CN111222919A - Business data pushing method and device, computer equipment and storage medium - Google Patents
Business data pushing method and device, computer equipment and storage medium Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 38
- 230000006399 behavior Effects 0.000 claims description 172
- 238000004590 computer program Methods 0.000 claims description 23
- 230000002349 favourable effect Effects 0.000 claims description 10
- 238000013075 data extraction Methods 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 abstract description 2
- 230000001351 cycling effect Effects 0.000 description 20
- 235000012054 meals Nutrition 0.000 description 11
- 238000010586 diagram Methods 0.000 description 5
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- 230000002354 daily effect Effects 0.000 description 1
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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
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
- G06Q30/0222—During e-commerce, i.e. online transactions
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0645—Rental transactions; Leasing transactions
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Abstract
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for pushing service data, a computer device, and a storage medium. The method comprises the following steps: receiving current behavior data collected by a user terminal, wherein the current behavior data carries a user identifier; acquiring behavior characteristic data corresponding to the user identification, 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 pushing accuracy of 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 an apparatus for pushing service data, a computer device, and a storage medium.
Background
With the popularization of the sharing travel mode, the number of sharing vehicles (such as sharing single vehicles, sharing moped vehicles and the like) is more and more, and great convenience is brought to short-distance travel of users. During the shared trip, the user typically makes payment for the ride fee by purchasing a ride card.
In the prior art, when riding card service data pushing is performed by a shared platform, all service data is generally pushed to all user terminals, so that effective pushing of the service data is less, and accuracy of data pushing is lower.
Disclosure of Invention
In view of the foregoing, it is necessary to provide a method and an apparatus for pushing business data, a computer device, and a storage medium, which can improve the accuracy of pushing business data.
A method for pushing service data includes:
receiving current behavior data collected by a user terminal, wherein the current behavior data carries a user identifier;
acquiring behavior characteristic data corresponding to the user identification, 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 includes:
extracting multi-level feature data from the current behavior data to obtain multi-level feature data;
according to the feature data of each layer, updating the behavior feature data of the same layer of the user to obtain updated behavior feature data;
determining a corresponding service data label according to the behavior feature data, and acquiring corresponding service data based on the service data label, wherein the method comprises the following steps:
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 includes:
and determining the preferential value index of the service data, and sending the preferential value index to the user terminal.
In one embodiment, determining a favorable value indicator of the service data includes:
determining each value index of a plurality of service subdata forming the service data;
determining the preferential grade index of each service subdata;
obtaining the preferential value indexes of the service subdata based on the preferential grade indexes and the value indexes of the service subdata;
and obtaining the preferential value indexes of the service data based on the preferential value indexes of the service subdata forming the service data.
In one embodiment, determining a favorable value indicator of the service 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 user preferential grade index and the value index of the service data.
A pushing device of service data, the device comprising:
the current behavior data receiving module is used for receiving current behavior data collected by the user terminal, and 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 business data acquisition module is used for determining a corresponding business data label according to the behavior characteristic data and acquiring corresponding business data based on the business 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 multi-level characteristic data from the current behavior data to obtain multi-level characteristic data;
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 acquisition module is used for 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 apparatus further includes:
and the preferential value index determining module is used for determining the preferential value index of the service data and sending 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 of the methods described above when the processor executes the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any of the above.
According to the business data pushing method, the business data pushing device, the computer equipment and the storage medium, the current behavior data collected by the user terminal is received, the current behavior data carries the user identification, then the behavior characteristic data corresponding to the user identification is obtained, the behavior characteristic data is generated based on historical behavior data, the corresponding business data label is further determined according to the behavior characteristic data, and the business data obtained based on the business 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 pushing the service data can be improved.
Drawings
Fig. 1 is an application scenario diagram of a pushing method of service data in an embodiment;
fig. 2 is a schematic flow chart of a method for pushing service data in an embodiment;
FIG. 3 is a flowchart illustrating the behavior feature data update step in one embodiment;
fig. 4 is a schematic flow chart of a method for pushing service data in another embodiment;
fig. 5 is a block diagram of a pushing apparatus for service data according to an embodiment;
FIG. 6 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The pushing method of the service data provided by the application can be applied to the application environment shown in fig. 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 is sent to the server 104. After receiving the current behavior data acquired by the terminal 102, the server 104 acquires 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, acquires the corresponding service data based on the service data tag, acquires the service data corresponding to the matched service data tag, and then pushes the acquired service data to the terminal 102. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, portable wearable devices, and the like, and the server 104 may be implemented by an independent server or a server cluster formed by a plurality of servers.
In an embodiment, as shown in fig. 2, a method for pushing service data is provided, which is described by taking the method as an example applied to the server in fig. 1, and includes the following steps:
step S202, receiving current behavior data collected by a user terminal, 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 different service scenarios, for example, in the shared riding service, the service data may refer to riding card package combination data of a shared bicycle or a shared moped, and for the insurance industry, the service data may refer to various different types of insurance package data, and the like.
