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Unverified Commit 92a94e38 authored by Trung-Hoang Le's avatar Trung-Hoang Le Committed by GitHub
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Add column model type into table models (#588) 


* Add colunm model type into table models

* Update README.md

* Shorten requirements to give more space for other columns

* Shorten a few model names

* Update README.md

* Shorten LRPPM model title

* Shorten examples

---------

Co-authored-by: default avatarQuoc-Tuan Truong <tqtg@users.noreply.github.com>
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...@@ -144,67 +144,68 @@ One important aspect of deploying recommender model is efficient retrieval via A ...@@ -144,67 +144,68 @@ One important aspect of deploying recommender model is efficient retrieval via A
The recommender models supported by Cornac are listed below. Why don't you join us to lengthen the list? 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 |
| :---: | --- | :---: | :---: | | Year | Model and paper | Model type | Require-ments | Examples |
| 2021 | [Bilateral Variational Autoencoder for Collaborative Filtering (BiVAECF)](cornac/models/bivaecf), [paper](https://dl.acm.org/doi/pdf/10.1145/3437963.3441759) | [requirements.txt](cornac/models/bivaecf/requirements.txt) | [PreferredAI/bi-vae](https://github.com/PreferredAI/bi-vae) | :---: | --- | :---: | :---: | :---: |
| | [Causal Inference for Visual Debiasing in Visually-Aware Recommendation (CausalRec)](cornac/models/causalrec), [paper](https://arxiv.org/abs/2107.02390) | [requirements.txt](cornac/models/causalrec/requirements.txt) | [causalrec_clothing.py](examples/causalrec_clothing.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 | [reqs](cornac/models/bivaecf/requirements.txt) | [exp](https://github.com/PreferredAI/bi-vae)
| | [Explainable Recommendation with Comparative Constraints on Product Aspects (ComparER)](cornac/models/comparer), [paper](https://dl.acm.org/doi/pdf/10.1145/3437963.3441754) | N/A | [PreferredAI/ComparER](https://github.com/PreferredAI/ComparER) | | [Causal Inference for Visual Debiasing in Visually-Aware Recommendation (CausalRec)](cornac/models/causalrec), [paper](https://arxiv.org/abs/2107.02390) | Content-Based / Image | [reqs](cornac/models/causalrec/requirements.txt) | [exp](examples/causalrec_clothing.py)
| 2020 | [Adversarial Training Towards Robust Multimedia Recommender System (AMR)](cornac/models/amr), [paper](https://ieeexplore.ieee.org/document/8618394) | [requirements.txt](cornac/models/amr/requirements.txt) | [amr_clothing.py](examples/amr_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 | N/A | [exp](https://github.com/PreferredAI/ComparER)
| | [Hybrid neural recommendation with joint deep representation learning of ratings and reviews (HRDR)](cornac/models/hrdr), [paper](https://www.sciencedirect.com/science/article/abs/pii/S0925231219313207) | [requirements.txt](cornac/models/hrdr/requirements.txt) | [hrdr_example.py](examples/hrdr_example.py) | 2020 | [Adversarial Multimedia Recommendation (AMR)](cornac/models/amr), [paper](https://ieeexplore.ieee.org/document/8618394) | Content-Based / Image | [reqs](cornac/models/amr/requirements.txt) | [exp](examples/amr_clothing.py)
| | [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](cornac/models/lightgcn), [paper](https://arxiv.org/pdf/2002.02126.pdf) | [requirements.txt](cornac/models/lightgcn/requirements.txt) | [lightgcn_example.py](examples/lightgcn_example.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 | [reqs](cornac/models/hrdr/requirements.txt) | [exp](examples/hrdr_example.py)
