Ebay, Incorporated

Paypal, Inc.

Ryan McDonald

Muhammad Faisal Rehman

Olivier G Dumon

Sanjay Pundlkrao Ghatare

Ken Sun

Julie Lavee Netzloff

Naren M Chittar

John Roper

Michael Schmitz

Oliver G Dumon

Ashita Achuthan

Richard Dean Henderson

Sandip Namdeo Gaikwad

Joseph Anthony Beynon

Qian Zhong

Jingzhou Hua

Ted Tao Yuan

Methods and systems for analyzing, ordering and presenting item listings are described. In one example embodiment, a search query is processed to identify item listings satisfying the search query. Then, for each item listing that satisfies the search query, a ranking score is derived and assigned to the item listing. The ranking score is based in part on a relevance score, a listing quality score and a business rules score (or, adjustment factor). Finally, the item listings are ordered, based on their corresponding ranking score, and presented in order in a search results page.

Methods and systems for deriving a score with which item listings are ordered when presented in search results

Methods and systems enable access to listing performance data associated with a search engine. A request for listing performance data for an item is received from a client computing system. The request is associated with a keyword and/or a category. The request is processed to retrieve the listing performance data corresponding to the item, and a value is determined that indicates a strength of the item in a search results listing having a plurality of search results based on a search using the keyword and/or the category. The listing performance data and value are provided for presentation.

Method and system for exposing data used in ranking search results

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 enable access to listing performance data used in conjunction with a search engine to order search results. A request for listing performance data for a specific item listing from a seller is received from a client computing system. The request specifies a keyword and/or a category. The request is processed to retrieve the listing performance data corresponding to the specific item listing, and a position value is determined that indicates a position at which the specific item listing would appear to a potential buyer in a search results listing having a plurality of item listings based on a search using the keyword and/or the category. The listing performance data and position value are transmitted to the client computing system for presentation to the seller.

Methods and systems for using a demand metric for disambiguating a keyword entered from a user of a client machine, the keyword ambiguously identifying a product category, deriving a dominant product category that indicates the probable identity of the product category, and providing selectable images of aspects of the dominant product category for rendering at the client machine. Selection of one of the selectable images of aspects of the dominant product category disambiguates the identity of the ambiguously identified product category. Additional aspects of the disambiguated product category are then presented to the user.

Methods and systems for determining a product category

Methods and systems for enabling access to listing performance data, used in conjunction with a search engine to order search results, are described. Consistent with some embodiments, a listing performance data engine module processes API commands received from client computing systems. The data engine module processes the API commands to identify and communicate relevant listing performance data that is used by a listing performance algorithm to assign a listing performance score to item listings satisfying a search request. The listing performance data provides sellers who have items listed for sale an insight as to how their item listings are performing in comparison to other item listings.

A method and system for determining an order of presentation of the search results is provided. An example system comprises a search request detector, a search engine, and an intermingler. The search request detector may be configured to receive a search request, and the search engine may be configured to determine search results based on the search request. The search results may comprise a first set of items of a first type and a second set of items of a second type. The number of items in the first set and the number of items in the second may reflect a predetermined target ratio. The intermingler may be configured to determine an order of presentation of the search results in a list of search results based on the target ratio.

Method and system for determining an order of presentation of search results

A system and method extracts relevant image features about listed products in a network-based publication system for enabling image similarity searching of such listed products. When a seller lists a product, an image of the product 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 of a listed product such as edge, color, texture, or words. These digests are then stored in a database, where the digests can be used to retrieve listings of products by image similarity at scale. A similar process can be performed for a query by image similarity searching the database for products similar to the query. When a product is located by image similarity search, a selectable icon is provided to enable more products like the located product to be located by image similarity search.

Methods and systems for assessing the quality of an item listing are described. In an example embodiment, a listing quality score for an item listing is derived as a weighted sum of first and second parts. The first part represents a predicted score based on a comparison of item attributes for the item listing that are known at listing time, with item attributes of similar item listings that have historical data available for assessing their actual performance. The second part is based on one or more observed demand metrics representing actual historical performance of the item listing. The weighting factor is derived, such that over time, the emphasis shifts from the predicted to the observed score.

Methods and systems for assessing the quality of an item listing

Using a processor, receiving, a query including a query image. A database is searched for a set of images similar to the query image, using a two-pass search. The results of the searching are then provided, the results including image members of the set of images similar to the query image. The first pass may be performed using a TF-IDF algorithm and the second pass ranks a predetermined number of results from the first pass by a best match algorithm or other type of algorithm. The type of second pass algorithm may be selectable by a user. If an end signal is not detected, a further two-pass search may be made based on a subsequent query having a subsequent query image. The subsequent query image may be one of the results of the previous two pass search. A subsequent query image may change from an image of a product of a first product category to an image of a product of a second product category.

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San Francisco, CA, United States of America

Ryan McDonald