CN111222919B - Service data pushing method, device, computer equipment and storage medium - Google Patents
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract
The present application relates to the field of computer technologies, and in particular, to a method and apparatus for pushing service data, a computer device, and a storage medium. The method comprises the following steps: receiving current behavior data acquired by a user terminal, wherein the current behavior data carries a user identifier; acquiring behavior characteristic data corresponding to the user identifier, wherein the behavior characteristic data is generated based on historical behavior data; determining a corresponding service data label according to the behavior characteristic data, and acquiring corresponding service data based on the service data label; and pushing the acquired service data to the user terminal. By adopting the method, the accuracy of pushing the service data can be improved.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and apparatus for pushing service data, a computer device, and a storage medium.
Background
Along with popularization of the sharing travel mode, the number of sharing vehicles (such as sharing bicycles, sharing mopeds and the like) is increased, and great convenience is brought to short-distance travel of users. During the sharing travel, the user typically makes a payment for the riding fee by purchasing a riding card.
In the prior art, when the service data of the riding card is pushed by the sharing platform, all the service data are pushed to all the user terminals, so that the effective pushing of the service data is less, and the accuracy of the data pushing is lower.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a service data pushing method, device, computer apparatus, and storage medium capable of improving service data pushing accuracy.
A method of pushing business data, the method comprising:
receiving current behavior data acquired by a user terminal, wherein the current behavior data carries a user identifier;
acquiring behavior characteristic data corresponding to the user identifier, wherein the behavior characteristic data is generated based on historical behavior data;
determining a corresponding service data label according to the behavior characteristic data, and acquiring corresponding service data based on the service data label;
and pushing the acquired service data to the user terminal.
In one embodiment, the method further comprises:
extracting multi-level characteristic data from the current behavior data to obtain characteristic data of a plurality of levels;
according to the feature data of each level, the behavior feature data of the same level of the user is updated to obtain updated behavior feature data;
according to the behavior characteristic data, determining a corresponding service data tag, and acquiring corresponding service data based on the service data tag, including:
and determining a corresponding service data label according to the updated behavior characteristic data, and acquiring corresponding service data based on the service data label.
In one embodiment, the method further comprises:
and determining the preferential value index of the service data and sending the preferential value index to the user terminal.
In one embodiment, determining the preference value index of the business data includes:
determining each value index of a plurality of business sub-data constituting the business data;
determining preferential grade indexes of the business sub-data;
based on the preferential grade index and the value index of each business sub-data, preferential value index of each business sub-data is obtained;
and obtaining the preferential value index of the service data based on the preferential value index of each service sub-data composing the service data.
In one embodiment, determining the preference value index of the business data includes:
acquiring a value index of service data;
determining a user preferential grade index corresponding to the user based on the behavior characteristic data of the user;
and obtaining the preferential value index of the service data based on the preferential grade index of the user and the value index of the service data.
A push device for service data, the device comprising:
the current behavior data receiving module is used for receiving current behavior data acquired by the user terminal, wherein the current behavior data carries a user identifier;
the behavior characteristic data acquisition module is used for acquiring behavior characteristic data corresponding to the user identifier, and the behavior characteristic data is generated based on historical behavior data;
the service data acquisition module is used for determining a corresponding service data label according to the behavior characteristic data and acquiring corresponding service data based on the service data label;
and the pushing module is used for pushing the acquired service data to the user terminal.
In one embodiment, the apparatus further includes:
the characteristic data extraction module is used for extracting the characteristic data of the current behavior data in multiple layers to obtain the characteristic data of multiple layers;
the updating module is used for updating the behavior feature data of the same level of the user according to the feature data of each level to obtain updated behavior feature data;
the service data acquisition module is used for determining a corresponding service data tag according to the updated behavior characteristic data and acquiring corresponding service data based on the service data tag.
In one embodiment, the apparatus further includes:
and the preferential value index determining module is used for determining the preferential value index of the service data and transmitting the preferential value index to the user terminal.
A computer device comprising a memory storing a computer program and a processor implementing the steps of any one of the methods described above when the processor executes the computer program.
A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of any of the preceding claims.
