Buyers reveal a whole range of behaviors and interests when they browse our pages, so we decided to incorporate these additional purchase intent signals into our machine learning model to improve the relevance of our recommended items.

本文介绍了在eBay的NuGraph分析插件中，作者使用JanusGraph和Spark进行图导出，并通过优化Tinkerpop的SparkGraphComputer和CloneVertexProgram解决了性能问题。作者还介绍了基于导出图的图结构分析方法，并提到了实现顶点度分布的幂律分布模型。作者还提到了解决处理数十亿顶点和边时出现的JVM内存管理问题的技术和改进。此外，文章提供了一些相关工具和资源的链接，包括FoundationDB、Supernode问题的解决方案、Gremlin查询语言、Nebula Algorithm、Async-profiler和Memory Analyzer。

By integrating information from different modalities of eBay listings such as titles and images, we greatly improved the buyer experience and relevance of the recommended items on eBay’s listing pages.

本文讨论了基于local-aggregation集群拓扑, 设计Kafka跨数据中心高可用方案的思路，同时支撑了上下游数据和服务的高可用和连续性。

Here at eBay, we’ve crafted a brand new approach to automate platform evolution for all applications — one that provides a repeatable and reusable infrastructure to streamline evolution.

The Similarity Engine's use cases include item-to-item similarity for text and image modality and user-to-item personalized recommendations based on a user’s historical behavior data.

eBay's core page — the View Item page — gets 250 million views per day. Here's how we took on the task of modernizing it.

A new feature generates customer delight by using modern computer vision techniques to drive new search paradigms through visual discovery.

It’s been effective at speeding up productivity, pushed to eBay, and contributed back to the open source community.

A new risk data hub for decisioning can significantly boost variable time-to-market, with high performance under billions of query traffic.

Contract testing has grown in popularity in recent years with the widespread adoption of microservice architectures. In this article, we will share our experiences with contract testing at eBay.

By leveraging deep learning techniques to compare the titles of product listings, we greatly improved the relevance of our recommended items on eBay’s View Item page.

eBay’s notification platform team built a fault-tolerant, resilient system by injecting faults in the application level.

Determining which promoted auction items to display in a merchandising placement is a multi-sided customer challenge that presents opportunities to both surface amazing auction inventory to buyers and help sellers boost visibility on their auction listings.

A case study demonstrates how eBay's Notification Engineering team optimizes a streaming system in a microservice architecture to support high-throughput broadcast notifications.

Buyers reveal a whole range of behaviors and interests when they browse our pages, so we decided to incorporate these additional purchase intent signals into our machine learning model to improve the relevance of our recommended items.