Aplicação de Deep Learning para relacionar Pins

Ao que parece, o Pinterest está virando a nova casa de força de Deep Learning aplicada à imagens.

Using deep learning to generate Related Pins

We built Pin2Vec to embed all the Pins in a 128-dimension space. First, we label a Pin with all the other Pins someone has saved in his/her activity session, each as a Pin tuple. Pin tuples are used in supervised training to train the embedding matrix for each of the tens of millions of Pins of the vocabulary. We use TensorFlow as the trainer. At serving time, a set of nearest neighbors are found as Related Pins in the space for each of the Pins.

Training data is collected from recent engagement, such as saving or clicking, and a sliding window is applied. Low quality Pins and those not engaged with are removed from training. Then, each Pin is assigned with a unique Pin ID. Within the sliding window, training pairs are extracted such that the first Pin is the example and each of the following Pins is its label. Figure 3 illustrates an example session and training pairs. In our case, you can imagine each user session is a sentence with Pins as words.

We used a feedforward neural network with a hidden layer of 128 dimensions. Figure 4 shows the architecture. The network is inspired by word2vec. The input vector is a one-hot vector with a size of vocabulary and, in our case, is tens of millions of Pins. The vector is reduced to the 128-dimension vector by multiplying with the hidden layer weight matrix. An eLu activation function is applied after hidden layer. At last the hidden layer output is multiplied with the softmax matrix and a cross-entropy is used to calculate the loss. We sampled 64 negative Pins in loss optimization in lieu of iterating on tens of millions of Pins. We trained the Pin2Vec embedding on machines with 32 cores and 244GB memory.