According to the pushing method, the device, the computer equipment and the storage medium of the service data, the current behavior data collected by the user terminal is received, the current behavior data carries the user identifier, then the behavior characteristic data corresponding to the user identifier is obtained, the behavior characteristic data is generated based on the historical behavior data, the corresponding service data label is further determined according to the behavior characteristic data, and the service data obtained based on the service data label is pushed to the user terminal. Therefore, the service data pushed to the user terminal is determined based on the behavior characteristic data of the user, and the behavior characteristic data is generated based on the historical behavior data, so that the accuracy of service data pushing can be improved.
Drawings
FIG. 1 is an application scenario diagram of a method for pushing service data in one embodiment;
FIG. 2 is a flow chart of a method for pushing service data in one embodiment;
FIG. 3 is a flow chart illustrating a feature data update step in one embodiment;
fig. 4 is a flow chart of a method for pushing service data in another embodiment;
FIG. 5 is a block diagram of a business data pushing device in one embodiment;
fig. 6 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The pushing method of the service data provided by the application can be applied to an application environment shown in figure 1. Wherein the terminal 102 communicates with the server 104 via a network. The terminal 102 collects current behavior data of the user, where the current behavior data carries a user identifier, and sends the current behavior data to the server 104. After receiving the current behavior data collected by the terminal 102, the server 104 obtains behavior feature data corresponding to the user based on the user identifier carried by the current behavior data. Further, the server 104 determines a corresponding service data tag according to the behavior feature data, obtains corresponding service data based on the service data tag, obtains service data corresponding to the matched service data tag, and then pushes the obtained service data to the terminal 102. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smartphones, tablet computers, portable wearable devices, etc., and the server 104 may be implemented by a stand-alone server or a server cluster formed by a plurality of servers.
In one embodiment, as shown in fig. 2, a method for pushing service data is provided, and the method is applied to the server in fig. 1 for illustration, and includes the following steps:
step S202, current behavior data collected by a user terminal is received, wherein the current behavior data carries a user identifier.
The service data refers to data to be pushed to a user, and may be data in various service scenarios, for example, in the case of sharing riding service, the service data may refer to riding card sleeve meal combination data of a sharing bicycle or a sharing booster bicycle, and in the case of insurance industry, the service data may refer to various different types of insurance package data.
The current behavior data refers to data generated by various operation behaviors of the user on the user terminal, and may include, but is not limited to, browsing data for various websites, recharging data, purchase request data, user riding data, and the like.
Specifically, browsing data of a website refers to data generated by a user browsing on a website corresponding to service data through a user terminal, such as browsing corresponding service through a shared riding Application (APP), or browsing corresponding website through a browser, etc.; the recharging data refers to data generated by the actions such as recharging the account by a user, such as recharging the riding amount in the account by the user or recharging the riding number of times by the user; the purchase request data may refer to a user requesting purchase of a riding ferrule meal or the like by sharing the riding APP; the user riding data is data generated in the process of unlocking to locking and stopping riding through sharing the software such as the riding APP.
The user identifier refers to an identifier corresponding to the user one by one, and may be a user ID, or other identifier that may indicate the uniqueness of the user.
In this embodiment, the user terminal may collect the behavior operation of the user to obtain corresponding current behavior data, and send the collected current behavior data to the server. And the server extracts the corresponding user identification from the current behavior data according to the received current behavior data.
Step S204, behavior feature data corresponding to the user identification is obtained, and the behavior feature data is generated based on historical behavior data.
The historical behavior data may include user liveness data and user card holding data. Specifically, the user activity data includes, but is not limited to, login behavior data of the user login sharing riding APP or applet, for example, login frequency, login time, stay time, login place, etc., and travel behavior data, for example, travel tool is 2 or 4 rounds, travel frequency, travel time, etc.; the user card holding data includes, but is not limited to, card holding types, such as card remaining valid time period, remaining number of times, etc., as well as historical card purchasing time, card purchasing frequency, card purchasing type, etc.
