood-Based | CPU | [quick-start](examples/knn_movielens.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/02_neighborhood.ipynb) | | [Item K-Nearest-Neighbors (ItemKNN)](cornac/models/knn), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#item-k-nearest-neighbors-itemknn), [paper](https://dl.acm.org/doi/pdf/10.1145/371920.372071) | Neighborhood-Based | CPU | [quick-start](examples/knn_movielens.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/02_neighborhood.ipynb)
| | [Matrix Factorization (MF)](cornac/models/mf), [paper](https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf) | Collaborative Filtering | CPU / GPU | [quick-start](examples/biased_mf.py), [pre-split-data](examples/given_data.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/03_matrix_factorization.ipynb) | | [Matrix Factorization (MF)](cornac/models/mf), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.mf.recom_mf), [paper](https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf) | Collaborative Filtering | CPU / GPU | [quick-start](examples/biased_mf.py), [pre-split-data](examples/given_data.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/03_matrix_factorization.ipynb)
| | [Maximum Margin Matrix Factorization (MMMF)](cornac/models/mmmf), [paper](https://link.springer.com/content/pdf/10.1007/s10994-008-5073-7.pdf) | Collaborative Filtering | CPU | [quick-start](examples/mmmf_exp.py) | | [Maximum Margin Matrix Factorization (MMMF)](cornac/models/mmmf), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.mmmf.recom_mmmf), [paper](https://link.springer.com/content/pdf/10.1007/s10994-008-5073-7.pdf) | Collaborative Filtering | CPU | [quick-start](examples/mmmf_exp.py)
| | [Most Popular (MostPop)](cornac/models/most_pop), [paper](https://arxiv.org/ftp/arxiv/papers/1205/1205.2618.pdf) | Baseline | CPU | [quick-start](examples/bpr_netflix.py) | | [Most Popular (MostPop)](cornac/models/most_pop), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.most_pop.recom_most_pop), [paper](https://arxiv.org/ftp/arxiv/papers/1205/1205.2618.pdf) | Baseline | CPU | [quick-start](examples/bpr_netflix.py)
| | [Non-negative Matrix Factorization (NMF)](cornac/models/nmf), [paper](http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf) | Collaborative Filtering | CPU | [quick-start](examples/nmf_example.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/03_matrix_factorization.ipynb) | | [Non-negative Matrix Factorization (NMF)](cornac/models/nmf), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.nmf.recom_nmf), [paper](http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf) | Collaborative Filtering | CPU | [quick-start](examples/nmf_example.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/03_matrix_factorization.ipynb)
| | [Probabilistic Matrix Factorization (PMF)](cornac/models/pmf), [paper](https://papers.nips.cc/paper/3208-probabilistic-matrix-factorization.pdf) | Collaborative Filtering | CPU | [quick-start](examples/pmf_ratio.py) | | [Probabilistic Matrix Factorization (PMF)](cornac/models/pmf), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.pmf.recom_pmf), [paper](https://papers.nips.cc/paper/3208-probabilistic-matrix-factorization.pdf) | Collaborative Filtering | CPU | [quick-start](examples/pmf_ratio.py)
| | [Session Popular (SPop)](cornac/models/spop), [paper](https://arxiv.org/pdf/1511.06939.pdf) | Next-Item / Baseline | CPU | [quick-start](examples/spop_yoochoose.py) | | [Session Popular (SPop)](cornac/models/spop), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.spop.recom_spop), [paper](https://arxiv.org/pdf/1511.06939.pdf) | Next-Item / Baseline | CPU | [quick-start](examples/spop_yoochoose.py)
| | [Singular Value Decomposition (SVD)](cornac/models/svd), [paper](https://people.engr.tamu.edu/huangrh/Spring16/papers_course/matrix_factorization.pdf) | Collaborative Filtering | CPU | [quick-start](examples/svd_example.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/03_matrix_factorization.ipynb) | | [Singular Value Decomposition (SVD)](cornac/models/svd), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.svd.recom_svd), [paper](https://people.engr.tamu.edu/huangrh/Spring16/papers_course/matrix_factorization.pdf) | Collaborative Filtering | CPU | [quick-start](examples/svd_example.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/03_matrix_factorization.ipynb)
| | [Social Recommendation using PMF (SoRec)](cornac/models/sorec), [paper](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.304.2464&rep=rep1&type=pdf) | Content-Based / Social | CPU | [quick-start](examples/sorec_filmtrust.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/05_multimodality.ipynb) | | [Social Recommendation using PMF (SoRec)](cornac/models/sorec), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.sorec.recom_sorec), [paper](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.304.2464&rep=rep1&type=pdf) | Content-Based / Social | CPU | [quick-start](examples/sorec_filmtrust.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/05_multimodality.ipynb)
| | [User K-Nearest-Neighbors (UserKNN)](cornac/models/knn), [paper](https://arxiv.org/pdf/1301.7363.pdf) | Neighborhood-Based | CPU | [quick-start](examples/knn_movielens.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/02_neighborhood.ipynb) | | [User K-Nearest-Neighbors (UserKNN)](cornac/models/knn), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#user-k-nearest-neighbors-userknn), [paper](https://arxiv.org/pdf/1301.7363.pdf) | Neighborhood-Based | CPU | [quick-start](examples/knn_movielens.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/02_neighborhood.ipynb)
| | [Weighted Matrix Factorization (WMF)](cornac/models/wmf), [paper](http://yifanhu.net/PUB/cf.pdf) | Collaborative Filtering | [requirements](cornac/models/wmf/requirements.txt), CPU / GPU | [quick-start](examples/wmf_example.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/04_implicit_feedback.ipynb) | | [Weighted Matrix Factorization (WMF)](cornac/models/wmf), [docs](https://cornac.readthedocs.io/en/stable/api_ref/models.html#module-cornac.models.wmf.recom_wmf), [paper](http://yifanhu.net/PUB/cf.pdf) | Collaborative Filtering | [requirements](cornac/models/wmf/requirements.txt), CPU / GPU | [quick-start](examples/wmf_example.py), [deep-dive](https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/04_implicit_feedback.ipynb)
## Resources ## Resources
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