Production Ranking Systems: A Review <s> One-box Architecture <s> This issue's "Cloud Tidbit" focuses on container technology and how it's emerging as an important part of the cloud computing infrastructure. It looks at Docker, an open source project that automates the faster deployment of Linux applications, and Kubernetes, an open source cluster manager for Docker containers. <s> BIB001 </s> Production Ranking Systems: A Review <s> One-box Architecture <s> 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. <s> BIB002
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.