The behavior feature data is generated based on historical behavior data of the user and is used for indicating behavior features of the user and distinguishing behaviors of different users. The behavioral profile data may include, but is not limited to, total time of cycle riding by the user, total number of times of riding, frequency of user purchase of riding cards, distribution of card surface amounts of user purchased riding cards, frequency of user recharge, distribution of amounts charged each time, and the like.
In this embodiment, the server may extract feature data from the historical behavior data of the user, and use the extracted feature data as the behavior feature data of the user.
Optionally, the server may further analyze the behavior feature data of the user to determine that the users with substantially identical behavior feature data are the same type of user, for example, the users with recharging frequency in a certain interval range may be determined to be the same type of user, and assign corresponding user type labels to the users, for example, referring to fig. 3, may be "user type 1", "user type 2", and "user type 3".
Step S206, corresponding business data labels are determined according to the behavior characteristic data, and corresponding business data are obtained based on the business data labels.
The service data tag is a tag for indicating the uniqueness of service data, for example, with continued reference to fig. 3, may refer to a tag of service data corresponding to different package combinations, such as "package combination 1", "package combination 2", and "package combination 3", where the service data tag corresponds to the service data one by one.
As previously described, the behavioral profile data may include, but is not limited to, a total period of time the user is riding around, a total number of times the user is riding, a frequency with which the user purchases a card to ride, a distribution of card amounts for the user to purchase a card to ride, a frequency with which the user is recharging, a distribution of amounts for each recharge, and the like. In this embodiment, the server may analyze and process the behavior feature data of the user to determine a proper combination of the riding and cutting ferrule meals, for example, the user a uses the shared moped multiple times per day, then determines that the combination of the riding and cutting ferrule meals calculated according to the number of days of use is more suitable for the user a, the user B occasionally rides, then determines that the combination of the riding and cutting ferrule meals calculated according to the number of times of use is longer in the use period and is more suitable for the user B, or the user C rides for a longer time each time, then determines that the combination of the riding and cutting ferrule meals calculated according to the number of times is more suitable for the user C.
Further, after analyzing the behavior characteristic data of the user, the server determines a service data tag corresponding to the user, and further, the server obtains corresponding service data, such as a specific riding card meal combination, from the database according to the service data tag.
Optionally, with continued reference to fig. 3, if the server allocates a corresponding user type tag to the user, the server may directly determine the service data tag of the corresponding user according to the one-to-one correspondence between the user type tag and the service data tag, and obtain the corresponding service data according to the service data tag.
Step S208, the acquired service data is pushed to the user terminal.
Specifically, after acquiring the corresponding service data, the server sends the service data to the user terminal corresponding to the user identifier so as to display the service data to the user through the user terminal.
Optionally, after analyzing the behavior feature data of the user, the server may also determine a plurality of service data labels corresponding to the user, sort the plurality of service data labels based on correlation between the behavior feature data of the user and service data corresponding to the service data labels, and push the plurality of service data to the user terminal according to the sorting, so as to be selected by the user.
In the service data pushing method, the current behavior data collected by the user terminal is received, the current behavior data carries the user identifier, then behavior characteristic data corresponding to the user identifier is obtained, the behavior characteristic data is generated based on historical behavior data, a corresponding service data tag is further determined according to the behavior characteristic data, and service data obtained based on the service data tag is pushed to the user terminal. Therefore, the service data pushed to the user terminal is determined based on the behavior characteristic data of the user, and the behavior characteristic data is generated based on the historical behavior data, so that the accuracy of service data pushing can be improved.
In one embodiment, referring to fig. 4, the method may further include:
step S402, extracting multi-level feature data from the current behavior data to obtain feature data of multiple levels.
As described above, the server may extract feature data from the historical behavior data of the user, and use the extracted feature data as behavior feature data of the user, that is, the feature data is the same type of data as the behavior feature data.
Specifically, the current behavior data may include a plurality of different data, such as browsing data of various websites, recharging data, purchase request data, user riding data, and the like, and the server may extract feature data of a plurality of layers by extracting feature data of the current behavior data, for example, recharging amount, riding time, riding duration, browsing records of riding packages, and the like, respectively, so as to obtain feature data of a plurality of different layers.
Step S404, according to the feature data of each level, the behavior feature data of the same level of the user is updated, and updated behavior feature data is obtained.
