Source code for cornac.models.vbpr.recom_vbpr

# -*- coding: utf-8 -*-
"""
@author: Guo Jingyao <jyguo@smu.edu.sg>
"""

from .vbpr import *
from ..recommender import Recommender


[docs]class VBPR(Recommender): """Visual Bayesian Personalized Ranking. Parameters ---------- k: int, optional, default: 5 The dimension of the latent factors. d: 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. aux_info:ndarray, shape (n_items, feature dimension), optional, default:None Image features of items learning_rate: float, optional, default: 0.001 The learning rate for SGD. lamda: float, optional, default: 0.01 The regularization parameter. batch_size: int, optional, default: 100 The batch size for SGD. name: string, optional, default: 'BRP' 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. E: ndarray, shape (d, feature dimension) The matrix embedding deep CNN feature, optional initialization via init_params. Ue: ndarray, shape (n_users, d) The visual factors of users, optional initialization via init_params. References ---------- * HE, Ruining et MCAULEY, Julian. VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback. In : AAAI. 2016. p. 144-150. """ def __init__(self, k=10, d=10, max_iter=100, aux_info=None, learning_rate=0.001, lamda=0.01, batch_size=100, name="vbpr", trainable=True, init_params=None): Recommender.__init__(self, name=name, trainable=trainable) self.k = k self.d = d self.init_params = init_params self.aux_info = aux_info self.max_iter = max_iter self.name = name self.learning_rate = learning_rate self.lamda = lamda self.batch_size = batch_size self.U = init_params['U'] # matrix of user factors self.V = init_params['V'] # matrix of item factors self.E = init_params['E'] # matrix embedding deep CNN feature self.Ue = init_params['Ue'] # visual factors of users # fit the recommender model to the traning data
[docs] def fit(self, X): """Fit the model to observations. Parameters ---------- X: scipy sparse matrix, required the user-item preference matrix (traning data), in a scipy sparse format\ (e.g., csc_matrix). """ if self.trainable: # change the data to original user Id item Id and rating format cooX = X.tocoo() data = np.ndarray(shape=(len(cooX.data), 3), dtype=float) data[:, 0] = cooX.row data[:, 1] = cooX.col data[:, 2] = cooX.data print('Learning...') res = vbpr(X, data, k=self.k, d=self.d, aux_info=self.aux_info, n_epochs=self.max_iter, lamda=self.lamda, learning_rate=self.learning_rate, batch_size=self.batch_size, init_params=self.init_params) self.U = res['U'] self.V = res['V'] self.Ue = res['Ue'] self.E = res['E'] print('Learning completed') else: print('%s is trained already (trainable = False)' % (self.name))
[docs] def score(self, user_index, item_indexes = None): """Predict the scores/ratings of a user for a list of items. Parameters ---------- user_index: int, required The index of the user for whom to perform score predictions. item_indexes: 1d array, optional, default: None A list of item indexes for which to predict the rating score.\ When "None", score prediction is performed for all test items of the given user. Returns ------- Numpy 1d array Array containing the predicted values for the items of interest """ if item_indexes is None: user_pred = self.U[user_index, :].dot(self.V.T) + self.Ue[user_index, :].dot(self.E).dot(self.aux_info.T) # user_pred = self.U[index_user, :].dot(self.V.T) + self.Ue[index_user, :]*self.E.dot(self.aux_info.T) else: user_pred = self.U[user_index, :].dot(self.V[item_indexes,:].T) + self.Ue[user_index, :].dot(self.E).dot(self.aux_info[item_indexes,:].T) # transform user_pred to a flatten array, but keep thinking about another possible format user_pred = np.array(user_pred, dtype='float64').flatten() return user_pred
[docs] def rank(self, user_index, known_items = None): """Rank all test items for a given user. Parameters ---------- user_index: int, required The index of the user for whom to perform item raking. known_items: 1d array, optional, default: None A list of item indices already known by the user Returns ------- Numpy 1d array Array of item indices sorted (in decreasing order) relative to some user preference scores. """ u_pref_score = np.array(self.score(user_index)) if known_items is not None: u_pref_score[known_items] = None rank_item_list = (-u_pref_score).argsort() # ordering the items (in decreasing order) according to the preference score return rank_item_list