# Copyright 2018 The Cornac Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# ============================================================================
from ..mf import MF
[docs]
class SVD(MF):
"""Singular Value Decomposition (SVD).
The implementation is based on Matrix Factorization with biases.
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.
early_stop: boolean, optional, default: False
When True, delta loss will be checked after each iteration to stop learning earlier.
num_threads: int, optional, default: 0
Number of parallel threads for training. If num_threads=0, all CPU cores will be utilized.
If seed is not None, num_threads=1 to remove randomness from parallelization.
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}
seed: int, optional, default: None
Random seed for weight initialization.
If specified, training will take longer because of single-thread (no parallelization).
References
----------
* Koren, Y. Factorization meets the neighborhood: a multifaceted collaborative filtering model. \
In SIGKDD, pp. 426-434. 2008.
* Koren, Y. Factor in the neighbors: Scalable and accurate collaborative filtering. \
In TKDD, 2010.
"""
def __init__(
self,
name="SVD",
k=10,
max_iter=20,
learning_rate=0.01,
lambda_reg=0.02,
early_stop=False,
num_threads=0,
trainable=True,
verbose=False,
init_params=None,
seed=None,
):
super().__init__(
name=name,
k=k,
max_iter=max_iter,
learning_rate=learning_rate,
lambda_reg=lambda_reg,
use_bias=True,
early_stop=early_stop,
num_threads=num_threads,
trainable=trainable,
verbose=verbose,
init_params=init_params,
seed=seed,
)
