z-scoREC Implementation

README.md

@@ -108,10 +108,12 @@ The recommender models supported by Cornac are listed below. Why don't you join
 
 | Year | Model and paper | Additional dependencies | Examples |
 | :---: | --- | :---: | :---: |
+| 2022 | [ImposeSVD: Incrementing PureSVD For Top-N Recommendations for Cold-Start Problems and Sparse Datasets (z-scoREC)](cornac/models/zscorec), [paper]( https://doi.org/10.1093/comjnl/bxac106) | N/A | [zscorec_netflix.py](examples/zscorec_netflix.py)
 | 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)
 | | [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)
 | 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)
+| 2019 | [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)
 | 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)
 | | [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)
 | | [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)
@@ -153,6 +155,7 @@ The recommender models supported by Cornac are listed below. Why don't you join
 | | [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)
 | | [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)
 
+
 ## Support
 
 Your contributions at any level of the library are welcome. If you intend to contribute, please:

cornac/models/__init__.py

@@ -31,6 +31,7 @@
 from .ctr import CTR
 from .cvae import CVAE
 from .cvaecf import CVAECF
+from .ease import EASE
 from .efm import EFM
 from .global_avg import GlobalAvg
 from .hft import HFT
@@ -59,6 +60,7 @@
 from .vbpr import VBPR
 from .vmf import VMF
 from .wmf import WMF
+from .zscorec import zscoREC
 
 try:
  from .fm import FM

cornac/models/ease/__init__.py

@@ -0,0 +1 @@
+from .recom_ease import EASE

cornac/models/ease/recom_ease.py

@@ -0,0 +1,135 @@
+import numpy as np
+
+from cornac.models.recommender import Recommender
+from cornac.exception import ScoreException
+
+class EASE(Recommender):
+ """Embarrassingly Shallow Autoencoders for Sparse Data.
+
+ Parameters
+ ----------
+ name: string, optional, default: 'EASEᴿ'
+ The name of the recommender model.
+
+ lamb: float, optional, default: 500
+ L2-norm regularization-parameter λ ∈ R+.
+
+ posB: boolean, optional, default: False
+ Remove Negative Weights
+
+ trainable: boolean, optional, default: True
+ When False, the model is not trained and Cornac assumes that the model is already \
+ trained. 
+
+ verbose: boolean, optional, default: False
+ When True, some running logs are displayed.
+
+ seed: int, optional, default: None
+ Random seed for parameters initialization.
+
+ References
+ ----------
+ * Steck, H. (2019, May). "Embarrassingly shallow autoencoders for sparse data." \
+ In The World Wide Web Conference (pp. 3251-3257).
+ """
+
+ def __init__(
+ self,
+ name="EASEᴿ",
+ lamb=500,
+ posB=True,
+ trainable=True,
+ verbose=True,
+ seed=None,
+ B=None,
+ U=None,
+ ):
+ Recommender.__init__(self, name=name, trainable=trainable, verbose=verbose)
+ self.lamb = lamb
+ self.posB = posB
+ self.verbose = verbose
+ self.seed = seed
+ self.B = B
+ self.U = U
+
+ def fit(self, train_set, val_set=None):
+ """Fit the model to observations.
+
+ Parameters
+ ----------
+ train_set: :obj:`cornac.data.Dataset`, required
+ User-Item preference data as well as additional modalities.
+
+ val_set: :obj:`cornac.data.Dataset`, optional, default: None
+ User-Item preference data for model selection purposes (e.g., early stopping).
+
+ Returns
+ -------
+ self : object
+ """
+ Recommender.fit(self, train_set, val_set)
+
+ # A rating matrix
+ self.U = self.train_set.matrix
+
+ # Gram matrix is X^t X, compute dot product
+ G = self.U.T.dot(self.U).toarray()
+
+ diag_indices = np.diag_indices(G.shape[0])
+
+ G[diag_indices] = G.diagonal() + self.lamb
+
+ P = np.linalg.inv(G)
+
+ B = P / (-np.diag(P))
+
+ B[diag_indices] = 0.0
+
+ # if self.posB remove -ve values
+ if self.posB:
+ B[B<0]=0 
+
+ # save B for predictions
+ self.B=B
+
+ return self
+
+
+ def score(self, user_idx, item_idx=None):
+ """Predict the scores/ratings of a user for an item.
+
+ Parameters
+ ----------
+ user_idx: int, required
+ The index of the user for whom to perform score prediction.
+
+ item_idx: int, optional, default: None
+ The index of the item for which to perform score prediction.
+ If None, scores for all known items will be returned.
+
+ Returns
+ -------
+ res : A scalar or a Numpy array
+ Relative scores that the user gives to the item or to all known items
+
+ """
+ if item_idx is None:
+ if self.train_set.is_unk_user(user_idx):
+ raise ScoreException(
+ "Can't make score prediction for (user_id=%d)" % user_idx
+ )
+
+ known_item_scores = self.U[user_idx, :].dot(self.B)
+ return known_item_scores
+ else:
+ if self.train_set.is_unk_user(user_idx) or self.train_set.is_unk_item(
+ item_idx
+ ):
+ raise ScoreException(
+ "Can't make score prediction for (user_id=%d, item_id=%d)"
+ % (user_idx, item_idx)
+ )
+
+ user_pred = self.B[item_idx, :].dot(self.U[user_idx, :])
+
+ return user_pred

