PREDICTING A STATUS OF A TRANSACTION

• US 20160217513A1
• Filed: 05/29/2015
• Published: 07/28/2016
• Est. Priority Date: 01/23/2015
• Status: Active Grant

• Current Assignee: eBay Inc.
• Original Assignee: eBay Inc.
• Inventors: Louis, Jerry, Moghaddam, Samaneh Abbasi, Dalal, Vipul C.
• Granted Patent: US 10,217,148 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/0609 Buyer or seller confidence or verification

Abstract

Methods, systems, and apparatus for predicting a status of a transaction are described. Feature data related to one or more transactions is collected and a subset of features is selected for use in predicting the status of the transaction. A model is trained using the collected feature data that corresponds to the selected features, and the model is applied to feature data of a selected transaction to generate a probability of the selected transaction attaining one or more defined statuses. Mitigating or preventive actions are performed based on the generated probability.

Claims

1. A system for predicting a status of a transaction, the system comprising:

a data collection module implemented using one or more hardware processors, the data collection module configured to collect feature data related to one or more transactions;

a model generation module implemented using one or more hardware processors, the model generation module configured to select a subset of features for use in predicting the status of the transaction and train a model using the collected feature data that corresponds to the selected subset of features;

a prediction processing module implemented using one or more hardware processors, the prediction processing module configured to apply the model to feature data of a selected transaction to generate a probability of the selected transaction attaining one or more defined statuses; and

a transaction mitigation module implemented using one or more hardware processors, the transaction mitigation module configured to perform one or more mitigating or preventive actions based on the generated probability.

2. The system of claim 1, wherein the data collection module is further configured to normalize the collected feature data.

3. The system of claim 1, wherein the feature data is collected for one or more of a buyer, a seller, an item, and a transaction.

4. The system of claim 1, wherein the collected feature data comprises one or more of user segment data, user historical data, user behavioral data, an item price, an item title, an item category, a transaction site identifier, a buyer tool, a shipping fee, and a shipping time.

5. The system of claim 1, wherein the model is a Logistic Regression model.

6. The system of claim 5, wherein the subset of features is selected using a Forward Feature Selection mechanism of the Logistic Regression model.

7. The system of claim 1, wherein the model is a Random Forest model.

8. The system of claim 7, wherein the subset of features is selected using a Recursive Feature Elimination mechanism of the Random Forest model.

9. The system of claim 1, wherein the model is trained for each of a plurality of leaf-categories of items.

10. The system of claim 1, wherein the one or more mitigating or preventive actions are one or more of issuing a message to a buyer, issuing a message to a seller, blocking the buyer from executing the transaction, blocking the seller from executing the transaction, and holding funds of the seller.

11. A method for predicting a status of a transaction, the method comprising:

collecting feature data related to one or more transactions;

selecting a subset of features for use in predicting the status of the transaction;

training a model using the collected feature data that corresponds to the selected subset of features;

applying the model to feature data of a selected transaction to generate a probability of the selected transaction attaining one or more defined statuses; and

performing one or more mitigating or preventive actions based on the generated probability.

12. The method of claim 11, further comprising normalizing the collected feature data.

13. The method of claim 11, wherein the feature data is collected for one or more of a buyer, a seller, an item, and a transaction.

14. The method of claim 11, wherein the collected feature data comprises one or more of user segment data, user historical data, user behavioral data, an item price, an item title, an item category, a transaction site identifier, a buyer tool, a shipping fee, and a shipping time.

15. The method of claim 11, wherein the model is a Logistic Regression model.

16. The method of claim 15, wherein the subset of features is selected using a Forward Feature Selection mechanism of the Logistic Regression model.

17. The method of claim 11, wherein the model is a Random Forest model.

18. The method of claim 17, wherein the subset of features is selected using a Recursive Feature Elimination mechanism of the Random Forest model.

19. The method of claim 11, wherein the model is trained for each of a plurality of leaf-categories of items.

20. A non-transitory computer-readable medium embodying instructions that, when executed by a processor, perform operations comprising:

collecting feature data related to one or more transactions;

selecting a subset of features for use in predicting a status of at least one of the transactions;

training a model using the collected feature data that corresponds to the selected subset of features;

applying the model to feature data of a selected transaction to generate a probability of the selected transaction attaining one or more defined statuses; and

performing one or more mitigating or preventive actions based on the generated probability.