10s or 100s of millions of items in your system, embeddings are challenging to • estimate • store • and compute predictions with. True both in offline and online systems, but problem especially severe online.
to • update models • retrieve candidates • perform scoring. Can you carry out 100 million dot products in under 100ms? Still need to fit in business logic, network latency and so on.
• Negative sampling • BPR loss with tied weights • Adaptive hinge loss with tied weights Binary model: • Embeddings followed by a sign function • Trained by backpropagation
the continuous model implies a 29 times increase in prediction speed at the expense of a modest 4% decrease in accuracy Moving from a float representation to a 1024-dimensional binary representation implies a sharper accuracy drop at 6% in exchange for a smaller 20 times increase in prediction speed.