| | [New Variational Autoencoder for Top-N Recommendations with Implicit Feedback (RecVAE)](cornac/models/recvae), [paper](https://doi.org/10.1145/3336191.3371831) | [requirements.txt](cornac/models/recvae/requirements.txt) | [recvae_example.py](examples/recvae_example.py) | | [LightGCN: Simplifying and Powering Graph Convolution Network](cornac/models/lightgcn), [paper](https://arxiv.org/pdf/2002.02126.pdf) | Collaborative Filtering | [reqs](cornac/models/lightgcn/requirements.txt) | [exp](examples/lightgcn_example.py)
| | [Predicting Temporal Sets with Deep Neural Networks (DNNTSP)](cornac/models/dnntsp), [paper](https://arxiv.org/pdf/2006.11483.pdf) | [requirements.txt](cornac/models/dnntsp/requirements.txt) | [dnntsp_tafeng.py](examples/dnntsp_tafeng.py) | | [Predicting Temporal Sets with Deep Neural Networks (DNNTSP)](cornac/models/dnntsp), [paper](https://arxiv.org/pdf/2006.11483.pdf) | Next-Basket | [reqs](cornac/models/dnntsp/requirements.txt) | [exp](examples/dnntsp_tafeng.py)
| | [Recency Aware Collaborative Filtering for Next Basket Recommendation (UPCF)](cornac/models/upcf), [paper](https://dl.acm.org/doi/abs/10.1145/3340631.3394850) | [requirements.txt](cornac/models/upcf/requirements.txt) | [upcf_tafeng.py](examples/upcf_tafeng.py) | | [Recency Aware Collaborative Filtering (UPCF)](cornac/models/upcf), [paper](https://dl.acm.org/doi/abs/10.1145/3340631.3394850) | Next-Basket | [reqs](cornac/models/upcf/requirements.txt) | [exp](examples/upcf_tafeng.py)
| | [Temporal-Item-Frequency-based User-KNN (TIFUKNN)](cornac/models/tifuknn), [paper](https://arxiv.org/pdf/2006.00556.pdf) | N/A | [tifuknn_tafeng.py](examples/tifuknn_tafeng.py) | | [Temporal-Item-Frequency-based User-KNN (TIFUKNN)](cornac/models/tifuknn), [paper](https://arxiv.org/pdf/2006.00556.pdf) | Next-Basket | N/A | [exp](examples/tifuknn_tafeng.py)
| 2019 | [Correlation-Sensitive Next-Basket Recommendation (Beacon)](cornac/models/beacon), [paper](https://www.ijcai.org/proceedings/2019/0389.pdf) | [requirements.txt](cornac/models/beacon/requirements.txt) | [beacon_tafeng.py](examples/beacon_tafeng.py) | | [Variational Autoencoder for Top-N Recommendations (RecVAE)](cornac/models/recvae), [paper](https://doi.org/10.1145/3336191.3371831) | Collaborative Filtering | [reqs](cornac/models/recvae/requirements.txt) | [exp](examples/recvae_example.py)
| | [Embarrassingly Shallow Autoencoders for Sparse Data (EASEᴿ)](cornac/models/ease), [paper](https://arxiv.org/pdf/1905.03375.pdf) | N/A | [ease_movielens.py](examples/ease_movielens.py) | 2019 | [Correlation-Sensitive Next-Basket Recommendation (Beacon)](cornac/models/beacon), [paper](https://www.ijcai.org/proceedings/2019/0389.pdf) | Next-Basket | [reqs](cornac/models/beacon/requirements.txt) | [exp](examples/beacon_tafeng.py)
| | [Neural Graph Collaborative Filtering (NGCF)](cornac/models/ngcf), [paper](https://arxiv.org/pdf/1905.08108.pdf) | [requirements.txt](cornac/models/ngcf/requirements.txt) | [ngcf_example.py](examples/ngcf_example.py) | | [Embarrassingly Shallow Autoencoders for Sparse Data (EASEᴿ)](cornac/models/ease), [paper](https://arxiv.org/pdf/1905.03375.pdf) | Collaborative Filtering | N/A | [exp](examples/ease_movielens.py)
| 2018 | [Collaborative Context Poisson Factorization (C2PF)](cornac/models/c2pf), [paper](https://www.ijcai.org/proceedings/2018/0370.pdf) | N/A | [c2pf_exp.py](examples/c2pf_example.py) | | [Neural Graph Collaborative Filtering (NGCF)](cornac/models/ngcf), [paper](https://arxiv.org/pdf/1905.08108.pdf) | Collaborative Filtering | [reqs](cornac/models/ngcf/requirements.txt) | [exp](examples/ngcf_example.py)
