You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Production Ranking Systems: A Review 
Chapter Title:  One-box Architecture 
1. Abstract of Cited Paper (BIB001):  User experience in modern content discovery applications critically depends on high-quality personalized recommendations. However, building systems that provide such recommendations presents a major challenge due to a massive pool of items, a large number of users, and requirements for recommendations to be responsive to user actions and generated on demand in real-time. Here we present Pixie, a scalable graph-based real-time recommender system that we developed and deployed at Pinterest. Given a set of user-specific pins as a query, Pixie selects in real-time from billions of possible pins those that are most related to the query. To generate recommendations, we develop Pixie Random Walk algorithm that utilizes the Pinterest object graph of 3 billion nodes and 17 billion edges. Experiments show that recommendations provided by Pixie lead up to 50% higher user engagement when compared to the previous Hadoop-based production system. Furthermore, we develop a graph pruning strategy at that leads to an additional 58% improvement in recommendations. Last, we discuss system aspects of Pixie, where a single server executes 1,200 recommendation requests per second with 60 millisecond latency. Today, systems backed by Pixie contribute to more than 80% of all user engagement on Pinterest.

Literature Review:
One-box architectures allow for the use of complex modeling techniques to be employed in ranking systems. Here a single machine loads training data from a distributed database and trains a model in memory BIB002  . ese architectures are used for representation learning via deep learning models or graph based approaches. Candidate selection can also performed on one-box architectures via tree or graph based methods, which are described further in Section 5. Online models are o en trained via one-box solutions, and can be served in parallel to scale to support requests from high volumes of users, such as millions or billions BIB001 . is architecture a ords ML practitioners the freedom to use di erent tools, without the constraints imposed by distributed architectures. Recent development of containerized solutions allows one-box architectures to simultaneously support a number of tools and solutions by isolating system dependencies  . Practitioners are thus free to employ complex models without the need to work within system speci c paradigms. One-box architectures are limited to in-memory computations and cannot fully leverage the entire dataset and thus are still reliant on distributed datastores to pre-compute data.