Mercari, Inc.

Chiaki Osaka

Byong Mok Oh

Dhruv Mehrotra

Patrick Michael Wiseman

Minami Sueyasu

Naoya Makino

Robin D Clark

Mohammad-Mahdi Mozzami

Eric Turner

Yu Ishikawa

Masanori Uehara

Lawrence Cate

Masonori Uehara

Lawrie Crate

Disclosed herein are system, method, and computer program product embodiments for probabilistic search biasing and recommendations. A system including at least one processor may be configured to receive an input relating to an identified item, generate a database query based on the input, and receive a response to the database query. The response may include information on comparable items similar to the identified item, and corresponding metadata for the comparable items. The corresponding metadata may include a range of values corresponding to the comparable items. The system may be further configured to generate a probability score for at least two values of the range of values, based at least on the corresponding metadata for the comparable items. The system may be further configured to output at least a suggested value based on at least the generated probability score for the range of values and prompt for further input.

Probabilistic search biasing and recommendations

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.

Disclosed herein are system, method, and computer program product embodiments for probabilistic item matching and searching. A system including at least one processor may be configured to receive a data point relating to a specific item, generate a database query based on the data point, and receive a response to the database query. The response may include multiple candidate items relating to the specific item. The system may be further configured to receive a first input relating to the specific item and generate a probability score for at least two candidate items of the multiple candidate items in the response, based on at least the second input. The system may be further configured to select a selected item from the candidate items, based on the probability score for the selected item. The system may be further configured to output a reference to, or value representing, the selected item.

Probabilistic item matching and searching

Growing community of inventors

South San Francisco, CA, United States of America

Chiaki Osaka