This chapter explores the integration of deep learning in recommender systems, highlighting its significance as a leading application area with substantial business value. We examine notable advancements driven by industry leaders like Meta, Google, Airbnb, and Alibaba. These innovations mark a transformative shift toward deep learning in recommender systems, evidenced by Alibaba’s ongoing innovations in e-commerce and Airbnb’s applications in search and recommendation. For aspiring recommender system engineers, the current era of open-source code and knowledge sharing provides unparalleled access to cutting-edge applications and insights from industry pioneers. This chapter aims to build a foundational understanding of deep learning recommender systems developed by Meta, Airbnb, YouTube, and Alibaba, encouraging readers to focus on technical details and engineering practices for practical application.

This concluding chapter revisits the overarching architecture of recommender systems, encouraging readers to synthesize the technical details discussed throughout the book into a cohesive knowledge framework. Initially introduced in Chapter 1, the technical architecture diagram serves as a foundational reference for understanding the field. With a comprehensive overview of each module now complete, readers are invited to refine their interpretations of the architecture. Establishing a personal knowledge framework is crucial for identifying gaps, appreciating details, and maintaining a holistic view of the subject.

Embedding technology plays a pivotal role in deep learning, particularly in industries such as recommendation, advertising, and search. It is considered a fundamental operation for transforming sparse vectors into dense representations that can be further processed by neural networks. Beyond its basic role, embedding technology has evolved significantly in both academia and industry, with applications ranging from sequence processing to multifeature heterogeneous data. This chapter discusses the basics of embedding, its evolution from Word2Vec to graph embeddings and multifeature fusion, and its applications in recommender systems, with an emphasis on online deployment and inference.

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.

This chapter dissects the modeling of time series and the estimation of scaling laws. It introduced methodologies to estimate the generalized Hurst exponent and discusses stationarity tests. Tools for modeling temporal patterns such as rolling windows, empirical mode decomposition, and temporal clustering are introduced.

This chapter introduces the concept of entropy and its significance in modeling. The focus extends to joint entropy, Kullback–Leibler divergence, and conditional entropy. Readers are equipped with tools to quantify information and uncertainty, pivotal in probabilistic modeling. The chapter focuses on Shannon entropy but also introduces to other entropy formulations.