Neural Input Search for Large Scale Recommendation Models

Recommendation problems with large numbers of discrete items, such as products, webpages, or videos, are ubiquitous in the technology industry. Deep neural networks are being increasingly used for these recommendation problems. These models use embeddings to represent discrete items as continuous vectors, and the vocabulary sizes and embedding dimensions, despite their heavy influence on the model’s accuracy, are often manually selected in a heuristical manner.