Specifically, the server can compare and analyze the extracted characteristic data and the acquired behavior characteristic data respectively, and analyze the behavior characteristic data through the characteristic data of the same level after determining that the data is the data of the same level.
For example, in the behavior feature data, the total login days of the user are 5 days, in the current behavior data, the user has login and shares the APP to perform code scanning and riding, the user has login APP can be extracted, and then the login days in the current behavior data are updated based on the data, namely, the current behavior data are updated to 6 days.
In this embodiment, determining the corresponding service data tag according to the behavior feature data, and acquiring the corresponding service data based on the service data tag may include: and determining a corresponding service data label according to the updated behavior characteristic data, and acquiring corresponding service data based on the service data label.
In particular, due to the updating of the user's behavioral characteristic data, the combination of riding bites suitable for the user may change accordingly, e.g., the user changes from riding occasionally before to riding every day, so that the combination of riding bites suitable for the user will change. Therefore, the server can determine the corresponding service data label by analyzing and processing the updated behavior characteristic data and push the corresponding service data.
In the above embodiment, the behavior feature data of the user is updated by the current behavior data of the user, so that the behavior of the user can be continuously perfected, a service data tag which is more matched with the user can be determined, and the accuracy of service data pushing can be further improved.
In one embodiment, the method may further include: and determining the preferential value index of the service data and sending the preferential value index to the user terminal.
The preferential value index refers to the value of service data sent to the user, for example, for a riding card sleeve meal combination, the riding card sleeve meal combination can be preferential price after being discounted and the like.
Specifically, for different business data, the preferential value index may be different, for example, for a combination of riding and cutting ferrule meals with a longer effective period, the preferential value index is higher, and for a combination of riding and cutting ferrule meals with a shorter effective period, the preferential value index is lower.
In this embodiment, the server may query and obtain the preferential value index of the corresponding service data in the database according to the corresponding service data tag, and send the preferential value index to the user terminal, so as to display the preferential value index to the user through the user terminal.
In the above embodiment, by sending the preferential value index to the user terminal, the user can obtain the service data and the preferential value index corresponding to the service data at the same time, so that the decision of the user can be accelerated, and the efficiency of data processing can be improved.
In one embodiment, determining the preference value index of the service data may include: determining each value index of a plurality of business sub-data constituting the business data; determining preferential grade indexes of the business sub-data; based on the preferential grade index and the value index of each business sub-data, preferential value index of each business sub-data is obtained; and obtaining the preferential value index of the service data based on the preferential value index of each service sub-data composing the service data.
The service data may be composed of a plurality of service sub-data, for example, for a riding card meal combination, at least 2 riding card packages may be included.
Continuing taking the combination of the riding card covers as an example, in this embodiment, after creating a plurality of riding card covers, the server may combine a plurality of riding card covers having similar characteristics according to the characteristics of each riding card cover, for example, the price of riding card cover a is consistent with that of riding card cover B, then the riding card cover a and the riding card cover B may be combined together, the effective duration of riding card cover C and the effective duration of riding card cover D are consistent, and then the riding card cover C and the riding card cover D may be combined together.
The value index of the service sub data refers to the original value of the service sub data, namely the 'card original price', and the preferential level index of the service sub data refers to the preferential level of each service sub data, namely the 'discount', for example, 7-fold, 8-fold and the like, and the preferential value index of the service sub data can be obtained based on the value index and the preferential level of the service sub data, namely the preferential price of the service sub data.
In this embodiment, different service sub-data have different original values, and the preference levels may also be different. The original value and the discount level of the riding card package can be related to the effective duration, the riding times and the like of the riding card package, for example, the longer the effective duration of the package is, the lower the discount is, the shorter the effective duration of the package is, the higher the discount is.
Further, the server adds the preferential value indexes of the plurality of business sub-data of the business data to obtain the preferential value indexes of the business data.
In the above embodiment, by respectively acquiring the value index and the preference level index of each different service sub-data, and then obtaining the preference value index of the service data, the accuracy of the obtained preference value index of the service data can be improved.
