Add Learn to Rank user Preferences based on Phrase-level sentiment analysis across Multiple categories (LRPPM) model

README.md

@@ -135,12 +135,13 @@ The recommender models supported by Cornac are listed below. Why don't you join
 | 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)
 |      | [Collaborative Ordinal Embedding (COE)](cornac/models/coe), [paper](http://www.hadylauw.com/publications/sdm16.pdf) | [requirements.txt](cornac/models/coe/requirements.txt) |
 |      | [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)
+|      | [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)
 |      | [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)
 |      | [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)
 | 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)
 |      | [Hierarchical Poisson Factorization (HPF)](cornac/models/hpf), [paper](http://jakehofman.com/inprint/poisson_recs.pdf) | N/A | [hpf_movielens.py](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) | N/A | [trirank_example.py](examples/trirank_example.py)
-| 2014 | [Explicit Factor Model (EFM)](cornac/models/efm), [paper](http://yongfeng.me/attach/efm-zhang.pdf) | N/A | [efm_exp.py](examples/efm_example.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)
 |      | [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)
 | 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)
 | 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)

cornac/models/__init__.py

@@ -42,6 +42,7 @@
 from .knn import ItemKNN
 from .knn import UserKNN
 from .lightgcn import LightGCN
+from .lrppm import LRPPM
 from .mcf import MCF
 from .mf import MF
 from .mmmf import MMMF

cornac/models/lrppm/__init__.py

@@ -0,0 +1 @@
+from .recom_lrppm import LRPPM