METHOD AND SYSTEM FOR IDENTIFYING BAD COMMODITIES BASED ON USER PURCHASE BEHAVIORS

• US 20150379532A1
• Filed: 06/10/2015
• Published: 12/31/2015
• Est. Priority Date: 12/11/2012
• Status: Abandoned Application

• Current Assignee: Beijing Jingdong Century Trading Co., Ltd.
• Original Assignee: Beijing Jingdong Century Trading Co., Ltd.
• Inventors: LIU, Sizhe, HE, Zhi
• Application Number:
  US14/736,073
• Publication Number:
  US 20150379532A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/06 Buying, selling or leasing transactions

Abstract

A method and system for identifying bad commodities based on user purchase behaviors is disclosed. In one aspect, the method includes selecting, by a user screening module a set of users who only perform a single shopping behavior within a specific time period and constructing, by the user screening module, a user-commodity purchase relationship matrix based on the set of users and specifications of the commodities purchased by all the customers. The method also includes calculating, by an identifying module and based on the user-commodity purchase relationship matrix, a probability that a commodity is bad to identify bad commodities, generating, by the identifying module, a list of bad commodities based on the identified bad commodities, and providing, by a pushing module, the generated list of bad commodities to a commodity intervention system.

Claims

1. A method for identifying bad commodities based on user purchase behaviors, the method comprising:

selecting, by a user screening module, from a set of all customers received from a customer transaction system, a set of users who only perform a single shopping behavior within a specific time period;

constructing, by the user screening module, a user-commodity purchase relationship matrix based on the specifications of the commodities purchased by the set of all customers and the set of users;

calculating, by an identifying module, a probability for each of the commodities that a corresponding commodity is bad based on the user commodity purchase relationship matrix;

generating, by the identifying module, a list of bad commodities based on the identified bad commodities; and

pushing, by a pushing module, the generated list of bad commodities to a commodity intervention system.

2. The method according to claim 1, wherein the set of users comprises a first subset of users, which is a subset of users who only perform a single shopping behavior within the specific time period and who do not perform a shopping behavior within a previous specific time period before the specific time period.

3. The method according to claim 2, wherein before the user-commodity purchase relationship matrix is constructed, a behavior marking module marks whether the first subset of users perform a specific behavior within a future specific time period after the specific time period based on the first subset of users to generate behavior data of the corresponding users.

4. The method according to claim 2, wherein the first subset of users comprises a second subset of users, which is a subset of users who only perform a single shopping behavior within the specific time period, who do not perform a shopping behavior within a previous specific time period before the specific time period, and who do not perform a specific behavior within a future specific time period after the specific time period.

5. The method according to claim 2, wherein the user-commodity purchase relationship matrix is constructed based on the first subset of users and the specifications of the commodities purchased by all the customers.

6. The method according to claim 4, wherein the user-commodity purchase relationship matrix is constructed based on the second subset of users and the specifications of the commodities purchased by all the customers.

7. The method according to claim 3, wherein the probability that a commodity is bad is further calculated based on the behavior data.

8. The method according to claim 3, wherein the specific behavior is one of a shopping behavior, a login behavior or a marking-as-favorite behavior.

9. The method according to claim 1, wherein the identifying module adopts an algorithm adapted to a sparse matrix environment to resolve the probability that a commodity is bad.

10. The method according to claim 1, wherein the identifying module adopts a methodology of a binomial distribution hypothesis inspection to identify the bad commodities.

11. A system for identifying bad commodities based on user purchase behaviors, the system comprising:

a user screening module configured to;

i) select, from a set of all customers received from a customer transaction system, a set of users who only perform a single shopping behavior within a specific time period, and ii) construct a user-commodity purchase relationship matrix based on specifications of the commodities purchased by the set of all customers and the set of users;

an identifying module configured to;

i) calculate a probability for each of the commodities that a corresponding commodity is bad based on the user-commodity purchase relationship matrix, and ii) generate a list of bad commodities based on the identified bad commodities; and

a pushing module configured to push the generated list of bad commodities to a commodity intervention system.

12. The system according to claim 11, wherein the set of users comprises a first subset of users, which is a subset of users who only perform a single shopping behavior within the specific time period and who do not perform a shopping behavior within a previous specific time period before the specific time period.

13. The system according to claim 12, further comprising a marking module configured to mark, before the user screening module constructs the user-commodity purchase relationship matrix, whether the first subset of users perform a specific behavior within a future specific time period after the specific time period based on the first subset of users to generate behavior data of the corresponding users.

14. The system according to claim 12, wherein the first subset of users comprises a second subset of users, which is a subset of users who only perform a single shopping behavior within the specific time period, who do not perform a shopping behavior within a previous specific time period before the specific time period, and who do not perform a specific behavior within a future specific time period after the specific time period.

15. The system according to claim 12, wherein the user-commodity purchase relationship matrix is constructed based on the first subset of users and the specifications of the commodities purchased by all the customers.

16. The system according to claim 14, wherein the user-commodity purchase relationship matrix is constructed based on the second subset of users and the specifications of the commodities purchased by all the customers.

17. The system according to claim 13, wherein the probability that a commodity is bad is further calculated based on the behavior data.

18. The system according to claim 13, wherein the specific behavior is one of a shopping behavior, a login behavior or a marking-as-favorite behavior.

19. The system according to claim 11, wherein the identifying module adopts an algorithm adapted to a sparse matrix environment to resolve the probability that a commodity is bad.

20. The system according to claim 11, wherein the identifying module adopts a methodology of a binomial distribution hypothesis inspection to identify the bad commodities.