Connecting sellers and buyers on the world's largest inventory.

At eBay, sellers can offer virtually any type of listing, rendering the world's largest inventory, with well over a billion items. Yet, the noisy nature of the input data and the extremely long-tailed item distribution pose a variety of challenges for search and recommendation, such as understanding the unique attributes (aspects) of the products, their importance to both sellers and buyers, and their intra-relationships, all essential to providing a high-quality user experience on the site. In this talk, I will present several challenges and corresponding solution frameworks recently developed at eBay Research for aspect extraction, normalization, weighting, and relation inference; the mapping of relationships between e-commerce entities for matching uploaded listings to catalog products and feeding the e-commerce knowledge graph; the recommendation of categories for sellers' contributions; and the automatic generation of textual fields (title, description) to bridge the gap between sellers and buyers by helping them speak the same language. Our methods combine a variety of language processing and computer vision approaches applied on the different types of data contributed by sellers. Learning to rank, named entity recognition, object identification, machine translation, and summarization are just a few example techniques that come to play. Our methods drive different usage scenarios by enabling a better representation of users and items and an effective computation of their similarities. I will also describe how our applied research teams perform their work, from the development of initial prototypes, through offline and online production processes, to different evaluation schemes. I will conclude the talk by reviewing open challenges in large-scale e-commerce that will have to be addressed in the years to come.