| | [Graph Convolutional Matrix Completion (GCMC)](cornac/models/gcmc), [paper](https://www.kdd.org/kdd2018/files/deep-learning-day/DLDay18_paper_32.pdf) | [requirements.txt](cornac/models/gcmc/requirements.txt) | [gcmc_example.py](examples/gcmc_example.py) | 2018 | [Collaborative Context Poisson Factorization (C2PF)](cornac/models/c2pf), [paper](https://www.ijcai.org/proceedings/2018/0370.pdf) | Content-Based / Graph | N/A | [exp](examples/c2pf_example.py)
| | [Multi-Task Explainable Recommendation (MTER)](cornac/models/mter), [paper](https://arxiv.org/pdf/1806.03568.pdf) | N/A | [mter_exp.py](examples/mter_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 | [reqs](cornac/models/gcmc/requirements.txt) | [exp](examples/gcmc_example.py)
| | [Neural Attention Rating Regression with Review-level Explanations (NARRE)](cornac/models/narre), [paper](http://www.thuir.cn/group/~YQLiu/publications/WWW2018_CC.pdf) | [requirements.txt](cornac/models/narre/requirements.txt) | [narre_example.py](examples/narre_example.py) | | [Multi-Task Explainable Recommendation (MTER)](cornac/models/mter), [paper](https://arxiv.org/pdf/1806.03568.pdf) | Explainable | N/A | [exp](examples/mter_example.py)
| | [Probabilistic Collaborative Representation Learning (PCRL)](cornac/models/pcrl), [paper](http://www.hadylauw.com/publications/uai18.pdf) | [requirements.txt](cornac/models/pcrl/requirements.txt) | [pcrl_exp.py](examples/pcrl_example.py) | | [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 | [reqs](cornac/models/narre/requirements.txt) | [exp](examples/narre_example.py)
| | [Variational Autoencoder for Collaborative Filtering (VAECF)](cornac/models/vaecf), [paper](https://arxiv.org/pdf/1802.05814.pdf) | [requirements.txt](cornac/models/vaecf/requirements.txt) | [vaecf_citeulike.py](examples/vaecf_citeulike.py) | | [Probabilistic Collaborative Representation Learning (PCRL)](cornac/models/pcrl), [paper](http://www.hadylauw.com/publications/uai18.pdf) | Content-Based / Graph | [reqs](cornac/models/pcrl/requirements.txt) | [exp](examples/pcrl_example.py)
| 2017 | [Collaborative Variational Autoencoder (CVAE)](cornac/models/cvae), [paper](http://eelxpeng.github.io/assets/paper/Collaborative_Variational_Autoencoder.pdf) | [requirements.txt](cornac/models/cvae/requirements.txt) | [cvae_exp.py](examples/cvae_example.py) | | [Variational Autoencoder for Collaborative Filtering (VAECF)](cornac/models/vaecf), [paper](https://arxiv.org/pdf/1802.05814.pdf) | Collaborative Filtering | [reqs](cornac/models/vaecf/requirements.txt) | [exp](examples/vaecf_citeulike.py)
| | [Conditional Variational Autoencoder for Collaborative Filtering (CVAECF)](cornac/models/cvaecf), [paper](https://seslab.kaist.ac.kr/xe2/?module=file&act=procFileDownload&file_srl=18019&sid=4be19b9d0134a4aeacb9ef1ecd81c784&module_srl=1379) | [requirements.txt](cornac/models/cvaecf/requirements.txt) | [cvaecf_filmtrust.py](examples/cvaecf_filmtrust.py) | 2017 | [Collaborative Variational Autoencoder (CVAE)](cornac/models/cvae), [paper](http://eelxpeng.github.io/assets/paper/Collaborative_Variational_Autoencoder.pdf) | Content-Based / Text | [reqs](cornac/models/cvae/requirements.txt) | [exp](examples/cvae_example.py)
| | [Generalized Matrix Factorization (GMF)](cornac/models/ncf), [paper](https://arxiv.org/pdf/1708.05031.pdf) | [requirements.txt](cornac/models/ncf/requirements.txt) | [ncf_exp.py](examples/ncf_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 | [reqs](cornac/models/cvaecf/requirements.txt) | [exp](examples/cvaecf_filmtrust.py)
