Ebay, Incorporated

Parashuram Kulkarni

Michael Mathieson

Jon Degenhardt

Abdelhalim Abbas

Jingzhou Hua

Ramakrishnan Natarajan

Ted Tao Yuan

Various embodiments include systems and methods for search result ranking using machine learning. A goal model can be created using machine learning. Responsive to a search query, a plurality of data factors can be inputted into the goal model to create a model output. Search results can be presented to a user based on the model output.

Search result ranking using machine learning

A system and method for assessing excessive accessory listings in search results includes a processor-implemented textual mining module that parses a data field of a document and generates at least one token from the data field. A processor-implemented scoring module calculates a score for the at least one token, with the at least one token score representing a likelihood that the at least one token belongs to one of two binary classifications. The processor-implemented scoring module also calculates a score for the document based on the at least one token score, with the document score representing a probability of the document being in one of the two binary classifications. A processor-implemented decision tree module inputs the document score and document attribute values into a decision tree and generates an output representing a refined score based on the document score and at least one of the document attribute values.

Methods and systems for assessing excessive accessory listings in search results

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Santa Clara, CA, United States of America

Parashuram Kulkarni