Ebay, Incorporated

Paypal, Inc.

Oracle International Corporation

Other

Sanjay Pundlkrao Ghatare

Naren M Chittar

Sandip Namdeo Gaikwad

Ryan McDonald

Ali Dasdan

Joseph Anthony Beynon

John Roper

Muhammad Faisal Rehman

Suhail Ansari

Michael Schmitz

Hugh Evan Williams

Olivier G Dumon

Ashit Kumar Jain

Praveen K Jayaraman

Niteen Bhat

Guanglei Song

Anjan Goswami

Sung Hwan Chung

Rami El-Charif

Steven Aihua Chen

Alvaro Bolivar

Yan Chen

Nishith Parikh

Ken Sun

Julie Lavee Netzloff

Vipul C Dalal

Baruch Perlov

Neelakantan Sundaresan

Richard Dean Henderson

Randall Scott Shoup

Ravi Chandra Jammalamadaka

Eric Noel Billingsley

Michael Mathieson

Karin Mauge′

Gunshekar Cemballi

Sameep Navin Solanki

Oliver G Dumon

Ted Tao Yuan

James Ladd

Karin Mauge

Joe Anthony Beynon

Guanglie Song

A system and method for selecting diverse product titles to display on a website are disclosed. In some example embodiments, the methods and systems described herein identify available products to be displayed, cluster the identified products based on their similarity to one another, select one or more products from each of the clusters, and display information, such as a title, associated with the randomly selected products.

Selecting product descriptions to display

A metadata extraction machine accesses an image that depicts an item. The item depicted in the image may have an attribute that describes a characteristic of the item and an attribute descriptor that corresponds to the attribute of the item and specifies a value of the attribute. The metadata extraction machine performs an analysis of the image. The analysis may include identifying the attribute descriptor corresponding to the attribute based on image segmentation of the image. The metadata extraction machine transmits a communication to a device of a user based on the identifying of the attribute descriptor corresponding to the attribute of the item depicted in the image.

Identifying product metadata from an item image

Methods and systems to build containers of uncategorized items are described. In some example embodiments, the methods and systems collect information from titles of items available for purchase within a network-based publication system, generate a title for a container of uncategorized items available for purchase within the network-based publication system that is based on the collected information, and associate two or more uncategorized items available for purchase within the network-based publication system to the container based on a comparison of titles of the two or more uncategorized items and the generated title for the container of uncategorized items.

Building containers of uncategorized items

Selecting diverse product titles to display on a website

Image-based features may be significantly correlated with click-through rates of images that depict a product, which may provide a more formal basis for the informal notion that good quality images will result in better click-through rates, as compared to poor quality images. Accordingly, an image assessment machine is configured to analyze image-based features to improve click-through rates for shopping search applications (e.g., a product search engine). Moreover, the image assessment machine may rank search results based on image quality factors and may notify sellers about low quality images. This may have the effect of improving the brand value for an online shopping website and accordingly have a positive long-term impact on the online shopping website.

Image quality assessment to merchandise an item

A system and method, which may be an offline method, extracts relevant image features about listing items in a network-based publication system for enabling image similarity searching of such listing items. When a seller lists an item, an image of the item is uploaded and may be sent to a picture processing service, which generates several digests. The digests are compressed data structures each representing a particular image feature such as edge, color, texture, or words. These digests are then stored in a search database, where the digests can be used to retrieve listings by image similarity at scale. A similar process can be performed for a query listing for searching the search database for items similar to the query listing.

Finding products that are similar to a product selected from a plurality of products

Methods and systems for simulating a search, for the purpose of evaluating one or more scoring functions used in ordering item listings for presentation in a search results page are described. Consistent with some embodiments, a simulation platform includes a real-time simulation module that receives search result sets for search queries that result in the conclusion of a transaction. The result set is then processed by the simulation platform with one or more test scoring functions, such that the resulting position of the item listing that has resulted in the transaction can be compared with the actual position at which the item listing was displayed in the actual search results. For each test scoring function, an average rank shift metric is determined, and displayed, thereby providing a metric with which to base decisions about which scoring functions to use in the production system.

Methods and systems for selecting an optimized scoring function for use in ranking item listings presented in search results

Methods and systems for deriving a scoring function, for use in ordering a set of search results, are described. Consistent with some embodiments, a simulation platform includes an offline simulator module that receives search result sets for search queries that result in the conclusion of a transaction. The result set is then processed by the offline simulation platform to derive a set of weighting factors for use with one or more parameters in a parametric scoring function. The weighting factors are determined, for example, by specifying an equation setting one or more constraints and then solving the equation for the weighting factors.

Methods and systems for simulating a search to generate an optimized scoring function

Methods and systems to build containers of uncategorized items are described. In some example embodiments, the methods and systems access a search request for items available for purchase via a network-based location, collect information associated with available items having product description information satisfying the search request and located at multiple network-based locations, determine one or more containers that associate items available for purchase based on the collected information, and publish a result for the search request that is based on the determined containers.

Building containers of uncategorized items at multiple locations

Growing community of inventors

San Jose, CA, United States of America

Sanjay Pundlkrao Ghatare