While previous chapters discussed deep learning recommender systems from a theoretical and algorithmic perspective, this chapter shifts focus to the engineering platform that supports their implementation. Recommender systems are divided into two key components: data and model. The data aspect involves the engineering of the data pipeline, while the model aspect is split between offline training and online serving. This chapter is structured into three parts: (1) the data pipeline framework and big data platform technologies; (2) popular platforms for offline training of recommendation models like Spark MLlib, TensorFlow, and PyTorch; and (3) online deployment and serving of deep learning recommendation models. Additionally, the chapter covers the trade-offs between engineering execution and theoretical considerations, offering insights into how algorithm engineers can balance these aspects in practice.

High-quality behavioural data can be recorded using cheap and simple technologies such as checks sheets and sound recorders. Advances in technologies for data recording have made big data available to behavioural scientists, which in turn has stimulated the development of AI technologies for automated data processing. A data pipeline describes the workflow of data recording, processing and analysis, including details of the technologies used in each step. The choice of technology for capturing behavioural data will depend on the research question and the resources available, the quantity of data required, where the data is to be collected, the amount of interaction with subjects and the likely impact of the technology on the subjects and their environment. Data that are initially recorded in a relatively rich form will require subsequent processing to code behavioural metrics. Coding of data can be either manual or automated using rules-based approaches and machine learning.