"""Implementation of SVD."""
import numpy as np
from ..bases import EmbedBase, ModelMeta
from ..embedding import normalize_embeds
from ..tfops import reg_config, sess_config, tf
[docs]class SVD(EmbedBase, metaclass=ModelMeta, backend="tensorflow"):
"""*Singular Value Decomposition* algorithm.
Parameters
----------
task : {'rating', 'ranking'}
Recommendation task. See :ref:`Task`.
data_info : :class:`~libreco.data.DataInfo` object
Object that contains useful information for training and inference.
loss_type : {'cross_entropy', 'focal'}, default: 'cross_entropy'
Loss for model training.
embed_size: int, default: 16
Vector size of embeddings.
norm_embed : bool, default: False
Whether to l2 normalize output embeddings.
n_epochs: int, default: 10
Number of epochs for training.
lr : float, default 0.001
Learning rate for training.
lr_decay : bool, default: False
Whether to use learning rate decay.
epsilon : float, default: 1e-5
A small constant added to the denominator to improve numerical stability in
Adam optimizer.
According to the `official comment <https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/training/adam.py#L64>`_,
default value of `1e-8` for `epsilon` is generally not good, so here we choose `1e-5`.
Users can try tuning this hyperparameter if the training is unstable.
reg : float or None, default: None
Regularization parameter, must be non-negative or None.
batch_size : int, default: 256
Batch size for training.
sampler : {'random', 'unconsumed', 'popular'}, default: 'random'
Negative sampling strategy.
- ``'random'`` means random sampling.
- ``'unconsumed'`` samples items that the target user did not consume before.
- ``'popular'`` has a higher probability to sample popular items as negative samples.
.. versionadded:: 1.1.0
num_neg : int, default: 1
Number of negative samples for each positive sample, only used in `ranking` task.
seed : int, default: 42
Random seed.
lower_upper_bound : tuple or None, default: None
Lower and upper score bound for `rating` task.
tf_sess_config : dict or None, default: None
Optional TensorFlow session config, see `ConfigProto options
<https://github.com/tensorflow/tensorflow/blob/v2.10.0/tensorflow/core/protobuf/config.proto#L431>`_.
References
----------
*Yehuda Koren* `Matrix Factorization Techniques for Recommender Systems
<https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf>`_.
"""
user_variables = ["bu_var", "pu_var"]
item_variables = ["bi_var", "qi_var"]
def __init__(
self,
task,
data_info,
loss_type="cross_entropy",
embed_size=16,
norm_embed=False,
n_epochs=20,
lr=0.001,
lr_decay=False,
epsilon=1e-5,
reg=None,
batch_size=256,
sampler="random",
num_neg=1,
seed=42,
lower_upper_bound=None,
tf_sess_config=None,
):
super().__init__(task, data_info, embed_size, lower_upper_bound)
self.all_args = locals()
self.sess = sess_config(tf_sess_config)
self.loss_type = loss_type
self.norm_embed = norm_embed
self.n_epochs = n_epochs
self.lr = lr
self.lr_decay = lr_decay
self.epsilon = epsilon
self.reg = reg_config(reg)
self.batch_size = batch_size
self.sampler = sampler
self.num_neg = num_neg
self.seed = seed
def build_model(self):
self.user_indices = tf.placeholder(tf.int32, shape=[None])
self.item_indices = tf.placeholder(tf.int32, shape=[None])
self.labels = tf.placeholder(tf.float32, shape=[None])
self.bu_var = tf.get_variable(
name="bu_var",
shape=[self.n_users],
initializer=tf.zeros_initializer(),
regularizer=self.reg,
)
self.bi_var = tf.get_variable(
name="bi_var",
shape=[self.n_items],
initializer=tf.zeros_initializer(),
regularizer=self.reg,
)
self.pu_var = tf.get_variable(
name="pu_var",
shape=[self.n_users, self.embed_size],
initializer=tf.glorot_uniform_initializer(),
regularizer=self.reg,
)
self.qi_var = tf.get_variable(
name="qi_var",
shape=[self.n_items, self.embed_size],
initializer=tf.glorot_uniform_initializer(),
regularizer=self.reg,
)
bias_user = tf.nn.embedding_lookup(self.bu_var, self.user_indices)
bias_item = tf.nn.embedding_lookup(self.bi_var, self.item_indices)
embed_user = tf.nn.embedding_lookup(self.pu_var, self.user_indices)
embed_item = tf.nn.embedding_lookup(self.qi_var, self.item_indices)
if self.norm_embed:
embed_user, embed_item = normalize_embeds(
embed_user, embed_item, backend="tf"
)
self.output = (
bias_user
+ bias_item
+ tf.reduce_sum(tf.multiply(embed_user, embed_item), axis=1)
)
def set_embeddings(self):
bu, bi, pu, qi = self.sess.run(
[self.bu_var, self.bi_var, self.pu_var, self.qi_var]
)
user_bias = np.ones([len(pu), 2], dtype=pu.dtype)
user_bias[:, 0] = bu
item_bias = np.ones([len(qi), 2], dtype=qi.dtype)
item_bias[:, 1] = bi
if self.norm_embed:
pu, qi = normalize_embeds(pu, qi, backend="np")
self.user_embeds_np = np.hstack([pu, user_bias])
self.item_embeds_np = np.hstack([qi, item_bias])