Models¶
Probabilistic Collaborative Representation Learning (PCRL)¶
@author: Aghiles Salah <asalah@smu.edu.sg>
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class
cornac.models.pcrl.recom_pcrl.PCRL(k=100, z_dims=[300], max_iter=300, batch_size=300, learning_rate=0.001, name='pcrl', trainable=True, verbose=False, w_determinist=True, init_params={'G_r': None, 'G_s': None, 'L_r': None, 'L_s': None})[source]¶ Probabilistic Collaborative Representation Learning.
Parameters: - k (int, optional, default: 100) – The dimension of the latent factors.
- z_dims (Numpy 1d array, optional, default: [300]) – The dimensions of the hidden intermdiate layers ‘z’ in the order [dim(z_L), …,dim(z_1)], please refer to Figure 1 in the orginal paper for more details.
- max_iter (int, optional, default: 300) – Maximum number of iterations (number of epochs) for variational PCRL.
- batch_size (int, optional, default: 300) – The batch size for SGD.
- learning_rate (float, optional, default: 0.001) – The learning rate for SGD.
- aux_info (see "cornac/examples/pcrl_example.py" in the GitHub repo for an example of how to use cornac's graph module provide item auxiliary data (e.g., context, text, etc.) for PCRL.) –
- name (string, optional, default: 'PCRL') – The name of the recommender model.
- trainable (boolean, optional, default: True) – When False, the model is not trained and Cornac assumes that the model already pre-trained (Theta, Beta and Xi are not None).
- w_determinist (boolean, optional, default: True) – When True, determinist wheights “W” are used for the generator network, otherwise “W” is stochastic as in the original paper.
- init_params (dictionary, optional, default: {'G_s':None, 'G_r':None, 'L_s':None, 'L_r':None}) – List of initial parameters, e.g., init_params = {‘G_s’:G_s, ‘G_r’:G_r, ‘L_s’:L_s, ‘L_r’:L_r}, where G_s and G_r are of type csc_matrix or np.array with the same shape as Theta, see below). They represent respectively the “shape” and “rate” parameters of Gamma distribution over Theta. It is the same for L_s, L_r and Beta.
- Theta (csc_matrix, shape (n_users,k)) – The expected user latent factors.
- Beta (csc_matrix, shape (n_items,k)) – The expected item latent factors.
References
- Salah, Aghiles, and Hady W. Lauw. Probabilistic Collaborative Representation Learning for Personalized Item Recommendation. In UAI 2018.
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fit(train_set)[source]¶ Fit the model to observations.
Parameters: train_set (object of type TrainSet, required) – An object contraining the user-item preference in csr scipy sparse format, as well as some useful attributes such as mappings to the original user/item ids. Please refer to the class TrainSet in the “data” module for details.
Collaborative Context Poisson Factorization (C2PF)¶
@author: Aghiles Salah <asalah@smu.edu.sg>
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class
cornac.models.c2pf.recom_c2pf.C2PF(k=100, max_iter=100, variant='c2pf', name=None, trainable=True, verbose=False, init_params={'G_r': None, 'G_s': None, 'L2_r': None, 'L2_s': None, 'L3_r': None, 'L3_s': None, 'L_r': None, 'L_s': None})[source]¶ Collaborative Context Poisson Factorization.
Parameters: - k (int, optional, default: 100) – The dimension of the latent factors.
- max_iter (int, optional, default: 100) – Maximum number of iterations for variational C2PF.
- variant (string, optional, default: 'c2pf') – C2pf’s variant: c2pf: ‘c2pf’, ‘tc2pf’ (tied-c2pf) or ‘rc2pf’ (reduced-c2pf). Please refer to the original paper for details.
- name (string, optional, default: None) – The name of the recommender model. If None, then “variant” is used as the default name of the model.
- trainable (boolean, optional, default: True) – When False, the model is not trained and Cornac assumes that the model already pre-trained (Theta, Beta and Xi are not None).
