Matching products with service scenarios

1. A computer-implemented method, comprising:

acquiring, by data processing apparatus of a distributed computing system of a website having a plurality of service scenarios, and for each service scenario of the plurality of service scenarios of the website, respective representative features of a plurality of users who have accessed the website through each service scenario, wherein each service scenario is a different respective access interface for accessing a different respective service of the website;

obtaining, by the data processing apparatus of the distributed computing system, features of a to-be-matched product to be presented to users accessing the website through one of the service scenarios;

calculating, by the data processing apparatus of the distributed computing system, a respective user feature mapping value of each service scenario of the plurality of the service scenarios of the website, wherein the respective user feature mapping value of each service scenario is based on matching features of the to-be-matched product corresponding to the service scenario to respective representative features of the plurality of the users who have accessed the website through the service scenario, comprising:

quantifying a first user feature corresponding to a first matching feature of the to-be-matched product corresponding to each service scenario according to a quantification rule, wherein the quantification rule is the same for the plurality of the service scenarios; and

calculating the respective user feature mapping value of each service scenario based on the first user feature corresponding to the first matching feature according to a mapping rule, wherein the mapping rule is the same for the plurality of the service scenarios;

selecting, by the data processing apparatus of the distributed computing system, among the plurality of service scenarios of the website, a target service scenario for the to-be-matched product based on the respective user feature mapping value of the target service scenario;

receiving, by the data processing apparatus of the distributed computing system of the website, a request for service information for the target service scenario of the website; and

providing, by the data processing apparatus of the distributed computing system for display to a user, service scenario information and the to-be-matched product selected for the target service scenario of the website.

2. The method of claim 1 , wherein the respective representative features of the plurality of the users who have accessed the web site through each service scenario comprises features derived from registration information or an access record of the plurality of the users who have accessed the web site through each service scenario.

3. The method of claim 1 , wherein selecting, among the plurality of the service scenarios of the website, the target service scenario for the to-be-matched product based on the respective user feature mapping value of the target service scenario comprises:

selecting a service scenario having a lowest user feature mapping value among a plurality of respective user feature mapping values of the plurality of the service scenarios as the target service scenario for the to-be-matched product; or

selecting a service scenario having a highest user feature mapping value among a plurality of respective user feature mapping values of the plurality of the service scenarios as the target service scenario for the to-be-matched product.

4. The method of claim 1 , wherein obtaining features of the to-be-matched product to be presented to the users accessing the website through one of the service scenarios comprises acquiring matching features of the to-be-matched product corresponding to one of the service scenarios based on the respective representative features of the plurality of the users who have accessed the web site through the service scenarios according to a machine learning algorithm.

5. The method of claim 4 , wherein the machine learning algorithm comprises at least one or more of a logistic regression algorithm, a Gradient Boosting Decision Tree (GBDT) algorithm, a decision tree algorithm, or a deep learning algorithm.

6. The method of claim 1 , wherein calculating the respective user feature mapping value of each service scenario based on the matching features of the to-be-matched product corresponding to each service scenario comprises:

obtaining a mapping function between the respective user feature mapping value of each service scenario and the matching features of the to-be-matched product corresponding to each service scenario using a machine learning method; and

calculating the respective user feature mapping value of each service scenario based on the mapping function.

7. A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising:

acquiring, by data processing apparatus of a distributed computing system of a website having a plurality of service scenarios, and for each service scenario of the plurality of service scenarios of the website, respective representative features of a plurality of users who have accessed the website through each service scenario, wherein each service scenario is a different respective access interface for accessing a different respective service of the website;

obtaining, by the data processing apparatus of the distributed computing system, features of a to-be-matched product to be presented to users accessing the website through one of the service scenarios;

calculating, by the data processing apparatus of the distributed computing system, a respective user feature mapping value of each service scenario of the plurality of the service scenarios of the website, wherein the respective user feature mapping value of each service scenario is based on matching features of the to-be-matched product corresponding to the service scenario to respective representative features of the plurality of the users who have accessed the website through the service scenario, comprising:

quantifying a first user feature corresponding to a first matching feature of the to-be-matched product corresponding to each service scenario according to a quantification rule, wherein the quantification rule is the same for the plurality of the service scenarios; and

calculating the respective user feature mapping value of each service scenario based on the first user feature corresponding to the first matching feature according to a mapping rule, wherein the mapping rule is the same for the plurality of the service scenarios;

selecting, by the data processing apparatus of the distributed computing system, among the plurality of service scenarios of the website, a target service scenario for the to-be-matched product based on the respective user feature mapping value of the target service scenario;

receiving, by the data processing apparatus of the distributed computing system of the website, a request for service information for the target service scenario of the website; and

providing, by the data processing apparatus of the distributed computing system for display to a user, service scenario information and the to-be-matched product selected for the target service scenario of the website.

8. The computer-readable medium of claim 7 , wherein the respective representative features of the plurality of the users who have accessed the website through each service scenario comprises features derived from registration information or an access record of the plurality of the users who have accessed the website through each service scenario.

