Recommender systems have evolved significantly in response to growing demands, progressing from early methods like Collaborative Filtering (CF) and Logistic Regression (LR) to more advanced models such as Factorization Machines (FM) and Gradient Boosting Decision Trees (GBDT). Since 2015, deep learning has become the dominant approach, leading to the development of hybrid and multimodel frameworks. Despite the rise of deep learning models, traditional recommendation methods still hold valuable advantages due to their interpretability, efficiency, and ease of deployment. Furthermore, these foundational models, such as CF, LR, and FM, form the basis for many deep learning approaches. This chapter explores the evolution of traditional recommendation models, detailing their principles, strengths, and influence on modern deep learning architectures, offering readers a comprehensive understanding of this foundational knowledge.

Building an effective recommender system requires more than just a strong model; it involves addressing a range of complex technical issues that contribute to the overall performance. This chapter explores recommender systems from seven distinct angles, covering feature selection, retrieval layer strategies, real-time performance optimization, scenario-based objective selection, model structure improvements based on user intent, the cold start problem, and the “exploration vs. exploitation” challenge. By understanding these critical aspects, machine learning engineers can develop robust recommender systems with comprehensive capabilities.

Recommender systems have become deeply integrated into daily life, shaping decisions in online shopping, news consumption, learning, and entertainment. These systems offer personalized suggestions, enhancing user experiences in various scenarios. Behind this, machine learning engineers drive the constant evolution of recommendation technology. Described as the “growth engine” of the internet, recommender systems play a critical role in the digital ecosystem. This chapter explores the role of these systems, why they are essential, and how they are architected from a technical perspective.

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.

Chapter 12 is the conclusion. It presents a discussion of how the components of performance evaluation for learning algorithms discussed throughout the book unify into an overall framework for in-laboratory evaluation. This is followed by a discussion of how to move from a laboratory setting to a deployment setting based on the material covered in the last part of the book. We then discuss the potential social consequences of machine learning technology deployment together with their causes, and advocate for the consideration of these consequences as part of the evaluation framework. We follow this discussion with a few concluding remarks.

Chapter 4 reviews frequently used machine learning evaluation procedures. In particular, it presents popular evaluation metrics for binary and multi-class classification (e.g., accuracy, precision/recall, ROC analysis), regression analysis (e.g., mean squared error, root mean squared error, R-squared error), clustering (e.g., Davies–Bouldin Index). It then reviews popular resampling approaches (e.g.,holdout, cross-validation) and statistical tests (e.g., the t-test and the sign test). It concludes with an explanation of why it is important to go beyond these well-known methods in order to achieve reliable evaluation results in all cases.