| | [Indexable Bayesian Personalized Ranking (IBPR)](cornac/models/ibpr), [paper](http://www.hadylauw.com/publications/cikm17a.pdf) | [requirements.txt](cornac/models/ibpr/requirements.txt) | [ibpr_exp.py](examples/ibpr_example.py) | | [Generalized Matrix Factorization (GMF)](cornac/models/ncf), [paper](https://arxiv.org/pdf/1708.05031.pdf) | Collaborative Filtering | [reqs](cornac/models/ncf/requirements.txt) | [exp](examples/ncf_example.py)
| | [Matrix Co-Factorization (MCF)](cornac/models/mcf), [paper](http://papers.www2017.com.au.s3-website-ap-southeast-2.amazonaws.com/proceedings/p1113.pdf) | N/A | [mcf_office.py](examples/mcf_office.py) | | [Indexable Bayesian Personalized Ranking (IBPR)](cornac/models/ibpr), [paper](http://www.hadylauw.com/publications/cikm17a.pdf) | Collaborative Filtering | [reqs](cornac/models/ibpr/requirements.txt) | [exp](examples/ibpr_example.py)
| | [Multi-Layer Perceptron (MLP)](cornac/models/ncf), [paper](https://arxiv.org/pdf/1708.05031.pdf) | [requirements.txt](cornac/models/ncf/requirements.txt) | [ncf_exp.py](examples/ncf_example.py) | | [Matrix Co-Factorization (MCF)](cornac/models/mcf), [paper](https://dsail.kaist.ac.kr/files/WWW17.pdf) | Content-Based / Graph | N/A | [exp](examples/mcf_office.py)
| | [Neural Matrix Factorization (NeuMF) / Neural Collaborative Filtering (NCF)](cornac/models/ncf), [paper](https://arxiv.org/pdf/1708.05031.pdf) | [requirements.txt](cornac/models/ncf/requirements.txt) | [ncf_exp.py](examples/ncf_example.py) | | [Multi-Layer Perceptron (MLP)](cornac/models/ncf), [paper](https://arxiv.org/pdf/1708.05031.pdf) | Collaborative Filtering | [reqs](cornac/models/ncf/requirements.txt) | [exp](examples/ncf_example.py)
| | [Online Indexable Bayesian Personalized Ranking (Online IBPR)](cornac/models/online_ibpr), [paper](http://www.hadylauw.com/publications/cikm17a.pdf) | [requirements.txt](cornac/models/online_ibpr/requirements.txt) | | | [Neural Matrix Factorization (NeuMF) / Neural Collaborative Filtering (NCF)](cornac/models/ncf), [paper](https://arxiv.org/pdf/1708.05031.pdf) | Collaborative Filtering | [reqs](cornac/models/ncf/requirements.txt) | [exp](examples/ncf_example.py)
| | [Visual Matrix Factorization (VMF)](cornac/models/vmf), [paper](http://papers.www2017.com.au.s3-website-ap-southeast-2.amazonaws.com/proceedings/p1113.pdf) | [requirements.txt](cornac/models/vmf/requirements.txt) | [vmf_clothing.py](examples/vmf_clothing.py) | | [Online Indexable Bayesian Personalized Ranking (Online IBPR)](cornac/models/online_ibpr), [paper](http://www.hadylauw.com/publications/cikm17a.pdf) | Collaborative Filtering | [reqs](cornac/models/online_ibpr/requirements.txt) |
| 2016 | [Collaborative Deep Ranking (CDR)](cornac/models/cdr), [paper](http://inpluslab.com/chenliang/homepagefiles/paper/hao-pakdd2016.pdf) | [requirements.txt](cornac/models/cdr/requirements.txt) | [cdr_exp.py](examples/cdr_example.py) | | [Visual Matrix Factorization (VMF)](cornac/models/vmf), [paper](https://dsail.kaist.ac.kr/files/WWW17.pdf) | Content-Based / Image | [reqs](cornac/models/vmf/requirements.txt) | [exp](examples/vmf_clothing.py)
| | [Collaborative Ordinal Embedding (COE)](cornac/models/coe), [paper](http://www.hadylauw.com/publications/sdm16.pdf) | [requirements.txt](cornac/models/coe/requirements.txt) | | 2016 | [Collaborative Deep Ranking (CDR)](cornac/models/cdr), [paper](http://inpluslab.com/chenliang/homepagefiles/paper/hao-pakdd2016.pdf) | Content-Based / Text | [reqs](cornac/models/cdr/requirements.txt) | [exp](examples/cdr_example.py)
