METHOD, DEVICE AND EQUIPMENT FOR RECOMMENDING PRODUCT, AND COMPUTER READABLE STORAGE MEDIUM

• US 20200134693A1
• Filed: 02/11/2018
• Published: 04/30/2020
• Est. Priority Date: 06/20/2017
• Status: Abandoned Application

• Current Assignee: Ping An Technology Company Limited (Ping An Insurance (Group) Company of China, Ltd.)
• Original Assignee: Ping An Technology Company Limited (Ping An Insurance (Group) Company of China, Ltd.)
• Inventors: Ding, Jialin
• Application Number:
  US16/305,887
• Publication Number:
  US 20200134693A1
• Time in Patent Office: Days
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/06 Buying, selling or leasing transactions
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

Disclosed is a method, a device and an equipment for recommending product, the method includes the following steps: when a trigger instruction of recommending a product to be recommended is detected, acquiring an operating data of a customer who has already purchased the product to be recommended according to the trigger instruction; calculating a predicted score of purchasing the product again for the customer to purchase the product to be recommended according to the operating data; and if the predicted score is higher than a preset score, recommending the product to be recommended to the customer. The present disclosure realizes that the predicted score of purchasing the product again for the customer to purchase the product to be recommended is calculated according to the operating data, and whether the product to be recommended is recommended to the customer is determined according to the predicted score.

Claims

1. A method for recommending product, comprising the following steps:

when a trigger instruction of recommending a product to be recommended is detected, acquiring an operating data of a customer who having already purchased the product to be recommended according to the trigger instruction;

calculating a predicted score of purchasing the product again for the customer to purchase the product to be recommended according to the operating data; and

recommending the product to be recommended to the customer if the predicted score is higher than a preset score.

2. The method according to claim 1, wherein before the step of calculating a predicted score of purchasing the product again for the customer to purchase the product to be recommended according to the operating data, the method further comprises:

acquiring a focused product of the customer, and determining a similarity between the focused product and the product to be recommended;

the step of calculating a predicted score of purchasing the product again for the customer to purchase the product to be recommended according to the operating data comprises;

calculating the predicted score of purchasing the product again for the customer to purchase the product to be recommended according to the similarity and the operating data.

3. The method according to claim 2, wherein when the focused product and the product to be recommended are both financial products, the step of acquiring a focused product of the customer, and determining a similarity between the focused product and the product to be recommended comprises:

acquiring the focused product of the customer, and acquiring a financial cycle, a risk degree, a product type, and a yield rate of the focused product; and

respectively comparing the financial cycle, the risk degree, the product type, and the yield rate of the focused product with a financial cycle, a risk degree, a product type, and a yield rate of the product to be recommended, to determine the similarity between the focused product and the product to be recommended.

4. The method according to claim 1, wherein before the step of when a trigger instruction of recommending a product to be recommended is detected, acquiring an operating data of a customer who having already purchased the product to be recommended according to the trigger instruction, the method further comprises:

when a login operation of logining into a corresponding application for purchasing the product to be recommended is detected, detecting a clicking operation of the customer applied on the product in the application; and

acquiring the operating data of the customer operating the product in the application according to the clicking operation, and storing the operating data.

5. The method according to claim 4, wherein the operating data comprises a focus frequency of the customer to the product in the application, a payment data of the product in the application, a payment data corresponding to the purchased product, and click times of the product to be recommended.

6. The method according to claim 5, wherein the step of calculating a predicted score of purchasing the product again for the customer to purchase the product to be recommended according to the operating data comprises:

respectively calculating predicted sub scores corresponding to the focus frequency, the payment data, the payment data, and the click times according to corresponding preset rules, based on the focus frequency, the payment data, the payment data and the click times;

determining weights of the focus frequency, the payment data, the payment data, and the click times;

calculating the predicted score of purchasing the product again for the customer to purchase the product to be recommended according to the predicted sub scores and the weights; and

wherein, the weight of the focus frequency is 0.25, the weight of the payment data is 0.2, the weight of the payment data is 0.25, the weight of the click times is 0.3, when the predicted sub score of the focus frequency is recorded as A, the predicted sub score of the payment data is recorded as B, the predicted sub score of the payment data is recorded as C, the predicted sub score of the click times is recorded as D, the predicted score is recorded as S, then the predicted score S=A*0.25+B*0.2+C*0.25+D*0.3.

7. The method according to claim 6, wherein the step of calculating the predicted sub score corresponding to the payment data according to the preset rule corresponding to the payment data, based on the payment data, comprises:

calculating a difference value between total payment times and missed payment times in the payment data; and

calculating the predicted sub score corresponding to the payment data according to the difference value and the total payment times.