In one embodiment, determining the preference value index of the service data may include: acquiring a value index of service data; determining a user preferential grade index corresponding to the user based on the behavior characteristic data of the user; and obtaining the preferential value index of the service data based on the preferential grade index of the user and the value index of the service data.
The user preference level index refers to preference levels of corresponding users. Specifically, the server may determine the user according to the behavior feature data of the user, for example, purchase history data, riding history data, and the like, and determine whether the user is an old user, if the user is an old user, a higher discount level may be allocated to the user, and if the user is a new customer or a user who uses the shared booster vehicle infrequently, a lower discount may be allocated to the user.
Optionally, the server may further score the credit rating of the user according to the behavior feature data of the user, and assign different preference rating indexes to the user based on the user score.
In the above embodiment, the user preference level index is determined based on the behavior feature data of the user, and the preference value index of the service data is obtained based on the user preference level index and the value index of the service data, so that the preference value index of the service data is determined based on the behavior feature data, and the preference value index of the service data and the behavior of the user have great relevance.
It should be understood that, although the steps in the flowcharts of fig. 2 and 4 are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 2 and 4 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor does the order in which the sub-steps or stages are performed necessarily occur in sequence, but may be performed alternately or alternately with at least a portion of the other steps or sub-steps of other steps.
In one embodiment, as shown in fig. 5, a pushing device for service data is provided, which may include: the system comprises a current behavior data receiving module 100, a behavior characteristic data obtaining module 200, a business data obtaining module 300 and a pushing module 400, wherein:
the current behavior data receiving module 100 is configured to receive current behavior data collected by a user terminal, where the current behavior data carries a user identifier.
The behavior feature data acquisition module 200 is configured to acquire behavior feature data corresponding to the user identifier, where the behavior feature data is generated based on historical behavior data.
The service data obtaining module 300 is configured to determine a corresponding service data tag according to the behavior feature data, and obtain corresponding service data based on the service data tag.
And the pushing module 400 is configured to push the acquired service data to the user terminal.
In one embodiment, the apparatus may further include:
and the characteristic data extraction module is used for extracting the characteristic data of the current behavior data in multiple layers to obtain the characteristic data of multiple layers.
And the updating module is used for updating the behavior characteristic data of the same level of the user according to the characteristic data of each level to obtain updated behavior characteristic data.
The service data obtaining module 300 is configured to determine a corresponding service data tag according to the updated behavior feature data, and obtain corresponding service data based on the service data tag.
In one embodiment, the apparatus may further include:
and the preferential value index determining module is used for determining the preferential value index of the service data and transmitting the preferential value index to the user terminal.
In one embodiment, the preference value indicator determination module may include:
the first value index determination submodule is used for determining value indexes of a plurality of service sub-data which form the service data.
And the preferential grade index determination submodule is used for determining preferential grade indexes of all business sub-data.
The first preferential value index generation sub-module is used for obtaining the preferential value index of each business sub-data based on the preferential grade index and the value index of each business sub-data.
The second preferential value index generation sub-module is used for obtaining the preferential value index of the service data based on the preferential value indexes of the service sub-data forming the service data.
In one embodiment, the preference value indicator determination module may include:
and the second value index determining submodule is used for acquiring the value index of the service data.
And the user preferential grade index determination submodule is used for determining the user preferential grade index corresponding to the user based on the attribute label of the user.
And the third preferential value index generation sub-module is used for obtaining the preferential value index of the service data based on the preferential grade index of the user and the value index of the service data.
For specific limitation of the service data pushing device, reference may be made to the limitation of the service data pushing method hereinabove, and the description thereof will not be repeated here. The modules in the service data pushing device can be realized in whole or in part by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 6. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing business data, business sub-data, user history behavior data and other data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a method of pushing business data.
It will be appreciated by those skilled in the art that the structure shown in FIG. 6 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory storing a computer program and a processor that when executing the computer program performs the steps of: receiving current behavior data acquired by a user terminal, wherein the current behavior data carries a user identifier; acquiring behavior characteristic data corresponding to the user identifier, wherein the behavior characteristic data is generated based on historical behavior data; determining a corresponding service data label according to the behavior characteristic data, and acquiring corresponding service data based on the service data label; and pushing the acquired service data to the user terminal.