9. The computer-readable medium of claim 7 , wherein selecting, among the plurality of the service scenarios of the website, the target service scenario for the to-be-matched product based on the respective user feature mapping value of the target service scenario comprises:

selecting a service scenario having a lowest user feature mapping value among a plurality of respective user feature mapping values of the plurality of the service scenarios as the target service scenario for the to-be-matched product; or

selecting a service scenario having a highest user feature mapping value among a plurality of respective user feature mapping values of the plurality of the service scenarios as the target service scenario for the to-be-matched product.

10. The computer-readable medium of claim 7 , wherein obtaining features of the to-be-matched product to be presented to the users accessing the website through one of the service scenarios comprises acquiring matching features of the to-be-matched product corresponding to one of the service scenarios based on the respective representative features of the plurality of the users who have accessed the website through the service scenarios according to a machine learning algorithm.

11. The computer-readable medium of claim 7 , wherein calculating the respective user feature mapping value of each service scenario based on the matching features of the to-be-matched product corresponding to each service scenario comprises:

obtaining a mapping function between the respective user feature mapping value of each service scenario and the matching features of the to-be-matched product corresponding to each service scenario using a machine learning method; and

calculating the respective user feature mapping value of each service scenario based on the mapping function.

12. A computer-implemented system, comprising:

one or more computers; and

one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing one or more instructions that, when executed by the one or more computers, perform one or more operations comprising:

acquiring, by data processing apparatus of a distributed computing system of a website having a plurality of service scenarios, and for each service scenario of the plurality of service scenarios of the website, respective representative features of a plurality of users who have accessed the website through each service scenario, wherein each service scenario is a different respective access interface for accessing a different respective service of the website;

obtaining, by the data processing apparatus of the distributed computing system, features of a to-be-matched product to be presented to users accessing the website through one of the service scenarios;

calculating, by the data processing apparatus of the distributed computing system, a respective user feature mapping value of each service scenario of the plurality of the service scenarios of the website, wherein the respective user feature mapping value of each service scenario is based on matching features of the to-be-matched product corresponding to the service scenario to respective representative features of the plurality of the users who have accessed the website through the service scenario, comprising:

quantifying a first user feature corresponding to a first matching feature of the to-be-matched product corresponding to each service scenario according to a quantification rule, wherein the quantification rule is the same for the plurality of the service scenarios; and

calculating the respective user feature mapping value of each service scenario based on the first user feature corresponding to the first matching feature according to a mapping rule, wherein the mapping rule is the same for the plurality of the service scenarios;

selecting, by the data processing apparatus of the distributed computing system, among the plurality of service scenarios of the website, a target service scenario for the to-be-matched product based on the respective user feature mapping value of the target service scenario;

receiving, by the data processing apparatus of the distributed computing system of the website, a request for service information for the target service scenario of the website; and

providing, by the data processing apparatus of the distributed computing system for display to a user, service scenario information and the to-be-matched product selected for the target service scenario of the website.

13. The computer-implemented system of claim 12 , wherein the respective representative features of the plurality of the users who have accessed the web site through each service scenario comprises features derived from registration information or an access record of the plurality of the users who have accessed the website through each service scenario.

14. The computer-implemented system of claim 12 , wherein selecting, among the plurality of the service scenarios of the website, the target service scenario for the to-be-matched product based on the respective user feature mapping value of the target service scenario comprises:

selecting a service scenario having a lowest user feature mapping value among a plurality of respective user feature mapping values of the plurality of the service scenarios as the target service scenario for the to-be-matched product; or

selecting a service scenario having a highest user feature mapping value among a plurality of respective user feature mapping values of the plurality of the service scenarios as the target service scenario for the to-be-matched product.

15. The computer-readable medium of claim 10 , wherein the machine learning algorithm comprises at least one or more of a logistic regression algorithm, a Gradient Boosting Decision Tree (GBDT) algorithm, a decision tree algorithm, or a deep learning algorithm.

16. The computer-implemented system of claim 12 , wherein obtaining features of the to-be-matched product to be presented to the users accessing the website through one of the service scenarios comprises acquiring matching features of the to-be-matched product corresponding to one of the service scenarios based on the respective representative features of the plurality of the users who have accessed the website through the service scenarios according to a machine learning algorithm.

17. The computer-implemented system of claim 16 , wherein the machine learning algorithm comprises at least one or more of a logistic regression algorithm, a Gradient Boosting Decision Tree (GBDT) algorithm, a decision tree algorithm, or a deep learning algorithm.

18. The computer-implemented system of claim 12 , wherein calculating the respective user feature mapping value of each service scenario based on the matching features of the to-be-matched product corresponding to each service scenario comprises:

obtaining a mapping function between the respective user feature mapping value of each service scenario and the matching features of the to-be-matched product corresponding to each service scenario using a machine learning method; and

calculating the respective user feature mapping value of each service scenario based on the mapping function.