8. The method according to claim 1, wherein the step of recommending the product to be recommended to the customer if the predicted score being higher than a preset score comprises:

if the predicted score is higher than the preset score, detecting whether the predicted score is in a discount score range corresponding to a discount program; and

if the predicted score is in the discount score range, recommending to the custom the product to be recommended, and sending the discount program for purchasing the product to be recommended to the customer.

9. The method according to claim 2, wherein the step of recommending the product to be recommended to the customer if the predicted score being higher than a preset score comprises:

if the predicted score is higher than the preset score, detecting whether the predicted score is in a discount score range corresponding to a discount program; and

if the predicted score is in the discount score range, recommending to the custom the product to be recommended, and sending the discount program for purchasing the product to be recommended to the customer.

10. The method according to claim 3, wherein the step of recommending the product to be recommended to the customer if the predicted score being higher than a preset score comprises:

if the predicted score is higher than the preset score, detecting whether the predicted score is in a discount score range corresponding to a discount program; and

if the predicted score is in the discount score range, recommending to the custom the product to be recommended, and sending the discount program for purchasing the product to be recommended to the customer.

11. The method according to claim 4, wherein the step of recommending the product to be recommended to the customer if the predicted score being higher than a preset score comprises:

if the predicted score is higher than the preset score, detecting whether the predicted score is in a discount score range corresponding to a discount program; and

if the predicted score is in the discount score range, recommending to the custom the product to be recommended, and sending the discount program for purchasing the product to be recommended to the customer.

12. The method according to claim 5, wherein the step of recommending the product to be recommended to the customer if the predicted score being higher than a preset score comprises:

if the predicted score is higher than the preset score, detecting whether the predicted score is in a discount score range corresponding to a discount program; and

if the predicted score is in the discount score range, recommending to the custom the product to be recommended, and sending the discount program for purchasing the product to be recommended to the customer.

13. The method according to claim 6, wherein the step of recommending the product to be recommended to the customer if the predicted score being higher than a preset score comprises:

if the predicted score is higher than the preset score, detecting whether the predicted score is in a discount score range corresponding to a discount program; and

if the predicted score is in the discount score range, recommending to the custom the product to be recommended, and sending the discount program for purchasing the product to be recommended to the customer.

14. The method according to claim 7, wherein the step of recommending the product to be recommended to the customer if the predicted score being higher than a preset score comprises:

if the predicted score is higher than the preset score, detecting whether the predicted score is in a discount score range corresponding to a discount program; and

if the predicted score is in the discount score range, recommending to the custom the product to be recommended, and sending the discount program for purchasing the product to be recommended to the customer.

15. A device for recommending product, comprising one or more processors and a non-transitory program storage medium storing program code executable by the one or more processors, the program code comprising:

an acquiring module, configured to, when a trigger instruction of recommending a product to be recommended is detected, acquire an operating data of a customer who having already purchased the product to be recommended according to the trigger instruction;

a calculating module, configured to calculate a predicted score of purchasing the product again for the customer to purchase the product to be recommended according to the operating data; and

a recommending module, configured to, if the predicted score is higher than a preset score, recommending the product to be recommended to the customer.

16. The device according to claim 15, wherein the acquiring module is also configured to acquire a focused product of the customer, and determine a similarity between the focused product and the product to be recommended;

the calculating module is also configured to calculate the predicted score of purchasing the product again for the customer to purchase the product to be recommended according to the similarity and the operating data.

17. The device according to claim 16, wherein when the focused product and the product to be recommended are both financial products, the acquiring module comprises:

an acquiring unit, configured to acquire the focused product of the customer, and acquire a financial cycle, a risk degree, a product type, and a yield rate of the focused product; and

a determining unit, configured to respectively compare the financial cycle, the risk degree, the product type, and the yield rate of the focused product with a financial cycle, a risk degree, a product type, and a yield rate of the product to be recommended, to determine the similarity between the focused product and the product to be recommended.

18. The device according to claim 15, wherein the device further comprises:

a detecting module, configured to, when a login operation of logining into a corresponding application for purchasing the product to be recommended is detected, detect a clicking operation of the customer applied on the product in the application; and

the acquiring module is also configured to acquire the operating data of the customer operating the product in the application according to the clicking operation, and store the operating data.

19. An apparatus for recommending product, comprising a memory,

a processor, and a program for recommending product stored in the memory and operated by the processor, the program for recommending product performing following steps in a method for recommending product when being executed by the processor:

when a trigger instruction of recommending a product to be recommended is detected, acquiring an operating data of a customer who having already purchased the product to be recommended according to the trigger instruction;

calculating a predicted score of purchasing the product again for the customer to purchase the product to be recommended according to the operating data; and

recommending the product to be recommended to the customer if the predicted score is higher than a preset score.

20. (canceled)