- Item_context (See "cornac/examples/c2pf_example.py" in the GitHub repo for an example of how to use cornac's graph module to load and provide "item context" for C2PF.) –
- init_params (dictionary, optional, default: {'G_s':None, 'G_r':None, 'L_s':None, 'L_r':None, 'L2_s':None, 'L2_r':None, 'L3_s':None, 'L3_r':None}) – List of initial parameters, e.g., init_params = {‘G_s’:G_s, ‘G_r’:G_r, ‘L_s’:L_s, ‘L_r’:L_r, ‘L2_s’:L2_s, ‘L2_r’:L2_r, ‘L3_s’:L3_s, ‘L3_r’:L3_r}, where G_s and G_r are of type csc_matrix or np.array with the same shape as Theta, see below). They represent respectively the “shape” and “rate” parameters of Gamma distribution over Theta. It is the same for L_s, L_r and Beta, L2_s, L2_r and Xi, L3_s, L3_r and Kappa.
- Theta (csc_matrix, shape (n_users,k)) – The expected user latent factors.
- Beta (csc_matrix, shape (n_items,k)) – The expected item latent factors.
- Xi (csc_matrix, shape (n_items,k)) – The expected context item latent factors multiplied by context effects Kappa, please refer to the paper below for details.
References
- Salah, Aghiles, and Hady W. Lauw. A Bayesian Latent Variable Model of User Preferences with Item Context. In IJCAI, pp. 2667-2674. 2018.
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fit(train_set)[source]¶ Fit the model to observations.
Parameters: train_set (object of type TrainSet, required) – An object contraining the user-item preference in csr scipy sparse format, as well as some useful attributes such as mappings to the original user/item ids. Please refer to the class TrainSet in the “data” module for details.
Indexable Bayesian Personalized Ranking (IBPR)¶
@author: Dung D. Le (Andrew) <ddle.2015@smu.edu.sg>
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class
cornac.models.ibpr.recom_ibpr.IBPR(k=20, max_iter=100, learning_rate=0.05, lamda=0.001, batch_size=100, name='ibpr', trainable=True, verbose=False, init_params=None)[source]¶ Indexable Bayesian Personalized Ranking.
Parameters: - k (int, optional, default: 20) – The dimension of the latent factors.
- max_iter (int, optional, default: 100) – Maximum number of iterations or the number of epochs for SGD.
- learning_rate (float, optional, default: 0.05) – The learning rate for SGD.
- lamda (float, optional, default: 0.001) – The regularization parameter.
- batch_size (int, optional, default: 100) – The batch size for SGD.
- name (string, optional, default: 'IBRP') – The name of the recommender model.
- trainable (boolean, optional, default: True) – When False, the model is not trained and Cornac assumes that the model already pre-trained (U and V are not None).
- verbose (boolean, optional, default: False) – When True, some running logs are displayed.
- init_params (dictionary, optional, default: None) – List of initial parameters, e.g., init_params = {‘U’:U, ‘V’:V} please see below the definition of U and V.
- U (csc_matrix, shape (n_users,k)) – The user latent factors, optional initialization via init_params.
- V (csc_matrix, shape (n_items,k)) – The item latent factors, optional initialization via init_params.
References
- Le, D. D., & Lauw, H. W. (2017, November). Indexable Bayesian personalized ranking for efficient top-k recommendation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (pp. 1389-1398). ACM.
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fit(train_set)[source]¶ Fit the model to observations.
Parameters: train_set (object of type TrainSet, required) – An object contraining the user-item preference in csr scipy sparse format, as well as some useful attributes such as mappings to the original user/item ids. Please refer to the class TrainSet in the “data” module for details.
