Ebay, Incorporated

A9.com, Inc.

Anjan Goswami

Sung Hwan Chung

Sanjay Pundlkrao Ghatare

Naren M Chittar

Ali Dasdan

Sandip Namdeo Gaikwad

Daniel Eric Rose

Muhammad Faisal Rehman

Kent Andrew Edmonds

Qian Zhong

Gerhard Fliedner

Swaminathan Sundaramurthy

Anuradha Subramanian

A machine may be configured to access an image of an item described by a description of the item. The machine may determine an image quality score of the image based on an analysis of the image. A request for search results that pertain to the description may be received by the machine, and the machine may present a search result that references the item's image, based on its image quality score. Also, the machine may access images of items and descriptions of items and generate a set of most frequent text tokens included in the item descriptions. The machine may identify an image feature exhibited by an item's image and determine that a text token from the corresponding item description matches one of the most frequent text tokens. A data structure may be generated by the machine to correlate the identified image feature with the text token.

Image-based popularity prediction

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

Methods and systems for selecting listing based on a click predictions score are disclosed. The server system includes one or more processors, memory, and one or more programs stored in the memory. The server system receives a search query from a client system. In response to receiving the search query, the server system identities a plurality of item listings relevant to the received search query. For each identified item listing, the server system generates a click prediction score, wherein a click predication score represents the estimated likelihood that a user will click on the listing. The server system ranks the plurality of item listings based on the generated click prediction score and selects a predetermined number of the item listings for transmission to the client system based on their ranking and associated click prediction score. The server system transmits one or more item listings to the client system for display.

User behavior data can be used with language-specific rule sets to generate stemming databases useful for such tasks as indexing and search query processing. The terms contained in user queries, as well as user behavior with respect to those queries or results returned for those queries, can be analyzed to determine a relative measure (e.g., relative frequency) of various forms of those terms. When generating a stemming database, language-specific rule sets can be used to determine appropriate stemming rules, and where more than one potential rule is identified the user behavior data can be used to select what is likely the appropriate rule, at least for the respective environment. Whitelists or other such components can be used to handle specific or irregular forms that do not follow the general rules or otherwise are exceptions that might not otherwise be processed correctly.

Behavior-driven multilingual stemming

Described herein are methods and systems for promoting item listings that satisfy a query based on the item listings being assigned to certain categories. A query to identify a set of item listings is processed where each item listing associated with an item or service being offered for sale and assigned to a leaf-level category. The scope of the query is determined based on a dictionary of queries or a length of the query when the query is not found in the dictionary. One or more categories are identified based on the scope of the query. A search results page is presented with the item listings from the identified one or more categories.

Growing community of inventors

Sunnyvale, CA, United States of America

Anjan Goswami