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, recharging data, purchase request data, user riding data and the like of various websites.
Specifically, the browsing data of the website refers to data generated by browsing the website corresponding to the service data by the user through the user terminal, such as browsing the corresponding service through a shared riding Application (APP), or browsing the corresponding website through a browser, etc.; the recharging data refers to data generated by actions of recharging the account by the user and the like, such as recharging riding money in the account by the user, recharging riding times and the like; the purchase request data can mean that the user requests to purchase a riding card package through the shared riding APP; the user riding data refers to data generated in the process of unlocking to locking and stopping riding through code scanning by sharing software such as riding APP.
The user identifier is an identifier corresponding to a 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 behavior operations 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 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, among other things, user liveness data and user cardholder data. Specifically, the user activity data includes, but is not limited to, login behavior data of the user logging in the shared riding APP or the applet, such as login frequency, login time, stay time, login place, and the like, and trip behavior data, such as 2-turn or 4-turn trip tool, trip frequency, trip time, and the like; user card data includes, but is not limited to, card type, e.g., remaining valid time of card, remaining number of times, etc., as well as historical card purchase time, frequency of card purchases, card purchase 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 behavior characteristic data can include, but is not limited to, the total weekly riding time of the user, the total riding times, the frequency of riding card purchase by the user, the card surface amount distribution of riding cards purchased by the user, the user recharging frequency, the distribution of each recharging amount and the like.
In this embodiment, the server may extract feature data from 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 consistent behavior feature data are users of the same type, for example, a user with a recharging frequency in a certain interval range may determine that the users are users of the same type, and assign a corresponding user type tag to the user, for example, referring to fig. 3, the user type tags may be "user type 1", "user type 2", and "user type 3".
Step S206, according to the behavior characteristic data, determining a corresponding service data label, and acquiring corresponding service data based on the service data label.
The service data label is a label used for indicating uniqueness of service data, for example, with continued reference to fig. 3, the label may refer to a label of service data corresponding to different package combinations, such as "package combination 1", "package combination 2", and "package combination 3", where the service data label corresponds to the service data one to one.
As previously mentioned, the behavior characteristic data may include, but is not limited to, the total time of the cycling of the user, the total times of cycling, the frequency of purchasing the cycling card by the user, the distribution of the card surface amount of the cycling card purchased by the user, the frequency of recharging by the user, the distribution of the recharging amount each time, and the like. In this embodiment, the server may analyze the behavior feature data of the user, and determine a suitable cycling card meal combination, for example, if the user a uses the shared moped for multiple times every day, it is determined that the cycling card meal combination calculated according to the number of usage days is more suitable for the user a, and if the user B cycles occasionally, it is determined that the cycling card meal combination calculated according to the number of usage cycles is more suitable for the user B, or if the user C cycles for a longer time each time, it may be determined that the cycling card meal combination calculated according to the number of times is more suitable for the user C.
Further, the server may analyze the behavior feature data of the user, determine a service data tag corresponding to the user, and then obtain corresponding service data, such as a specific riding card meal combination, from the database according to the service data tag.
Optionally, with 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 corresponding to the user according to the one-to-one correspondence relationship 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 tags corresponding to the user, rank the plurality of service data tags based on the correlation between the behavior feature data of the user and the service data corresponding to the service data tags, and push the plurality of service data to the user terminal according to the rank for the user to select.
In the method for pushing 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 pushing the service data 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 multi-level feature data.
As described above, 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, 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, recharging data, purchase request data, and user riding data of various websites, and the server may extract a plurality of levels of feature data of the current behavior data, for example, recharging amount, riding time, riding duration, browsing records of a riding card package, and the like, to obtain a plurality of different levels of feature data.
Step S404, according to the feature data of each layer, the behavior feature data of the same layer of the user is updated, and the updated behavior feature data is obtained.
Specifically, the server may analyze the behavior feature data through comparing the extracted feature data with the acquired behavior feature data, and after determining that the data is data of the same layer, analyze the behavior feature data through the feature data of the same layer.
For example, in the behavior feature data, the total login days of the user is 5 days, and in the current behavior data, if the user has a login sharing riding APP to sweep the code for riding, it may be extracted that the user has a login APP, and then the login days in the current behavior data are updated based on the data, that is, the current behavior data are updated to 6 days.
In this embodiment, determining a corresponding service data tag according to the behavior feature data, and acquiring 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.
