Mercari, Inc.

Yu Ishikawa

Robin D Clark

Chiaki Osaka

Mohammad-Mahdi Mozzami

Eric Turner

Masanori Uehara

Masonori Uehara

Lawrie Crate

Lawrence Cate

Disclosed herein are various embodiments for estimating shipping costs of items purchased on an online market. An embodiment operates by detecting a sales transaction between a seller and a buyer for an item via an online marketplace. An item vector representing the item as created by a deep learning model based on a plurality of characteristics associated with the item is received. A set of proximate vectors to the item vector, each of the proximate vectors corresponding to a past listings where a past shipping cost from shipping a product in the past listing is known are identified. A weighted average is calculated from the past shipping cost of each of the past listings. A dimensional weight of the item is estimated based on the weight average, and a shipping cost for the item is estimated based on the estimated dimensional weight prior to providing the item to a carrier.

Estimating shipping costs with machine learning

Disclosed herein are system, method, and computer program product embodiments for estimating shipping costs of items purchased in an online market using machine learning techniques. By determining in real-time the dimensional weight of an item with reference to a machine learning model describing past transactions, a buyer and seller in the online market can finalize the transaction in real-time. The machine learning model further implements a bias within the machine learning algorithm towards heavier estimation to avoid undercharging the market participants for shipping costs. One embodiment involves using these cost-estimation techniques in the context of a local shipping feature, which allows buyers and sellers to schedule a same-day delivery by seamlessly involving a local carrier.

Growing community of inventors

Palo Alto, CA, United States of America

Yu Ishikawa