cornac/models/zscorec/__init__.py

@@ -0,0 +1 @@
+from .recom_zscorec import zscoREC

cornac/models/zscorec/recom_zscorec.py

@@ -0,0 +1,141 @@
+import numpy as np
+
+from cornac.models.recommender import Recommender
+from cornac.exception import ScoreException
+
+import scipy.sparse as sps
+import scipy.stats as stats
+import numpy as np
+
+class zscoREC(Recommender):
+ """ImposeSVD: Incrementing PureSVD For Top-N Recommendations for Cold-Start Problems and Sparse Datasets.
+
+ Parameters
+ ----------
+ name: string, optional, default: 'zscoREC'
+ The name of the recommender model.
+
+ lamb: float, optional, default: .2
+ Shifting parameter λ ∈ R
+
+ posZ: boolean, optional, default: False
+ Remove Negative Weights
+
+ trainable: boolean, optional, default: True
+ When False, the model is not trained and Cornac assumes that the model is already \
+ trained. 
+
+ verbose: boolean, optional, default: False
+ When True, some running logs are displayed.
+
+ seed: int, optional, default: None
+ Random seed for parameters initialization.
+
+ References
+ ----------
+ * Hakan Yilmazer, Selma Ayşe Özel, ImposeSVD: Incrementing PureSVD For Top-N Recommendations for Cold-Start Problems and Sparse Datasets, 
+ The Computer Journal, 2022;, bxac106, https://doi.org/10.1093/comjnl/bxac106.
+ """
+
+ def __init__(
+ self,
+ name="zscoREC",
+ lamb=.2,
+ posZ=True,
+ trainable=True,
+ verbose=True,
+ seed=None,
+ Z=None,
+ U=None,
+ ):
+ Recommender.__init__(self, name=name, trainable=trainable, verbose=verbose)
+ self.lamb = lamb
+ self.posZ = posZ
+ self.verbose = verbose
+ self.seed = seed
+ self.Z = Z
+ self.U = U
+
+ def fit(self, train_set, val_set=None):
+ """Fit the model to observations.
+
+ Parameters
+ ----------
+ train_set: :obj:`cornac.data.Dataset`, required
+ User-Item preference data as well as additional modalities.
+
+ val_set: :obj:`cornac.data.Dataset`, optional, default: None
+ User-Item preference data for model selection purposes (e.g., early stopping).
+
+ Returns
+ -------
+ self : object
+ """
+ Recommender.fit(self, train_set, val_set)
+
+ # A rating matrix
+ self.U = self.train_set.matrix
+
+ # Gram matrix is X^t X, Eq.(2) in paper
+ G = sps.csr_matrix(self.U.T.dot(self.U), dtype="uint64")
+
+ # Shifting Gram matrix Eq.(7) in paper
+ """ In a structure view, the shifting operation on the second
+ matrix (which is the same as the matrix for the Gram-matrix
+ estimation) is performed as row-based degree shifting on
+ Gram-matrix.
+ """ 
+ W = G - np.tile(G.diagonal()*self.lam, (G.shape[0],1)).transpose()
+
+ # Column-based z-score normalization
+ # fit each item's column (could be parallel for big streams)
+ Z = stats.mstats.zscore(np.array(W), axis=0, ddof=1, nan_policy='omit')
+
+ # if self.posZ remove -ve values
+ if self.posZ:
+ Z[Z<0]=0 
+
+ # save Z for predictions
+ self.Z = Z
+
+ return self
+
+
+ def score(self, user_idx, item_idx=None):
+ """Predict the scores/ratings of a user for an item.
+
+ Parameters
+ ----------
+ user_idx: int, required
+ The index of the user for whom to perform score prediction.
+
+ item_idx: int, optional, default: None
+ The index of the item for which to perform score prediction.
+ If None, scores for all known items will be returned.
+
+ Returns
+ -------
+ res : A scalar or a Numpy array
+ Relative scores that the user gives to the item or to all known items
+
+ """
+ if item_idx is None:
+ if self.train_set.is_unk_user(user_idx):
+ raise ScoreException(
+ "Can't make score prediction for (user_id=%d)" % user_idx
+ )
+
+ known_item_scores = self.U[user_idx, :].dot(self.Z)
+ return known_item_scores
+ else:
+ if self.train_set.is_unk_user(user_idx) or self.train_set.is_unk_item(
+ item_idx
+ ):
+ raise ScoreException(
+ "Can't make score prediction for (user_id=%d, item_id=%d)"
+ % (user_idx, item_idx)
+ )
+
+ user_pred = self.Z[item_idx, :].dot(self.U[user_idx, :])
+
+ return user_pred