Specifically, due to the updating of the behavior feature data of the user, the cycling card meal combination suitable for the user can be changed correspondingly, for example, the user changes from the previous occasional cycling to daily cycling, so that the cycling card meal combination suitable for the user will be changed. 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 embodiment, the behavior characteristic data of the user is updated according to the current behavior data of the user, so that the behavior of the user can be continuously improved, the service data label 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 the service data sent to the user, for example, for a cycling card meal combination, the preferential price of the cycling card meal combination after discount and other preferential prices can be realized.
Specifically, the preferential value index of different service data may be different, for example, the preferential value index of a cycling card meal combination with a longer validity period is higher for a greater number of cycles of cycling, and the preferential value index of a cycling card meal combination with a shorter validity period is lower for a fewer number of cycles of cycling.
In this embodiment, the server may query and obtain the preferential value index of the corresponding service data from 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 embodiment, the preferential value indexes are sent to the user terminal, so that the user can simultaneously obtain the service data and the preferential value indexes corresponding to the service data, the decision of the user can be accelerated, and the data processing efficiency is improved.
In one embodiment, determining the favorable value indicator of the service data may include: determining each value index of a plurality of service subdata forming the service data; determining the preferential grade index of each service subdata; obtaining the preferential value indexes of the service subdata based on the preferential grade indexes and the value indexes of the service subdata; and obtaining the preferential value indexes of the service data based on the preferential value indexes of the service subdata forming the service data.
The service data may be composed of a plurality of service sub-data, for example, for a cycling card package combination, at least 2 cycling card packages may be included.
Continuing to use the card package combination of riding as an example, in this embodiment, after the server creates a plurality of card packages of riding, a plurality of card packages of riding with similar characteristics can be combined together according to the characteristics of each card package of riding, for example, the price of the card package a of riding is consistent with that of the card package B of riding, then the card package a of riding can be combined together with the card package B of riding, the effective duration of the card package C of riding is consistent with that of the card package D of riding, then the card package C of riding can be combined together with the card package D of riding.
The value index of the service subdata refers to the original value of the service subdata, namely 'card original price', the preferential grade index of the service subdata refers to the preferential grade of each service subdata, namely 'discount', for example, 7-fold, 8-fold and the like, and the preferential value index of the service subdata can be obtained based on the value index and the preferential grade of the service subdata, namely the preferential price of the service subdata is obtained.
In this embodiment, different service sub data have different original values and different preference levels. The original value and the preferential level of the riding card package can be related to the effective duration, riding times and the like of the riding card package, for example, the longer the effective duration of the package is, the lower the preferential level is, the less the discount is, the shorter the effective duration of the package is, the higher the preferential level is, and the larger the discount is.
Further, the server adds the preferential value indexes of the plurality of service subdata of the service data to obtain the preferential value indexes of the service data.
In the above embodiment, the accuracy of the obtained preferential value index of the service data can be improved by respectively obtaining the value index and the preferential grade index of each different service subdata and then obtaining the preferential value index of the service data.
In one embodiment, determining the favorable value indicator 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 user preferential grade index and the value index of the service data.
The user preference level index refers to the preference level of the corresponding user. Specifically, the server may determine the user according to behavior feature data of the user, for example, purchase history data, riding history data, and the like, determine whether the user is an old user, and if the user is an old user, may assign a higher discount level to the user, the discount that may be obtained is larger, and if the user is a new user or a user who does not use the shared moped frequently, assign a lower discount level to the user, the discount that may be obtained is smaller.
Optionally, the server may also score the credit rating of the user according to the behavior feature data of the user, and allocate different preference rating indexes to the user based on the user score.
In the embodiment, the preferential grade index of the user is determined based on the behavior characteristic data of the user, and the preferential value index of the business data is obtained based on the preferential grade index of the user and the value index of the business data, so that the preferential value index of the business data is determined based on the behavior characteristic data, and the preferential value index of the business data has great relevance with the behavior of the user.
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, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2 and 4 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least some of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 5, a device for pushing service data is provided, which may include: the system comprises a current behavior data receiving module 100, a behavior feature data acquiring module 200, a service data acquiring 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.
A behavior feature data obtaining module 200, configured to obtain 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.
A pushing module 400, configured to push the obtained service data to the user terminal.
In one embodiment, the apparatus may further include:
and the characteristic data extraction module is used for extracting multi-level characteristic data from the current behavior data to obtain multi-level characteristic data.
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 the 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 sending the preferential value index to the user terminal.
In one embodiment, the offer value indicator determining module may include:
and the first value index determining submodule is used for determining each value index of a plurality of service subdata forming the service data.
And the preferential level index determining submodule is used for determining the preferential level index of each service subdata.
And the first preferential value index generation submodule is used for obtaining the preferential value index of each service subdata based on the preferential grade index and the value index of each service subdata.
And the second preferential value index generation submodule is used for obtaining the preferential value index of the service data based on the preferential value index of each service subdata forming the service data.
