def __init__(self, users=None, items=None, config=None): BaseRecommender.__init__(self, users, items, config) self.P = None self.Q = None self.b_i = None if config and self.users and self.items: nb_users = len(self.users) nb_movies = len(self.items) nb_latent_f = config['nb_latent_f'] scale = config['init_params_scale'] if 'init_params_scale' in config else 0.001 params = self._init_params(nb_users, nb_movies, nb_latent_f, scale=scale) self._set_params(params)
def __init__(self, users=None, items=None, config=None): BaseRecommender.__init__(self, users, items, config) self.sparse_matrix = None self.similarity_matrix = None self.intercepts = None if config: self.ignore_negative_weights = config['ignore_negative_weights'] self.model = SGDRegressor(penalty='elasticnet', n_iter=config['nb_epochs'], fit_intercept=config['fit_intercept'], alpha=config['alpha'], l1_ratio=config['l1_ratio']) self.intercepts = {}
def __init__(self, users=None, items=None, config=None, movie_to_imdb=None, user_pref_model=None, d2v_model=None): BaseRecommender.__init__(self, users, items, config) self.user_factors = None self.item_factors = None self.item_bias = None self.global_bias = None self.user_pref_model = user_pref_model self.d2v_model = d2v_model self.movie_to_imdb = movie_to_imdb if config and self.users and self.items: nb_users = len(self.users) nb_movies = len(self.items) nb_latent_f = config['nb_latent_f'] params = self._init_params(nb_users, nb_movies, nb_latent_f) self._set_params(params)
def __init__(self, users=None, items=None, config=None): BaseRecommender.__init__(self, users, items, config) self.user_factors = None self.item_factors = None self.user_interest_factors = None self.user_interest_bias = None self.item_bias = None self.global_bias = None self.si_user_model = None self.si_item_model = None if config and self.users and self.items: nb_users = len(self.users) nb_movies = len(self.items) nb_latent_f = config['nb_latent_f'] nb_user_pref = config['nb_user_pref'] scale = config['init_params_scale'] if 'init_params_scale' in config else 0.001 params = self._init_params(nb_users, nb_movies, nb_latent_f, nb_user_pref, scale=scale) self._set_params(params)
def __init__(self, users=None, items=None, config=None, movie_to_imdb=None,d2v_model=None): BaseRecommender.__init__(self, users, items, config) self.user_factors = None self.item_factors = None self.item_bias = None self.global_bias = None self.nn_w1 = None self.nn_w2 = None self.d2v_model = d2v_model self.movie_to_imdb = movie_to_imdb if config and self.users and self.items: nb_users = len(self.users) nb_movies = len(self.items) self.nb_latent_f = config['nb_latent_f'] nb_d2v_features = config['nb_d2v_features'] nb_hidden_neurons = config['nb_hidden_neurons'] params = self._init_params(nb_users, nb_movies, self.nb_latent_f, nb_d2v_features, nb_hidden_neurons) self._set_params(params)