After developing a Deep Neural Network model, I have decided to kick it up a notch by trying out with RNN. The RNN model is a regression model that predicts users’ next best items, either for browsing or purchasing. Since it’s a regressor, there’s no hard single item that would be recommended. Instead, it’s predicting values of the next best items features which could be compared against our inventory of items. This way similarity ( 1 – distance ) could be computed and items could then be ranked accordingly. The RNN model that I’ve designed is as follows and it was coded in Chainer.

Schema

 

Learning Curve

Recurrent Unit

[python]
class RNN(chainer.Chain):

def __init__(self, itemEncoder, itemDecoder, n_feas, n_units, dropout_rate, use_gpu=False):
super(RNN, self).__init__()
with self.init_scope():
self.dropout_rate = dropout_rate
self.itemEncoder = itemEncoder
self.l1 = L.LSTM(n_units, n_units)
self.l2 = L.LSTM(n_units, n_units)
# self.l3 = L.GRU(n_units, n_units)
self.itemDecoder = itemDecoder
self.use_gpu = use_gpu

for param in self.params():
param.data[…] = np.random.uniform(-0.1, 0.1, param.data.shape)

def reset_state(self):
self.l1.reset_state()
self.l2.reset_state()
# self.l3.reset_state()

def __call__(self, xs):
if self.use_gpu:
xs = F.transpose(xs, (1, 0, 2))
else:
xs = np.transpose(xs, (1, 0, 2))

for x in xs:
h0 = self.itemEncoder(x)
h1 = self.l1(F.dropout(h0, self.dropout_rate))
h2 = self.l2(F.dropout(h1, self.dropout_rate))
y = self.itemDecoder(F.dropout(h2, self.dropout_rate))
return y
[/python]
 
Will continue on production infra later…