Tech Blog

Learn how eBay's architecture knowledge graph was developed; the benefits eBay has received from it; and the use cases we see now and in the future for this approach.

In this article, we propose a set of better practices, designed by and for eBay ML scientists, for facilitating weaving ML modeling into the cyclical Agile process flow.

Trade has played a critical role in the history of humanity and yet, data from ecommerce, the modern form of trading, has received limited attention from academia. We at eBay want to change that.

In the era of artificial intelligence, machine learning, and cloud technology, data is growing exponentially. eBay data continues to grow, serving more than 182 million buyers and $13.4 billion of transactions completed on mobile devices. Understanding how to manage data is a key to success. System hardware platforms must be designed for the data.

How do you normalize Edit Distance? Some simple ideas to get useful numbers about the changes in your text.

If you change a sentence, should you see the characters or words that changed? Edit Distance is back to help you figure this out.

What is Edit Distance? How could it be used to measure quality? Find out the basics about this simple metric used for Machine Translation.

Our newest experience helps you find more products you just can't describe.

Generating relevant complementary item recommendations that drive conversion at eBay is a challenging problem. In this blog post, we describe some of these challenges, and how we incorporated several different signals, including behavior-based (co-purchase, co-view, co-search, popularity) and content-based (title text), to significantly enrich the number and quality of candidate recommendations. This can produce an improved user shopping experience, which can lead to increased transactions between eBay buyers and sellers, and an increase in the number of items bought, which is good for the eBay marketplace as a whole.

Incorporating knowledge graphs into recommender systems has attracted increasing attention in recent years. By exploring the interlinks within a knowledge graph, the connectivity between users and items can be discovered as paths, which provide rich and complementary information to user-item interactions. Such connectivity not only reveals the semantics of entities and relations, but also helps to comprehend a user’s interest. However, existing efforts have not fully explored this connectivity to infer user preferences, especially in terms of modeling the sequential dependencies within and holistic semantics of a path. We have developed a new model named Knowledge-aware Path Recurrent Network (KPRN) to exploit knowledge graphs for recommendation.