Mercari, Inc.

Mohammad-Mahdi Mozzami

Robin D Clark

Chiaki Osaka

Eric Turner

Yu Ishikawa

Dhruv Mehrotra

Manikandan Sankar

Masanori Uehara

Lawrence Cate

Masonori Uehara

Lawrie Crate

Laura Furman

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 the prediction of listing attributes for a for sale object (FSO). Data analyzed with respect to historical listings in a database is compared to attributes of the FSO using machine learning (ML) and artificial intelligence (AI), in order for the user to select a category related to the FSO, and for a listing generation module to generate a ultimately reduced set of listings related to the FSO, where the ultimately reduced set is used to predict a price for the FSO based on the selected category, and generate a listing for the FSO, wherein a listing for the FSO is monitored, and information from the monitoring is used as machine learning feedback to train and enhance predictions.

Method, system, and device to intelligently create effective listings

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

Mohammad-Mahdi Mozzami