| | [Convolutional Matrix Factorization (ConvMF)](cornac/models/conv_mf), [paper](http://uclab.khu.ac.kr/resources/publication/C_351.pdf) | [requirements.txt](cornac/models/conv_mf/requirements.txt) | [convmf_exp.py](examples/conv_mf_example.py) | | [Collaborative Ordinal Embedding (COE)](cornac/models/coe), [paper](http://www.hadylauw.com/publications/sdm16.pdf) | Collaborative Filtering | [reqs](cornac/models/coe/requirements.txt) |
| | [Learn to Rank user Preferences based on Phrase-level sentiment analysis across Multiple categories (LRPPM)](cornac/models/lrppm), [paper](https://www.yongfeng.me/attach/sigir16-chen.pdf) | N/A | [lrppm_example.py](examples/lrppm_example.py) | | [Convolutional Matrix Factorization (ConvMF)](cornac/models/conv_mf), [paper](http://uclab.khu.ac.kr/resources/publication/C_351.pdf) | Content-Based / Text | [reqs](cornac/models/conv_mf/requirements.txt) | [exp](examples/conv_mf_example.py)
| | [Session-based Recommendations With Recurrent Neural Networks (GRU4Rec)](cornac/models/gru4rec), [paper](https://arxiv.org/pdf/1511.06939.pdf) | [requirements.txt](cornac/models/gru4rec/requirements.txt) | [gru4rec_yoochoose.py](examples/gru4rec_yoochoose.py) | | [Learning to Rank Features for Recommendation over Multiple Categories (LRPPM)](cornac/models/lrppm), [paper](https://www.yongfeng.me/attach/sigir16-chen.pdf) | Explainable | N/A | [exp](examples/lrppm_example.py)
| | [Spherical K-means (SKM)](cornac/models/skm), [paper](https://www.sciencedirect.com/science/article/pii/S092523121501509X) | N/A | [skm_movielens.py](examples/skm_movielens.py) | | [Session-based Recommendations With Recurrent Neural Networks (GRU4Rec)](cornac/models/gru4rec), [paper](https://arxiv.org/pdf/1511.06939.pdf) | Next-Item | [reqs](cornac/models/gru4rec/requirements.txt) | [exp](examples/gru4rec_yoochoose.py)
| | [Visual Bayesian Personalized Ranking (VBPR)](cornac/models/vbpr), [paper](https://arxiv.org/pdf/1510.01784.pdf) | [requirements.txt](cornac/models/vbpr/requirements.txt) | [vbpr_tradesy.py](examples/vbpr_tradesy.py) | | [Spherical K-means (SKM)](cornac/models/skm), [paper](https://www.sciencedirect.com/science/article/pii/S092523121501509X) | Collaborative Filtering | N/A | [exp](examples/skm_movielens.py)
| 2015 | [Collaborative Deep Learning (CDL)](cornac/models/cdl), [paper](https://arxiv.org/pdf/1409.2944.pdf) | [requirements.txt](cornac/models/cdl/requirements.txt) | [cdl_exp.py](examples/cdl_example.py) | | [Visual Bayesian Personalized Ranking (VBPR)](cornac/models/vbpr), [paper](https://arxiv.org/pdf/1510.01784.pdf) | Content-Based / Image | [reqs](cornac/models/vbpr/requirements.txt) | [exp](examples/vbpr_tradesy.py)
| | [Hierarchical Poisson Factorization (HPF)](cornac/models/hpf), [paper](http://jakehofman.com/inprint/poisson_recs.pdf) | N/A | [hpf_movielens.py](examples/hpf_movielens.py) | 2015 | [Collaborative Deep Learning (CDL)](cornac/models/cdl), [paper](https://arxiv.org/pdf/1409.2944.pdf) | Content-Based / Text | [reqs](cornac/models/cdl/requirements.txt) | [exp](examples/cdl_example.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) | N/A | [trirank_example.py](examples/trirank_example.py) | | [Hierarchical Poisson Factorization (HPF)](cornac/models/hpf), [paper](http://jakehofman.com/inprint/poisson_recs.pdf) | Collaborative Filtering | N/A | [exp](examples/hpf_movielens.py)
| 2014 | [Explicit Factor Model (EFM)](cornac/models/efm), [paper](https://www.yongfeng.me/attach/efm-zhang.pdf) | N/A | [efm_example.py](examples/efm_example.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 | N/A | [exp](examples/trirank_example.py)