Online Indexable Bayesian Personalized Ranking (OIBPR)¶
@author: Dung D. Le (Andrew) <ddle.2015@smu.edu.sg>
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class
cornac.models.online_ibpr.recom_online_ibpr.OnlineIBPR(k=20, max_iter=100, learning_rate=0.05, lamda=0.001, batch_size=100, name='online_ibpr', trainable=True, verbose=False, init_params=None)[source]¶ Online Indexable Bayesian Personalized Ranking.
Parameters: - k (int, optional, default: 20) – The dimension of the latent factors.
- max_iter (int, optional, default: 100) – Maximum number of iterations or the number of epochs for SGD.
- learning_rate (float, optional, default: 0.05) – The learning rate for SGD.
- lamda (float, optional, default: 0.001) – The regularization parameter.
- batch_size (int, optional, default: 100) – The batch size for SGD.
- name (string, optional, default: 'IBRP') – The name of the recommender model.
- trainable (boolean, optional, default: True) – When False, the model is not trained and Cornac assumes that the model already pre-trained (U and V are not None).
- verbose (boolean, optional, default: False) – When True, some running logs are displayed.
- init_params (dictionary, optional, default: None) – List of initial parameters, e.g., init_params = {‘U’:U, ‘V’:V} please see below the definition of U and V.
- U (csc_matrix, shape (n_users,k)) – The user latent factors, optional initialization via init_params.
- V (csc_matrix, shape (n_items,k)) – The item latent factors, optional initialization via init_params.
References
- Le, D. D., & Lauw, H. W. (2017, November). Indexable Bayesian personalized ranking for efficient top-k recommendation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (pp. 1389-1398). ACM.
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fit(train_set)[source]¶ Fit the model to observations.
Parameters: train_set (object of type TrainSet, required) – An object contraining the user-item preference in csr scipy sparse format, as well as some useful attributes such as mappings to the original user/item ids. Please refer to the class TrainSet in the “data” module for details.
Collaborative Ordinal Embedding (COE)¶
@author: Dung D. Le (Andrew) <ddle.2015@smu.edu.sg>
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class
cornac.models.coe.recom_coe.COE(k=20, max_iter=100, learning_rate=0.05, lamda=0.001, batch_size=1000, name='coe', trainable=True, verbose=False, init_params=None)[source]¶ Collaborative Ordinal Embedding.
Parameters: - k (int, optional, default: 20) – The dimension of the latent factors.
- max_iter (int, optional, default: 100) – Maximum number of iterations or the number of epochs for SGD.
- learning_rate (float, optional, default: 0.05) – The learning rate for SGD.
- lamda (float, optional, default: 0.001) – The regularization parameter.
- batch_size (int, optional, default: 100) – The batch size for SGD.
- name (string, optional, default: 'IBRP') – The name of the recommender model.
- trainable (boolean, optional, default: True) – When False, the model is not trained and Cornac assumes that the model already pre-trained (U and V are not None).
- verbose (boolean, optional, default: False) – When True, some running logs are displayed.
- init_params (dictionary, optional, default: None) – List of initial parameters, e.g., init_params = {‘U’:U, ‘V’:V} please see below the definition of U and V.
- U (csc_matrix, shape (n_users,k)) – The user latent factors, optional initialization via init_params.
- V (csc_matrix, shape (n_items,k)) – The item latent factors, optional initialization via init_params.
References
- Le, D. D., & Lauw, H. W. (2016, June). Euclidean co-embedding of ordinal data for multi-type visualization. In Proceedings of the 2016 SIAM International Conference on Data Mining (pp. 396-404). Society for Industrial and Applied Mathematics.
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fit(train_set)[source]¶ Fit the model to observations.
Parameters: train_set (object of type TrainSet, required) – An object contraining the user-item preference in csr scipy sparse format, as well as some useful attributes such as mappings to the original user/item ids. Please refer to the class TrainSet in the “data” module for details.
