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

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

Will continue on production infra later…