| | [Social Bayesian Personalized Ranking (SBPR)](cornac/models/sbpr), [paper](https://cseweb.ucsd.edu/~jmcauley/pdfs/cikm14.pdf) | N/A | [sbpr_epinions.py](examples/sbpr_epinions.py) | 2014 | [Explicit Factor Model (EFM)](cornac/models/efm), [paper](https://www.yongfeng.me/attach/efm-zhang.pdf) | Explainable | N/A | [exp](examples/efm_example.py)
| 2013 | [Hidden Factors and Hidden Topics (HFT)](cornac/models/hft), [paper](https://cs.stanford.edu/people/jure/pubs/reviews-recsys13.pdf) | N/A | [hft_exp.py](examples/hft_example.py) | | [Social Bayesian Personalized Ranking (SBPR)](cornac/models/sbpr), [paper](https://cseweb.ucsd.edu/~jmcauley/pdfs/cikm14.pdf) | Content-Based / Social | N/A | [exp](examples/sbpr_epinions.py)
| 2012 | [Weighted Bayesian Personalized Ranking (WBPR)](cornac/models/bpr), [paper](http://proceedings.mlr.press/v18/gantner12a/gantner12a.pdf) | N/A | [bpr_netflix.py](examples/bpr_netflix.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 | N/A | [exp](examples/hft_example.py)
| 2011 | [Collaborative Topic Regression (CTR)](cornac/models/ctr), [paper](http://www.cs.columbia.edu/~blei/papers/WangBlei2011.pdf) | N/A | [ctr_citeulike.py](examples/ctr_example_citeulike.py) | 2012 | [Weighted Bayesian Personalized Ranking (WBPR)](cornac/models/bpr), [paper](http://proceedings.mlr.press/v18/gantner12a/gantner12a.pdf) | Collaborative Filtering | N/A | [exp](examples/bpr_netflix.py)
| Earlier | [Baseline Only](cornac/models/baseline_only), [paper](http://courses.ischool.berkeley.edu/i290-dm/s11/SECURE/a1-koren.pdf) | N/A | [svd_exp.py](examples/svd_example.py) | 2011 | [Collaborative Topic Regression (CTR)](cornac/models/ctr), [paper](http://www.cs.columbia.edu/~blei/papers/WangBlei2011.pdf) | Content-Based / Text | N/A | [exp](examples/ctr_example_citeulike.py)
| | [Bayesian Personalized Ranking (BPR)](cornac/models/bpr), [paper](https://arxiv.org/ftp/arxiv/papers/1205/1205.2618.pdf) | N/A | [bpr_netflix.py](examples/bpr_netflix.py) | Earlier | [Baseline Only](cornac/models/baseline_only), [paper](http://courses.ischool.berkeley.edu/i290-dm/s11/SECURE/a1-koren.pdf) | Baseline | N/A | [exp](examples/svd_example.py)
| | [Factorization Machines (FM)](cornac/models/fm), [paper](https://www.csie.ntu.edu.tw/~b97053/paper/Factorization%20Machines%20with%20libFM.pdf) | Linux only | [fm_example.py](examples/fm_example.py) | | [Bayesian Personalized Ranking (BPR)](cornac/models/bpr), [paper](https://arxiv.org/ftp/arxiv/papers/1205/1205.2618.pdf) | Collaborative Filtering | N/A | [exp](examples/bpr_netflix.py)
| | [Global Average (GlobalAvg)](cornac/models/global_avg), [paper](https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf) | N/A | [biased_mf.py](examples/biased_mf.py) | | [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 only | [exp](examples/fm_example.py)
| | [Global Personalized Top Frequent (GPTop)](cornac/models/gp_top), [paper](https://dl.acm.org/doi/pdf/10.1145/3587153) | N/A | [gp_top_tafeng.py](examples/gp_top_tafeng.py) | | [Global Average (GlobalAvg)](cornac/models/global_avg), [paper](https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf) | Baseline | N/A | [exp](examples/biased_mf.py)
| | [Item K-Nearest-Neighbors (ItemKNN)](cornac/models/knn), [paper](https://dl.acm.org/doi/pdf/10.1145/371920.372071) | N/A | [knn_movielens.py](examples/knn_movielens.py) | | [Global Personalized Top Frequent (GPTop)](cornac/models/gp_top), [paper](https://dl.acm.org/doi/pdf/10.1145/3587153) | Next-Basket | N/A | [exp](examples/gp_top_tafeng.py)
