#recsys

Deep & Cross Network (DCN) is a neural network architecture developed by Google that combines a cross network with a deep network to efficiently learn explicit and implicit feature interactions.

Deep Learning Recommendation Model is a neural network architecture developed by Facebook to handle large-scale recommendation tasks efficiently.

Negative sampling trains a model by contrasting each observed positive with a small set of sampled alternatives instead of every item in the catalog.

Neural Collaborative Filtering (NCF) is a deep learning approach to collaborative filtering for recommendation systems that aims to learn the complex user-item interaction function using neural networks.

Two-tower architecture is a neural network approach used in recommendation systems for candidate generation and retrieval.

Wide & Deep Learning is a joint model architecture developed by Google for recommendation systems and search ranking.

LogQ correction is a bias correction technique used in recommendation systems to account for non-uniform sampling during training.

Cold start is the problem of producing useful recommendations when a user, item, or market has little or no reliable interaction history.

Recommendation system metrics are quantitative measures used to evaluate the performance and effectiveness of recommendation algorithms.

This note covers how recommendation systems are designed, built, evaluated, deployed, and debugged in production.