Ebay, Incorporated

Paypal, Inc.

Xiaoyuan Wu

Alvaro Bolivar

Shuhui Chen

David Goldberg

Yuanjie Liu

Dingxian Wang

Jiaqian Zheng

Raghav Gupta

Shen Huang

Wenyan Hu

Sunil Mohan

Gurudatta Horantur Shivaswamy

Michael Ching

Joseph Anthony Beynon

Yuyangzi Fu

Jingying Wang

Maoquan Wang

Linyin Wu

Manish Harshad Shah

Atcharavan Vajarapong

Song Feng

A first variant of a listing and a second variant of the listing are received. The listing describes an item for sale in an electronic marketplace. The first variant describes a different iteration of the item relative to the second variant. First user activity data of the first variant is generated and second user activity data of the second variant is generated. The first user activity data corresponds to user input metrics associated with the first variant. The second user activity data corresponds to user input metrics associated with the second variant. A search engine receives a first query. Based at least in part on the first user activity data relative to the second user activity data, a first search result associated with the first variant is ranked higher than a second search result associated with the second variant.

System, method, and computer storage media for employing user activity data of variants for improved search

A categorization analysis system is provided. The categorization analysis system includes one or more hardware processors, a memory including a first plurality of listings categorized in a first target category, and a categorization analysis engine executing on the one or more hardware processors. The categorization analysis engine is configured to determine a label for each listing including performing a search on title, select a set of training listings based on the determined labels, train a first model using the set of training listings and the determined labels, the first model being a classification model configured to classify categorization of listings, identify a suspect listing categorized in the first target category, apply the suspect listing to the first model, thereby generating a categorization result for the suspect listing, the categorization result indicating miscategorization of the suspect listing, and identify the suspect listing in the memory as miscategorized.

System and method for identifying miscategorization

Techniques for sharing item images based on a similarity score are described. For example, a machine receives a description of an item from a user device. The machine accesses an existing image. The existing image is associated with one or more existing descriptions of the item. The machine selects, based on a description selection rule, the one or more existing descriptions corresponding to the existing image for comparison with the description received from the user device. The machine generates a similarity score for the existing image based on the comparison between the description received from the user device and the one or more existing descriptions corresponding to the existing image. The machine causes a display, in a user interface of the user device, of a number of existing images having corresponding similarity scores that exceed a threshold value.

Sharing item images based on a similarity score

A method and a system for making contextual recommendations to users on a network-based system. For example, activity associated with a user interacting with a network-based system is tracked. Based, at least in part, on the tracked user activity on the network-based system, a recommendation relationship type is selected. The recommendation relationship type can be either a substitute relationship type or a complement relationship type. A recommended object can be selected based at least in part on the recommendation relationship type and a first object accessed by the user interacting with the network-based system. A recommendation can be generated for the recommended object for presentation to the user interacting with the network-based system.

Systems and methods for contextual recommendations

Techniques for determining search results based on session based refinements are presented herein. A method is disclosed that includes receiving a query in a user session, the query comprising one or more search parameters, detecting, in the user session and after receiving the query, a user event associated with a property of an item, updating a record in a table that associates the query with the property, the table comprising a plurality of records that associate the query with respective item properties, the record comprising the query, the property, and a score, and ranking search results for a subsequent query based on the associated properties indicated in the plurality of records, the subsequent query including the one or more search parameters.

Determining search results using session based refinements

Some embodiments provide personalized search services. The personalized search services operate to contextualize what is being searched for and to provide at least some search results in that context that are specific to the user to whom the results are to be provided.

Personalizing search results

Methods and systems to suggest information to be used with an item being listed for sale may include analyzing a plurality of stored entries of words and identifying certain entries based on desirability scores associated with those entries. The entries and the desirability scores may be identified based on their frequency of use in the plurality of stored entries and/or in the title information or other information associated with items offered for sale.

Method and system of suggesting information used with items offered for sale in a network-based marketplace

Techniques for sharing item images based on a similarity score are described. For example, a machine receives an input item listing for an item from a user device. The machine accesses an existing image that may be associated with one or more existing item listings. The machine identifies a degree of similarity between the input item listing and the one or more existing item listings. The machine generates a similarity score for the existing image based on the degree of similarity. The machine selects one or more existing images corresponding to the one or more existing item listings based on the one or more existing images having corresponding similarity scores that exceed a threshold value. The machine transmits a communication to the user device in response to the receiving of the input item listing. The communication may include the one or more existing images.

A method and a system are disclosed for predicting sales of item listings on a network-based system. For example, historical transaction data generated by the network-based system is accessed to create a prediction model. A feature predictive of an item being sold through the network-based system is selected. A training set is created by extracting the predictive feature from the historical transaction data. The prediction model is trained based on the training data set to predict the probability of an item listing being sold through the network-based system. The prediction model is used to rank search results, and the search results can be presented through the network-based system.

Systems and methods for predicting sales of item listings

A system and method for personalized search. The system includes a search engine that receives search input from a user and a user identification and generates a search result based on the search input; and a profiling engine to gather profile data, generate a user profile associated with a user, and rank the search result personalized to the specific user using the user profile.

System and method for personalized search

Methods and systems to suggest information to be used with an item being listed for sale may include analyzing stored search queries and identifying co-occurring terms or terms based on their desirability scores (or desirable terms). The co-occurring terms and the desirable terms may be identified based on their frequency of use in the search queries and/or in the title information or other information associated with items offered for sale.

Techniques for sharing item images based on a similarity score are described. For example, a machine receives an item listing for an item from a user device. The machine generates a similarity score for an existing image corresponding to one or more existing item listings by comparing the item listing received from the user device with the one or more existing item listings. The similarity score may indicate a degree of similarity between the item listing received from the user device and the one or more existing item listings corresponding to the existing image. The machine selects one or more existing images corresponding to the one or more existing item listings based on the one or more existing images having corresponding similarity scores that exceed a threshold value. The machine transmits a communication to the user device in response to the receiving of the item listing from the user device. The communication may include the one or more existing images.

Growing community of inventors

Shanghai, China

Xiaoyuan Wu