| | [Matrix Factorization (MF)](cornac/models/mf), [paper](https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf) | N/A | [biased_mf.py](examples/biased_mf.py), [given_data.py](examples/given_data.py) | | [Item K-Nearest-Neighbors (ItemKNN)](cornac/models/knn), [paper](https://dl.acm.org/doi/pdf/10.1145/371920.372071) | Neighborhood-Based | N/A | [exp](examples/knn_movielens.py)
| | [Maximum Margin Matrix Factorization (MMMF)](cornac/models/mmmf), [paper](https://link.springer.com/content/pdf/10.1007/s10994-008-5073-7.pdf) | N/A | [mmmf_exp.py](examples/mmmf_exp.py) | | [Matrix Factorization (MF)](cornac/models/mf), [paper](https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf) | Collaborative Filtering | N/A | [exp1](examples/biased_mf.py), [exp2](examples/given_data.py)
| | [Most Popular (MostPop)](cornac/models/most_pop), [paper](https://arxiv.org/ftp/arxiv/papers/1205/1205.2618.pdf) | N/A | [bpr_netflix.py](examples/bpr_netflix.py) | | [Maximum Margin Matrix Factorization (MMMF)](cornac/models/mmmf), [paper](https://link.springer.com/content/pdf/10.1007/s10994-008-5073-7.pdf) | Collaborative Filtering | N/A | [exp](examples/mmmf_exp.py)
| | [Non-negative Matrix Factorization (NMF)](cornac/models/nmf), [paper](http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf) | N/A | [nmf_exp.py](examples/nmf_example.py) | | [Most Popular (MostPop)](cornac/models/most_pop), [paper](https://arxiv.org/ftp/arxiv/papers/1205/1205.2618.pdf) | Baseline | N/A | [exp](examples/bpr_netflix.py)
| | [Probabilistic Matrix Factorization (PMF)](cornac/models/pmf), [paper](https://papers.nips.cc/paper/3208-probabilistic-matrix-factorization.pdf) | N/A | [pmf_ratio.py](examples/pmf_ratio.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 | N/A | [exp](examples/nmf_example.py)
| | [Session Popular (SPop)](cornac/models/spop), [paper](https://arxiv.org/pdf/1511.06939.pdf) | N/A | [spop_yoochoose.py](examples/spop_yoochoose.py) | | [Probabilistic Matrix Factorization (PMF)](cornac/models/pmf), [paper](https://papers.nips.cc/paper/3208-probabilistic-matrix-factorization.pdf) | Collaborative Filtering | N/A | [exp](examples/pmf_ratio.py)
| | [Singular Value Decomposition (SVD)](cornac/models/svd), [paper](https://people.engr.tamu.edu/huangrh/Spring16/papers_course/matrix_factorization.pdf) | N/A | [svd_exp.py](examples/svd_example.py) | | [Session Popular (SPop)](cornac/models/spop), [paper](https://arxiv.org/pdf/1511.06939.pdf) | Next-Item / Baseline | N/A | [exp](examples/spop_yoochoose.py)
| | [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) | N/A | [sorec_filmtrust.py](examples/sorec_filmtrust.py) | | [Singular Value Decomposition (SVD)](cornac/models/svd), [paper](https://people.engr.tamu.edu/huangrh/Spring16/papers_course/matrix_factorization.pdf) | Collaborative Filtering | N/A | [exp](examples/svd_example.py)
| | [User K-Nearest-Neighbors (UserKNN)](cornac/models/knn), [paper](https://arxiv.org/pdf/1301.7363.pdf) | N/A | [knn_movielens.py](examples/knn_movielens.py) | | [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 | N/A | [exp](examples/sorec_filmtrust.py)
| | [Weighted Matrix Factorization (WMF)](cornac/models/wmf), [paper](http://yifanhu.net/PUB/cf.pdf) | [requirements.txt](cornac/models/wmf/requirements.txt) | [wmf_exp.py](examples/wmf_example.py) | | [User K-Nearest-Neighbors (UserKNN)](cornac/models/knn), [paper](https://arxiv.org/pdf/1301.7363.pdf) | Neighborhood-Based | N/A | [exp](examples/knn_movielens.py)
| | [Weighted Matrix Factorization (WMF)](cornac/models/wmf), [paper](http://yifanhu.net/PUB/cf.pdf) | Collaborative Filtering | [reqs](cornac/models/wmf/requirements.txt) | [exp](examples/wmf_example.py)
## Contributing ## Contributing
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