In one embodiment, the following steps may also be implemented when the processor executes the computer program: extracting multi-level characteristic data from the current behavior data to obtain characteristic data of a plurality of levels; and updating the behavior feature data of the same level of the user according to the feature data of each level to obtain updated behavior feature data. The processor may be configured to determine a corresponding service data tag according to the behavior feature data when executing the computer program, and obtain corresponding service data based on the service data tag, and may include: and determining a corresponding service data label according to the updated behavior characteristic data, and acquiring corresponding service data based on the service data label.
In one embodiment, the following steps may also be implemented when the processor executes the computer program: and determining the preferential value index of the service data and sending the preferential value index to the user terminal.
In one embodiment, the processor, when executing the computer program, implements determining the preference value indicator of the service data, and may include: determining each value index of a plurality of business sub-data constituting the business data; determining preferential grade indexes of the business sub-data; based on the preferential grade index and the value index of each business sub-data, preferential value index of each business sub-data is obtained; and obtaining the preferential value index of the service data based on the preferential value index of each service sub-data composing the service data.
In one embodiment, the processor, when executing the computer program, implements determining the preference value indicator of the service data, and may include: acquiring a value index of service data; determining a user preferential grade index corresponding to the user based on the behavior characteristic data of the user; and obtaining the preferential value index of the service data based on the preferential grade index of the user and the value index of the service data.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of: receiving current behavior data acquired by a user terminal, wherein the current behavior data carries a user identifier; acquiring behavior characteristic data corresponding to the user identifier, wherein the behavior characteristic data is generated based on historical behavior data; determining a corresponding service data label according to the behavior characteristic data, and acquiring corresponding service data based on the service data label; and pushing the acquired service data to the user terminal.
In one embodiment, the computer program when executed by the processor may further implement the steps of: extracting multi-level characteristic data from the current behavior data to obtain characteristic data of a plurality of levels; and updating the behavior feature data of the same level of the user according to the feature data of each level to obtain updated behavior feature data. The computer program, when executed by the processor, may implement determining a corresponding service data tag according to the behavior feature data, and obtaining the corresponding service data based on the service data tag, may include: and determining a corresponding service data label according to the updated behavior characteristic data, and acquiring corresponding service data based on the service data label.
In one embodiment, the computer program when executed by the processor may further implement the steps of: and determining the preferential value index of the service data and sending the preferential value index to the user terminal.
In one embodiment, the computer program, when executed by the processor, implements determining a benefit value indicator for the business data, may include: determining each value index of a plurality of business sub-data constituting the business data; determining preferential grade indexes of the business sub-data; based on the preferential grade index and the value index of each business sub-data, preferential value index of each business sub-data is obtained; and obtaining the preferential value index of the service data based on the preferential value index of each service sub-data composing the service data.
In one embodiment, the computer program, when executed by the processor, implements determining a benefit value indicator for the business data, may include: acquiring a value index of service data; determining a user preferential grade index corresponding to the user based on the behavior characteristic data of the user; and obtaining the preferential value index of the service data based on the preferential grade index of the user and the value index of the service data.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (10)
1. The service data pushing method is characterized by comprising the following steps:
receiving current behavior data acquired by a user terminal, wherein the current behavior data carries a user identifier;
acquiring behavior characteristic data corresponding to the user identifier, wherein the behavior characteristic data is generated based on historical behavior data;
extracting multi-level characteristic data from the current behavior data to obtain multi-level characteristic data; the characteristic data and the behavior characteristic data are the same in type;
according to the feature data of each level, the behavior feature data of the same level of the user is updated to obtain updated behavior feature data;
determining a corresponding service data tag according to the behavior characteristic data, and acquiring corresponding service data based on the service data tag, wherein the method comprises the following steps: determining a corresponding service data label according to the updated behavior characteristic data, and acquiring corresponding service data based on the service data label;
pushing the acquired service data to the user terminal, including: determining a plurality of corresponding service data labels according to the behavior characteristic data, sorting the plurality of service data labels based on the correlation between the behavior characteristic data and the service data corresponding to the service data labels, and pushing the acquired service data to the user terminal according to the sorting;
determining preferential value indexes of service data and sending the preferential value indexes to a user terminal; wherein, determining the preferential value index of the service data comprises: determining each value index of a plurality of business sub-data composing the business data; determining preferential grade indexes of the business sub-data; based on the preferential grade index and the value index of each business sub-data, preferential value index of each business sub-data is obtained; and obtaining the preferential value index of the service data based on the preferential value index of each service sub-data composing the service data.
