AutoRec: Rating Prediction with Autoencoders
============================================
Although the matrix factorization model achieves decent performance on
the rating prediction task, it is essentially a linear model. Thus, such
models are not capable of capturing complex nonlinear and intricate
relationships that may be predictive of usersâ€™ preferences. In this
section, we introduce a nonlinear neural network collaborative filtering
model, AutoRec :cite:`Sedhain.Menon.Sanner.ea.2015`. It identifies
collaborative filtering (CF) with an autoencoder architecture and aims
to integrate nonlinear transformations into CF on the basis of explicit
feedback. Neural networks have been proven to be capable of
approximating any continuous function, making it suitable to address the
limitation of matrix factorization and enrich the expressiveness of
matrix factorization.
On one hand, AutoRec has the same structure as an autoencoder which
consists of an input layer, a hidden layer, and a reconstruction
(output) layer. An autoencoder is a neural network that learns to copy
its input to its output in order to code the inputs into the hidden (and
usually low-dimensional) representations. In AutoRec, instead of
explicitly embedding users/items into low-dimensional space, it uses the
column/row of the interaction matrix as the input, then reconstructs the
interaction matrix in the output layer.
On the other hand, AutoRec differs from a traditional autoencoder:
rather than learning the hidden representations, AutoRec focuses on
learning/reconstructing the output layer. It uses a partially observed
interaction matrix as the input, aiming to reconstruct a completed
rating matrix. In the meantime, the missing entries of the input are
filled in the output layer via reconstruction for the purpose of
recommendation.
There are two variants of AutoRec: user-based and item-based. For
brevity, here we only introduce the item-based AutoRec. User-based
AutoRec can be derived accordingly.
Model
-----
Let :math:`\mathbf{R}_{*i}` denote the :math:`i^\mathrm{th}` column of
the rating matrix, where unknown ratings are set to zeros by default.
The neural architecture is defined as:
.. math::
h(\mathbf{R}_{*i}) = f(\mathbf{W} \cdot g(\mathbf{V} \mathbf{R}_{*i} + \mu) + b)
where :math:`f(\cdot)` and :math:`g(\cdot)` represent activation
functions, :math:`\mathbf{W}` and :math:`\mathbf{V}` are weight
matrices, :math:`\mu` and :math:`b` are biases. Let :math:`h( \cdot )`
denote the whole network of AutoRec. The output
:math:`h(\mathbf{R}_{*i})` is the reconstruction of the
:math:`i^\mathrm{th}` column of the rating matrix.
The following objective function aims to minimize the reconstruction
error:
.. math::
\underset{\mathbf{W},\mathbf{V},\mu, b}{\mathrm{argmin}} \sum_{i=1}^M{\parallel \mathbf{R}_{*i} - h(\mathbf{R}_{*i})\parallel_{\mathcal{O}}^2} +\lambda(\| \mathbf{W} \|_F^2 + \| \mathbf{V}\|_F^2)
where :math:`\| \cdot \|_{\mathcal{O}}` means only the contribution of
observed ratings are considered, that is, only weights that are
associated with observed inputs are updated during back-propagation.
.. code:: python
from d2l import mxnet as d2l
from mxnet import autograd, gluon, np, npx
from mxnet.gluon import nn
import mxnet as mx
import sys
npx.set_np()
Implementing the Model
----------------------
A typical autoencoder consists of an encoder and a decoder. The encoder
projects the input to hidden representations and the decoder maps the
hidden layer to the reconstruction layer. We follow this practice and
create the encoder and decoder with dense layers. The activation of
encoder is set to ``sigmoid`` by default and no activation is applied
for decoder. Dropout is included after the encoding transformation to
reduce over-fitting. The gradients of unobserved inputs are masked out
to ensure that only observed ratings contribute to the model learning
process.
.. code:: python
class AutoRec(nn.Block):
def __init__(self, num_hidden, num_users, dropout=0.05):
super(AutoRec, self).__init__()
self.encoder = nn.Dense(num_hidden, activation='sigmoid',
use_bias=True)
self.decoder = nn.Dense(num_users, use_bias=True)
self.dropout = nn.Dropout(dropout)
def forward(self, input):
hidden = self.dropout(self.encoder(input))
pred = self.decoder(hidden)
if autograd.is_training(): # Mask the gradient during training
return pred * np.sign(input)
else:
return pred
Reimplementing the Evaluator
----------------------------
Since the input and output have been changed, we need to reimplement the
evaluation function, while we still use RMSE as the accuracy measure.
.. code:: python
def evaluator(network, inter_matrix, test_data, devices):
scores = []
for values in inter_matrix:
feat = gluon.utils.split_and_load(values, devices, even_split=False)
scores.extend([network(i).asnumpy() for i in feat])
recons = np.array([item for sublist in scores for item in sublist])
# Calculate the test RMSE
rmse = np.sqrt(np.sum(np.square(test_data - np.sign(test_data) * recons))
/ np.sum(np.sign(test_data)))
return float(rmse)
Training and Evaluating the Model
---------------------------------
Now, let us train and evaluate AutoRec on the MovieLens dataset. We can
clearly see that the test RMSE is lower than the matrix factorization
model, confirming the effectiveness of neural networks in the rating
prediction task.
.. code:: python
devices = d2l.try_all_gpus()
# Load the MovieLens 100K dataset
df, num_users, num_items = d2l.read_data_ml100k()
train_data, test_data = d2l.split_data_ml100k(df, num_users, num_items)
_, _, _, train_inter_mat = d2l.load_data_ml100k(train_data, num_users,
num_items)
_, _, _, test_inter_mat = d2l.load_data_ml100k(test_data, num_users,
num_items)
train_iter = gluon.data.DataLoader(train_inter_mat, shuffle=True,
last_batch="rollover", batch_size=256,
num_workers=d2l.get_dataloader_workers())
test_iter = gluon.data.DataLoader(np.array(train_inter_mat), shuffle=False,
last_batch="keep", batch_size=1024,
num_workers=d2l.get_dataloader_workers())
# Model initialization, training, and evaluation
net = AutoRec(500, num_users)
net.initialize(ctx=devices, force_reinit=True, init=mx.init.Normal(0.01))
lr, num_epochs, wd, optimizer = 0.002, 25, 1e-5, 'adam'
loss = gluon.loss.L2Loss()
trainer = gluon.Trainer(net.collect_params(), optimizer,
{"learning_rate": lr, 'wd': wd})
d2l.train_recsys_rating(net, train_iter, test_iter, loss, trainer, num_epochs,
devices, evaluator, inter_mat=test_inter_mat)
.. parsed-literal::
:class: output
train loss 0.000, test RMSE 0.901
36202297.4 examples/sec on [gpu(0), gpu(1)]
.. figure:: output_autorec_4e5735_7_1.svg
Summary
-------
- We can frame the matrix factorization algorithm with autoencoders,
while integrating non-linear layers and dropout regularization.
- Experiments on the MovieLens 100K dataset show that AutoRec achieves
superior performance than matrix factorization.
Exercises
---------
- Vary the hidden dimension of AutoRec to see its impact on the model
performance.
- Try to add more hidden layers. Is it helpful to improve the model
performance?
- Can you find a better combination of decoder and encoder activation
functions?
`Discussions `__