Optimizing similar item recommendations in a semi-structured environment

Patent metadata

Field	Value
Publication number	US-2017293695-A1
Application number	US-201615190279-A
Country	US
Kind code	A1
Filing date	Jun 23, 2016
Priority date	Apr 12, 2016
Publication date	Oct 12, 2017
Grant date	—

Abstract

Systems, methods and media are provided for optimizing similar item recommendations in a semi-structured environment. In one embodiment a system includes at least one processor and a memory storing instructions that, when executed by the at least one processor, cause the system to perform operations comprising, at least identifying a seed item; retrieving a subset of recommended items relevant to the seed item; and ranking the subset of recommended items based on an item conversion probability, wherein the ranking of the subset of recommended items is based on a machine learning technique, and wherein a binary or multi-class label is used as training input to the machine learning technique.

First claim

What is claimed is:

1 . A system for optimizing similar item recommendations in a semi-structured environment, the system including:
at least one processor; a memory storing instructions that, when executed by the at least one processor, cause the system to perform operations comprising, at least:
identifying a seed item;
retrieving a subset of recommended items relevant to the seed item; and
ranking the subset of recommended items based on an item conversion probability, wherein the ranking of the subset of recommended items is based on a machine learning technique, and wherein a binary or multi-class label is used as training input to the machine learning technique.

2 . The system of claim 1 , wherein the training input to the machine learning technique includes a binary label, and wherein the binary label includes item non-clicks and item purchases as the binary class labels, respectively.

3 . The system of claim 1 , wherein the operations further comprise:
conducting an offline indexing phase, the offline indexing phase including an analysis of behavioral data and click logs; and training a binary classifier based on an aspect of the behavioral data to determine the item conversion probability.

4 . The system of claim 1 , wherein retrieving a subset of recommended items includes sharding a search result including the subset of recommended items by country and category classifiers.

5 . The system of claim 1 , wherein the operations further comprise determining a divergence overlap score for the binary or multi-class label.

6 . The system of claim 1 , wherein a determination of the item conversion probability is based on a metric including

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where r i and l i is a rank of a recommended item and n is a maximum rank.

7 . A method of optimizing similar item recommendations in a semi-structured environment, the method including:
identifying a seed item; retrieving a subset of recommended items relevant to the seed item; and ranking the subset of recommended items based on an item conversion probability, wherein the ranking of the subset of recommended items is based on a machine learning technique, and wherein a binary or multi-class label is used as training input to the machine learning technique.

8 . The method of claim 7 , wherein the training input to the machine learning technique includes a binary label, and wherein the binary label includes item non-clicks and item purchases as the binary class labels, respectively.

9 . The method of claim 7 , wherein the method further comprises:
conducting an offline indexing phase, the offline indexing phase including an analysis of behavioral data and click logs; and training a binary classifier based on an aspect of the behavioral data to determine the item conversion probability.

10 . The method of claim 7 , wherein retrieving a subset of recommended items includes sharding a search result including the subset of recommended items by country and category classifiers.

11 . The method of claim 7 , wherein the method further comprises determining a divergence overlap score for the binary or multi-class label.

12 . The method of claim 7 , wherein a determination of the item conversion probability is based on a metric including

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where r i and l i is a rank of a recommended item and n is a maximum rank.

13 . A non-transitory machine-readable storage medium storing a set of instructions that, when executed by at least one processor, causes the at least one processor to perform operations including, at least:
identifying a seed item; and retrieving a subset of recommended items relevant to the seed item; and ranking the subset of recommended items based on an item conversion probability, wherein the ranking of the subset of recommended items is based on a machine learning technique, and wherein a binary or multi-class label is used as training input to the machine learning technique.

14 . The medium of claim 13 , wherein the training input to the machine learning technique includes a binary label, and wherein the binary label includes item non-clicks and

Assignees

• Ebay Inc

Inventors

• Brovman Yuri Michael
• Jacob Marie
• Srinivasan Natraj
• Neola Stephen
• Galron Daniel
• Snyder Ryan
• Wang Paul

Classifications

• G06Q30/0251

  Targeted advertisements · CPC title

• G06Q30/0631 Primary

  Recommending goods or services · CPC title

• G06F17/30938

  Physics · mapped topic

• G06F17/30911

  Physics · mapped topic

• G06F17/30941 Primary

  Physics · mapped topic