2. The method of claim 1, wherein the determining the offer value indicator for the business data comprises:
acquiring a value index of the service data;
determining a user preferential grade index corresponding to the user based on the behavior characteristic data of the user;
and obtaining the preferential value index of the service data based on the preferential grade index of the user and the value index of the service data.
3. The method of claim 1, wherein the obtaining behavior feature data corresponding to the user identification, the behavior feature data generated based on historical behavior data, comprises:
and determining that the users with the basically consistent behavior characteristic data are the same type of users.
4. The method of claim 2, wherein the determining the user preference level indicator corresponding to the user based on the behavior feature data of the user comprises:
and scoring the credit rating of the user according to the behavior characteristic data of the user, and distributing different preferential rating indexes for the user based on the user score.
5. A traffic data pushing apparatus, comprising:
the current behavior data receiving module is used for receiving current behavior data acquired by the user terminal, wherein the current behavior data carries a user identifier;
the behavior characteristic data acquisition module is used for acquiring behavior characteristic data corresponding to the user identifier, and the behavior characteristic data is generated based on historical behavior data;
the characteristic data extraction module is used for extracting the characteristic data of the current behavior data in multiple layers to obtain the characteristic data of multiple layers; the characteristic data and the behavior characteristic data are the same in type;
the updating module is used for updating the behavior feature data of the same level of the user according to the feature data of each level to obtain updated behavior feature data;
the service data acquisition module is used for determining a corresponding service data tag according to the behavior characteristic data and acquiring the corresponding service data based on the service data tag, and comprises the following steps: determining a corresponding service data label according to the updated behavior characteristic data, and acquiring corresponding service data based on the service data label;
the first pushing module is configured to push the acquired service data to the user terminal, and includes: determining a plurality of corresponding service data labels according to the behavior characteristic data, sorting the plurality of service data labels based on the correlation between the behavior characteristic data and the service data corresponding to the service data labels, and pushing the acquired service data to the user terminal according to the sorting;
the service data preferential value determining module is used for determining preferential value indexes of service data and comprises the following steps: determining each value index of a plurality of business sub-data composing the business data; determining preferential grade indexes of the business sub-data; based on the preferential grade index and the value index of each business sub-data, preferential value index of each business sub-data is obtained; based on the preferential value index of each business sub-data composing the business data, acquiring the preferential value index of the business data;
and the second pushing module is used for sending the preferential value index to the user terminal.
6. The apparatus of claim 5, wherein the business data offer value determination module comprises:
the second value index determining submodule is used for acquiring the value index of the service data;
the user preferential grade index determining submodule is used for determining a user preferential grade index corresponding to the user based on the behavior characteristic data of the user;
and the third preferential value index generation sub-module is used for obtaining the preferential value index of the service data based on the preferential grade index of the user and the value index of the service data.
7. The apparatus of claim 5, wherein the behavioral characteristic data acquisition module comprises:
and the user determination submodule is used for determining that the users with basically consistent behavior characteristic data are the same type of users.
8. The apparatus of claim 5, wherein the user preference level indicator determination submodule comprises:
and the user credit scoring sub-module is used for scoring the credit grade of the user according to the behavior characteristic data of the user and distributing different preferential grade indexes for the user based on the user score.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 4 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 4.
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| CN112907276A (en) * | 2021-01-22 | 2021-06-04 | 北京达佳互联信息技术有限公司 | Task processing method and device and storage medium |
| CN114357286A (en) * | 2021-12-24 | 2022-04-15 | 广州风腾网络科技有限公司 | Method, system, equipment and storage medium for pushing data marketing scheme |
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