In one embodiment, the offer value indicator determining module may include:
and the second value index determining submodule is used for acquiring the value index of the service data.
And the user preference level index determining submodule is used for determining the user preference level index corresponding to the user based on the attribute label of the user.
And the third preferential value index generation submodule is used for obtaining the preferential value index of the service data based on the user preferential grade index and the value index of the service data.
For specific limitations of the service data pushing device, reference may be made to the above limitations of the service data pushing method, which is not described herein again. All or part of the modules in the service data pushing device can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram 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 comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing data such as service data, service sub data, user historical behavior data and the like. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method for pushing business data.
Those skilled in the art will appreciate that the architecture shown in fig. 6 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, there is provided a computer device comprising a memory storing a computer program and a processor implementing the following steps when the processor executes the computer program: receiving current behavior data collected by a user terminal, wherein the current behavior data carries a user identifier; acquiring behavior characteristic data corresponding to the user identification, 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 processor, when executing the computer program, may further implement the following steps: extracting multi-level feature data from the current behavior data to obtain multi-level feature data; and updating the behavior characteristic data of the same level of the user according to the characteristic data of each level to obtain the updated behavior characteristic data. When the processor executes the computer program, 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 one embodiment, the processor, when executing the computer program, may further implement the following steps: and determining the preferential value index of the service data, and sending the preferential value index to the user terminal.
In one embodiment, the determining the favorable value indicator of the service data when the processor executes the computer program may include: determining each value index of a plurality of service subdata forming the service data; determining the preferential grade index of each service subdata; obtaining the preferential value indexes of the service subdata based on the preferential grade indexes and the value indexes of the service subdata; and obtaining the preferential value indexes of the service data based on the preferential value indexes of the service subdata forming the service data.
In one embodiment, the determining the favorable value indicator of the service data when the processor executes the computer program 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 user preferential grade index 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 collected by a user terminal, wherein the current behavior data carries a user identifier; acquiring behavior characteristic data corresponding to the user identification, 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 feature data from the current behavior data to obtain multi-level feature data; and updating the behavior characteristic data of the same level of the user according to the characteristic data of each level to obtain the updated behavior characteristic data. When executed by the processor, the computer program may determine a corresponding service data tag according to the behavior feature data, 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 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 favorable value indicator for the business data, and may include: determining each value index of a plurality of service subdata forming the service data; determining the preferential grade index of each service subdata; obtaining the preferential value indexes of the service subdata based on the preferential grade indexes and the value indexes of the service subdata; and obtaining the preferential value indexes of the service data based on the preferential value indexes of the service subdata forming the service data.
In one embodiment, the computer program, when executed by the processor, implements determining a favorable value indicator for the business 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 user preferential grade index and the value index of the service data.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile 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), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method for pushing service data, the method comprising:
receiving current behavior data collected by a user terminal, wherein the current behavior data carries a user identifier;
acquiring behavior characteristic data corresponding to the user identification, 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.
2. The method of claim 1, further comprising:
extracting multi-level feature data from the current behavior data to obtain multi-level feature data;
according to the feature data of each layer, updating the behavior feature data of the same layer of the user to obtain updated behavior feature data;
the determining a corresponding service data label according to the behavior feature data and acquiring corresponding service data based on the service data label 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.
3. The method of claim 1, further comprising:
and determining a preferential value index of the service data, and sending the preferential value index to the user terminal.
4. The method of claim 3, wherein the determining the favorable value indicator of the business data comprises:
determining each value index of a plurality of service subdata forming the service data;
determining the preferential grade index of each service subdata;
obtaining the preferential value indexes of the service subdata based on the preferential grade indexes and the value indexes of the service subdata;
and obtaining the preferential value indexes of the service data based on the preferential value indexes of the service subdata forming the service data.
5. The method of claim 3, wherein the determining the favorable value indicator of the business data comprises:
acquiring a value index of the service data;
determining a user preferential level 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 user preferential grade index and the value index of the service data.
6. A device for pushing service data, comprising:
the system comprises a current behavior data receiving module, a behavior data processing module and a behavior data processing module, wherein the current behavior data receiving module is used for receiving current behavior data collected by a user terminal, and 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 business data acquisition module is used for determining a corresponding business data label according to the behavior characteristic data and acquiring corresponding business data based on the business data label;
and the pushing module is used for pushing the acquired service data to the user terminal.
7. The apparatus of claim 6, further comprising:
the characteristic data extraction module is used for extracting multi-level characteristic data from the current behavior data to obtain multi-level characteristic data;
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 acquisition module is used for determining a corresponding service data label according to the updated behavior characteristic data and acquiring corresponding service data based on the service data label.
8. The apparatus of claim 6, further comprising:
and the preferential value index determining module is used for determining the preferential value index of the service data and sending the preferential value index to the user terminal.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 5 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 5.
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