Visual Bayesian Personalized Ranking (VBPR)¶
- @author: Guo Jingyao <jyguo@smu.edu.sg>
- Quoc-Tuan Truong <tuantq.vnu@gmail.com>
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class
cornac.models.vbpr.recom_vbpr.VBPR(k=10, k2=10, n_epochs=20, batch_size=100, learning_rate=0.001, lambda_w=0.01, lambda_b=0.01, lambda_e=0.0, use_gpu=False, trainable=True, init_params=None, **kwargs)[source]¶ Visual Bayesian Personalized Ranking.
Parameters: - k (int, optional, default: 10) – The dimension of the gamma latent factors.
- k2 (int, optional, default: 10) – The dimension of the theta latent factors.
- n_epochs (int, optional, default: 20) – Maximum number of epochs for SGD.
- batch_size (int, optional, default: 100) – The batch size for SGD.
- learning_rate (float, optional, default: 0.001) – The learning rate for SGD.
- lambda_w (float, optional, default: 0.01) – The regularization hyper-parameter for latent factor weights.
- lambda_b (float, optional, default: 0.01) – The regularization hyper-parameter for biases.
- lambda_e (float, optional, default: 0.0) – The regularization hyper-parameter for embedding matrix E and beta prime vector.
- use_gpu (boolean, optional, default: True) – Whether or not to use GPU to speed up training.
- trainable (boolean, optional, default: True) – When False, the model is not trained and Cornac assumes that the model already pre-trained (U and V are not None).
- init_params (dictionary, optional, default: None) –
- Initial parameters, e.g., init_params = {‘Bi’: beta_item,
- ’Gu’: gamma_user, ‘Gi’: gamma_item, ‘Tu’: theta_user, ‘E’: emb_matrix, ‘Bp’: beta_prime}
References
- HE, Ruining et MCAULEY, Julian. VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback. In : AAAI. 2016. p. 144-150.
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fit(train_set)[source]¶ Fit the model.
Parameters: train_set ( cornac.data.MultimodalTrainSet) – Multimodal training set.
Spherical k-means (Skmeans)¶
@author: Aghiles Salah <asalah@smu.edu.sg>
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class
cornac.models.skm.recom_skmeans.SKMeans(k=5, max_iter=100, name='Skmeans', trainable=True, tol=1e-06, verbose=True, init_par=None)[source]¶ Spherical k-means based recommender.
Parameters: - k (int, optional, default: 5) – The number of clusters.
- max_iter (int, optional, default: 100) – Maximum number of iterations.
- name (string, optional, default: 'Skmeans') – The name of the recommender model.
- trainable (boolean, optional, default: True) – When False, the model is not trained and Cornac assumes that the model is already trained.
- tol (float, optional, default: 1e-6) – Relative tolerance with regards to skmeans’ criterion to declare convergence.
- verbose (boolean, optional, default: False) – When True, some running logs are displayed.
- init_par (numpy 1d array, optional, default: None) – The initial object parition, 1d array contaning the cluster label (int type starting from 0) of each object (user). If par = None, then skmeans is initialized randomly.
- centroids (csc_matrix, shape (k,n_users)) – The maxtrix of cluster centroids.
References
- Salah, Aghiles, Nicoleta Rogovschi, and Mohamed Nadif. “A dynamic collaborative filtering system via a weighted clustering approach.” Neurocomputing 175 (2016): 206-215.
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fit(train_set)[source]¶ Fit the model to observations.
Parameters: train_set (object of type TrainSet, required) – An object contraining the user-item preference in csr scipy sparse format, as well as some useful attributes such as mappings to the original user/item ids. Please refer to the class TrainSet in the “data” module for details.
Collaborative Deep Learning (CDL)¶
@author: Trieu Thi Ly Ly
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class
cornac.models.cdl.recom_cdl.CDL(k=50, text_information=None, autoencoder_structure=None, lambda_u=0.1, lambda_v=0.01, lambda_w=0.01, lambda_n=0.01, a=1, b=0.01, autoencoder_corruption=0.3, learning_rate=0.001, keep_prob=1.0, batch_size=100, max_iter=100, name='CDL', trainable=True, verbose=False, init_params=None)[source]¶ Collaborative Deep Learning.