docs/source/models.rst

@@ -10,6 +10,11 @@ Recommender (Generic Class)
 .. automodule:: cornac.models.recommender
  :members:
 
+ImposeSVD: Incrementing PureSVD For Top-N Recommendations for Cold-Start Problems and Sparse Datasets (z-scoREC)
+----------------------------------------------------
+.. automodule:: cornac.models.zscorec.recom_zscorec
+ :members:
+
 Bilateral VAE for Collaborative Filtering (BiVAECF)
 ----------------------------------------------------
 .. automodule:: cornac.models.bivaecf.recom_bivaecf
@@ -33,6 +38,11 @@ Adversarial Training Towards Robust Multimedia Recommender System (AMR)
 .. automodule:: cornac.models.amr.recom_amr
  :members:
 
+Embarrassingly Shallow Autoencoders for Sparse Data (EASEᴿ)
+--------------------------------------------------
+.. automodule:: cornac.models.ease.recom_ease
+ :members:
+
 Collaborative Context Poisson Factorization (C2PF)
 ----------------------------------------------------
 .. automodule:: cornac.models.c2pf.recom_c2pf
@@ -231,4 +241,4 @@ User K-Nearest-Neighbors (UserKNN)
 Weighted Matrix Factorization (WMF)
 --------------------------------------------------
 .. automodule:: cornac.models.wmf.recom_wmf
- :members:
+ :members:

examples/README.md

@@ -68,6 +68,8 @@
 
 [bpr_netflix.py](bpr_netflix.py) - Example to run Bayesian Personalized Ranking (BPR) with Netflix dataset.
 
+[ease_movielens.py](ease_movielens.py) - Embarrassingly Shallow Autoencoders (EASEᴿ) with MovieLens 1M dataset.
+
 [fm_example.py](fm_example.py) - Example to run Factorization Machines (FM) with MovieLens 100K dataset.
 
 [hpf_movielens.py](hpf_movielens.py) - (Hierarchical) Poisson Factorization vs BPR on MovieLens data.
@@ -91,3 +93,5 @@
 [vaecf_citeulike.py](vaecf_citeulike.py) - Variational Autoencoder for Collaborative Filtering (VAECF) with CiteULike dataset.
 
 [wmf_example.py](wmf_example.py) - Weighted Matrix Factorization with CiteULike dataset.
+
+[zscorec_movielens.py](zscorec_movielens.py) - z-scoREC model with MovieLens 1M dataset.