Ebay, Incorporated

Sameep Navin Solanki

Manas Haribhai Somaiya

Rajyashree Mukherjee

Jayasimha Katukuri

Rakesh Setty

Sanjay Pundlkrao Ghatare

Muhammad Faisal Rehman

Selcuk Kopru

Vipul C Dalal

Chandra Prakash Khatri

Justin Nicholas House

Nish Parikh

Michael Mathieson

The present disclosure is directed to apparatuses, systems, and methods for enhancing search results based on recent user interactions. As described herein, embodiments may infer various refinements for search queries; these refinements are based, at least in part, on the user's recent interactions with the search platform (e.g., within a current session). In other words, as the user is interacting with the search platform, one or more refinements may be inferred to help make the search results more relevant to the user.

Dynamic modifications of results for search interfaces

In various example embodiments, a system and method for a Target Language Engine are presented. The Target Language Engine augments a synonym list in a base dictionary of a target language with one or more historical search queries previously submitted to search one or more listings in listing data. The Target Language Engine identifies a compound word and a plurality of words present in the listing data that have a common meaning in the target language. Each word from the plurality of words is present in the compound word. The Target Language Engine causes a database to create an associative link between the portion of text and a word selected from at least one of the synonym list or the plurality of words.

Snippet generation and item description summarizer

Described herein are methods and systems for promoting item listings that satisfy a query based on the item listings being assigned to certain categories that have, based on historical click data, exhibited high demand characteristics for the query. Consistent with some embodiments, a certain number of leaf-level categories are identified based on demand data for those categories, and the item listings assigned to those categories are promoted through a weighting factor derived in part based on the click probability score associated with the category. In some embodiments, certain sub-categories may be selected when the demand associated with the child categories of the sub-category is well balanced.

Method and system for ranking search results based on categories

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San Jose, CA, United States of America

Sameep Navin Solanki