Parameters: - k (int, optional, default: 50) – The dimension of the latent factors.
- max_iter (int, optional, default: 100) – Maximum number of iterations or the number of epochs for SGD.
- shape (n_items, n_vocabularies), optional, default (text_information：ndarray,) – Bag-of-words features of items
- optional, default (autoencoder_structure：array,) – The number of neurons of encoder/ decoder layer for SDAE
- learning_rate (float, optional, default: 0.001) – The learning rate for AdamOptimizer.
- lambda_u (float, optional, default: 0.1) – The regularization parameter for users.
- lambda_v (float, optional, default: 10) – The regularization parameter for items.
- lambda_w (float, optional, default: 0.1) – The regularization parameter for SDAE weights.
- lambda_n (float, optional, default: 1000) – The regularization parameter for SDAE output.
- a (float, optional, default: 1) – The confidence of observed ratings.
- b (float, optional, default: 0.01) – The confidence of unseen ratings.
- autoencoder_corruption (float, optional, default: 0.3) – The corruption ratio for SDAE.
- keep_prob (float, optional, default: 1.0) – The probability that each element is kept in dropout of SDAE.
- batch_size (int, optional, default: 100) – The batch size for SGD.
- name (string, optional, default: 'CDL') – The name of the recommender model.
- trainable (boolean, optional, default: True) – When False, the model is not trained and Cornac assumes that the model already pre-trained (U and V are not None).
- init_params (dictionary, optional, default: None) – List of initial parameters, e.g., init_params = {‘U’:U, ‘V’:V} please see below the definition of U and V.
- U (ndarray, shape (n_users,k)) – The user latent factors, optional initialization via init_params.
- V (ndarray, shape (n_items,k)) – The item latent factors, optional initialization via init_params.
References
- Hao Wang, Naiyan Wang, Dit-Yan Yeung. CDL: Collaborative Deep Learning for Recommender Systems. In : SIGKDD. 2015. p. 1235-1244.
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fit(train_set)[source]¶ Fit the model to observations.
Parameters: train_set (object of type TrainSet, required) – An object contraining the user-item preference in csr scipy sparse format, as well as some useful attributes such as mappings to the original user/item ids. Please refer to the class TrainSet in the “data” module for details.
Hierarchical Poisson Factorization (HPF)¶
@author: Aghiles Salah <asalah@smu.edu.sg>
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class
cornac.models.hpf.recom_hpf.HPF(k=5, max_iter=100, name='HPF', trainable=True, verbose=False, hierarchical=True, init_params={'G_r': None, 'G_s': None, 'L_r': None, 'L_s': None})[source]¶ Hierarchical Poisson Factorization.
Parameters: - k (int, optional, default: 5) – The dimension of the latent factors.
- max_iter (int, optional, default: 100) – Maximum number of iterations.
- name (string, optional, default: 'HPF') – The name of the recommender model.
- trainable (boolean, optional, default: True) – When False, the model is not trained and Cornac assumes that the model is already pre-trained (Theta and Beta are not None).
- verbose (boolean, optional, default: False) – When True, some running logs are displayed.
- hierarchical (boolean, optional, default: True) – When False, PF is used instead of HPF.
- init_params (dictionary, optional, default: {'G_s':None, 'G_r':None, 'L_s':None, 'L_r':None}) – List of initial parameters, e.g., init_params = {‘G_s’:G_s, ‘G_r’:G_r, ‘L_s’:L_s, ‘L_r’:L_r}, where G_s and G_r are of type csc_matrix or np.array with the same shape as Theta, see below). They represent respectively the “shape” and “rate” parameters of Gamma distribution over Theta. Similarly, L_s, L_r are the shape and rate parameters of the Gamma over Beta.
- Theta (csc_matrix, shape (n_users,k)) – The expected user latent factors.
- Beta (csc_matrix, shape (n_items,k)) – The expected item latent factors.
References
- Gopalan, Prem, Jake M. Hofman, and David M. Blei. Scalable Recommendation with Hierarchical Poisson Factorization. In UAI, pp. 326-335. 2015.
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fit(train_set)[source]¶ Fit the model to observations.
Parameters: train_set (object of type TrainSet, required) – An object contraining the user-item preference in csr scipy sparse format, as well as some useful attributes such as mappings to the original user/item ids. Please refer to the class TrainSet in the “data” module for details.
Bayesian Personalized Ranking (BPR)¶
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class
cornac.models.bpr.recom_bpr.BPR¶ Bayesian Personalized Ranking.
Parameters: - k (int, optional, default: 10) – The dimension of the latent factors.
- max_iter (int, optional, default: 100) – Maximum number of iterations or the number of epochs for SGD.
- learning_rate (float, optional, default: 0.001) – The learning rate for SGD.
- lambda_reg (float, optional, default: 0.001) – The regularization hyper-parameter.
- num_threads (int, optional, default: 0) – Number of parallel threads for training. If 0, all CPU cores will be utilized.
- trainable (boolean, optional, default: True) – When False, the model will not be re-trained, and input of pre-trained parameters are required.
- verbose (boolean, optional, default: True) – When True, some running logs are displayed.
- init_params (dictionary, optional, default: None) – Initial parameters, e.g., init_params = {‘U’: user_factors, ‘V’: item_factors, ‘Bi’: item_biases}
References
- Rendle, Steffen, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. BPR: Bayesian personalized ranking from implicit feedback. In UAI, pp. 452-461. 2009.
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fit¶ Fit the model to observations.
Parameters: train_set (object of type TrainSet, required) – An object contains the user-item preference in csr scipy sparse format, as well as some useful attributes such as mappings to the original user/item ids. Please refer to the class TrainSet in the “data” module for details.
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score¶ Predict the scores/ratings of a user for an item.
Parameters: Returns: res – Relative scores that the user gives to the item or to all known items
Return type: A scalar or a Numpy array
Probabilitic Matrix Factorization (PMF)¶
@author: Aghiles Salah
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class
cornac.models.pmf.recom_pmf.PMF(k=5, max_iter=100, learning_rate=0.001, gamma=0.9, lamda=0.001, name='PMF', variant='non_linear', trainable=True, verbose=False, init_params={'U': None, 'V': None})[source]¶ Probabilistic Matrix Factorization.
Parameters: - k (int, optional, default: 5) – The dimension of the latent factors.
- max_iter (int, optional, default: 100) – Maximum number of iterations or the number of epochs for SGD.
- learning_rate (float, optional, default: 0.001) – The learning rate for SGD_RMSProp.
- gamma (float, optional, default: 0.9) – The weight for previous/current gradient in RMSProp.
- lamda (float, optional, default: 0.001) – The regularization parameter.
- name (string, optional, default: 'PMF') – The name of the recommender model.
- variant ({"linear","non_linear"}, optional, default: 'non_linear') – Pmf variant. If ‘non_linear’, the Gaussian mean is the output of a Sigmoid function. If ‘linear’ the Gaussian mean is the output of the identity function.
- trainable (boolean, optional, default: True) – When False, the model is not trained and Cornac assumes that the model already pre-trained (U and V are not None).
- verbose (boolean, optional, default: False) – When True, some running logs are displayed.
- init_params (dictionary, optional, default: {'U':None,'V':None}) – List of initial parameters, e.g., init_params = {‘U’:U, ‘V’:V}. U: a csc_matrix of shape (n_users,k), containing the user latent factors. V: a csc_matrix of shape (n_items,k), containing the item latent factors.
References
- Mnih, Andriy, and Ruslan R. Salakhutdinov. Probabilistic matrix factorization. In NIPS, pp. 1257-1264. 2008.
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fit(train_set)[source]¶ Fit the model to observations.
Parameters: train_set (object of type TrainSet, required) – An object contraining the user-item preference in csr scipy sparse format, as well as some useful attributes such as mappings to the original user/item ids. Please refer to the class TrainSet in the “data” module for details.
Matrix Factorization (MF)¶
@author: Quoc-Tuan Truong <tuantq.vnu@gmail.com>
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class
cornac.models.mf.recom_mf.MF¶ Matrix Factorization.
Parameters: - k (int, optional, default: 10) – The dimension of the latent factors.
- max_iter (int, optional, default: 100) – Maximum number of iterations or the number of epochs for SGD.
- learning_rate (float, optional, default: 0.01) – The learning rate.
- lambda_reg (float, optional, default: 0.001) – The lambda value used for regularization.
- use_bias (boolean, optional, default: True) – When True, user, item, and global biases are used.
- early_stop (boolean, optional, default: False) – When True, delta loss will be checked after each iteration to stop learning earlier.
- trainable (boolean, optional, default: True) – When False, the model will not be re-trained, and input of pre-trained parameters are required.
- verbose (boolean, optional, default: True) – When True, running logs are displayed.
- init_params (dictionary, optional, default: None) – Initial parameters, e.g., init_params = {‘U’: user_factors, ‘V’: item_factors, ‘Bu’: user_biases, ‘Bi’: item_biases}
References
- Koren, Y., Bell, R., & Volinsky, C. Matrix factorization techniques for recommender systems. In Computer, (8), 30-37. 2009.
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fit¶ Fit the model to observations.
Parameters: train_set (object of type TrainSet, required) – An object contains the user-item preference in csr scipy sparse format, as well as some useful attributes such as mappings to the original user/item ids. Please refer to the class TrainSet in the “data” module for details.
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score¶ Predict the scores/ratings of a user for an item.
Parameters: Returns: res – Relative scores that the user gives to the item or to all known items
Return type: A scalar or a Numpy array
Convolutional Matrix Factorization (ConvMF)¶
@author: Tran Thanh Binh
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class
cornac.models.conv_mf.recom_convmf.ConvMF(give_item_weight=True, n_epochs=50, lambda_u=1, lambda_v=100, k=50, name='convmf', trainable=True, verbose=False, dropout_rate=0.2, emb_dim=200, max_len=300, num_kernel_per_ws=100, init_params=None)[source]¶ Parameters: - k (int, optional, default: 50) – The dimension of the user and item latent factors.
- n_epochs (int, optional, default: 50) – Maximum number of epochs for training.
- lambda_u (float, optional, default: 1.0) – The regularization hyper-parameter for user latent factor.
- lambda_v (float, optional, default: 100.0) – The regularization hyper-parameter for item latent factor.
- emb_dim (int, optional, default: 200) – The embedding size of each word. One word corresponds with [1 x emb_dim] vector in the embedding space
- max_len (int, optional, default 300) – The maximum length of item’s document
- num_kernel_per_ws (int, optional, default: 100) – The number of kernel filter in convolutional layer
- dropout_rate (float, optional, default: 0.2) – Dropout rate while training CNN
- give_item_weight (boolean, optional, default: True) – When True, each item will be weighted base on the number of user who have rated this item
- init_params (dict, optional, default: {'U':None, 'V':None, 'W': None}) – Initial U and V matrix and initial weight for embedding layer W
- trainable (boolean, optional, default: True) – When False, the model is not trained and Cornac assumes that the model already pre-trained (U and V are not None).
References
- Donghyun Kim1, Chanyoung Park1. ConvMF: Convolutional Matrix Factorization for Document Context-Aware Recommendation. In :10th ACM Conference on Recommender Systems Pages 233-240
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fit(train_set)[source]¶ Fit the model.
Parameters: train_set ( cornac.data.MultimodalTrainSet) – Multimodal training set.