CN110782288A - Cloud computing aggregate advertisement data processing method, device, equipment and medium - Google Patents
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Abstract
The invention relates to the technical field of computer technology, in particular to a cloud computing aggregated advertisement data processing method, a device, equipment and a medium, wherein the cloud computing aggregated advertisement data processing method comprises the following steps: s10: acquiring historical behavior data of a user, wherein the historical behavior data of the user comprises user query data and user use record data; s20: clustering analysis is carried out on the historical user behavior data to obtain user attribute data; s30: acquiring a push advertisement type, and associating the push advertisement type with the corresponding user attribute data; s40: and sending an advertisement pushing result to each user client of the user attribute data according to the association result. The invention has the advantages of promoting the advertisement push, particularly the relevance of the game advertisement push, and being beneficial to promoting the effect of the advertisement push.
Description
Technical Field
The invention relates to the technical field of computer technology, in particular to a cloud computing aggregate advertisement data processing method, device, equipment and medium.
Background
Currently, with the rapid development of the internet industry, mobile advertising becomes a new mode of the current internet advertising industry. The mobile advertisement can recommend corresponding advertisement information to the corresponding user according to the consumption habit of the user.
In the existing mobile advertisement push, especially in the mobile advertisement push related to the game industry, recommendation of game advertisements to users or players is performed only through the downloading or searching history of the users or players, although the recommendation can meet the interests of the users or players to a certain extent, after the downloading or searching history of the users or players is analyzed, corresponding games are recommended to the users or players, and there is a case that games included in the game advertisements recommended to the users or players do not meet the game level of the users or players, so there is a room for improvement.
Disclosure of Invention
The invention aims to provide a cloud computing aggregation advertisement data processing method, a device, equipment and a medium which are used for improving advertisement pushing, particularly the relevance of game advertisement pushing and are beneficial to improving the advertisement pushing effect.
The above object of the present invention is achieved by the following technical solutions:
a cloud computing aggregate advertisement data processing method comprises the following steps:
s10: acquiring historical behavior data of a user, wherein the historical behavior data of the user comprises user query data and user use record data;
s20: clustering analysis is carried out on the historical user behavior data to obtain user attribute data;
s30: acquiring a push advertisement type, and associating the push advertisement type with the corresponding user attribute data;
s40: and sending an advertisement pushing result to each user client of the user attribute data according to the association result.
By adopting the technical scheme, the latitude of analyzing the user attribute can be increased by acquiring the user historical behavior data of the user and dividing the user historical behavior data into the user query data and the user use record data, so that the accuracy and the relevance of the pushed advertisement can be improved; furthermore, the historical behavior data of the user is subjected to cluster analysis, so that the obtained user attribute data can be more accurate, meanwhile, a pushed advertisement result is sent to the user client according to the user attribute data, and advertisement content which is consistent with the real use habit of the user can be pushed to the user according to the history query record of the user and the historical use record of the user, so that the relevance of the advertisement and the user is favorably improved, and the viscosity between the pushed advertisement and the user is improved.
The invention is further configured to: step S10 includes:
s11: the user query data comprises user search data and user download data, and the user search data and the user download data are used as the corresponding user search data and the corresponding user download data according to user search behaviors and user download behaviors triggered by a user;
s12: setting a first weight value for the user search data, setting a second weight value for the user download data, and using the user search data and the user download data as the user query data according to the first weight value and the second weight value.
By adopting the technical scheme, the result of the user query data obtained by calculation can be more reasonable according to different query behaviors of the user by setting the first weight value for the user search data and taking the user disaster elimination data as the second weight value.
The invention is further configured to: step S20 includes:
s21: acquiring user interest data from user query data in the user historical behavior data;
s22: acquiring attribute data of the user to be clustered from the user interest data;
s23: acquiring user operation grade data from the user use record data;
s24: and clustering user attribute data corresponding to the user use record data from the user attribute data to be clustered according to the user operation level data.
By adopting the technical scheme, the user interest data which is interested by the user is obtained in the behavior of using the program or the software by the user, and according to the user using record data of using the software or the program by the user, the obtained user attribute data can accord with the actual operation of the user or the condition of using the software through the user operation grade data, thereby being beneficial to pushing related advertisement content to the user.
The invention is further configured to: step S30 includes:
s31: forming a type data set to be matched by all the pushed advertisement types, and matching in the type data set to be matched by using the user attribute data;
s32: and associating the successfully matched push advertisement type with the user attribute data.
By adopting the technical scheme, the user attribute data is associated with the corresponding pushed advertisement type, so that the corresponding user can be helped to push the advertisement content which is consistent with the user when the advertisement is pushed.
The invention is further configured to: step S40 includes:
s41: if the advertisement creating message is obtained, obtaining advertisement attribute information from the advertisement creating message;
s42: classifying the advertisement attribute information according to the user attribute data;
s43: and generating the advertisement pushing result corresponding to each user attribute data according to the classification result, and sending the advertisement pushing result to the corresponding user client.
By adopting the technical scheme, when the advertisement is created, the advertisement attribute information in the advertisement creating message is classified according to the user attribute data, the advertisement pushing result can be pushed or sent to the corresponding user client, and the relevance between the advertisement and the user is ensured.
The second aim of the invention is realized by the following technical scheme:
a cloud computing syndication ad data processing device, the cloud computing syndication ad data processing device comprising:
the data acquisition module is used for acquiring historical behavior data of a user, wherein the historical behavior data of the user comprises user query data and user use record data;
the analysis module is used for carrying out cluster analysis on the historical user behavior data to obtain user attribute data;
the association module is used for acquiring the types of the pushed advertisements and associating the types of the pushed advertisements with the corresponding user attribute data;
and the pushing module is used for sending an advertisement pushing result to each user client of the user attribute data according to the correlation result.
By adopting the technical scheme, the latitude of analyzing the user attribute can be increased by acquiring the user historical behavior data of the user and dividing the user historical behavior data into the user query data and the user use record data, so that the accuracy and the relevance of the pushed advertisement can be improved; furthermore, the historical behavior data of the user is subjected to cluster analysis, so that the obtained user attribute data can be more accurate, meanwhile, a pushed advertisement result is sent to the user client according to the user attribute data, and advertisement content which is consistent with the real use habit of the user can be pushed to the user according to the history query record of the user and the historical use record of the user, so that the relevance of the advertisement and the user is favorably improved, and the viscosity between the pushed advertisement and the user is improved.
The third object of the invention is realized by the following technical scheme:
a computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the cloud computing aggregate advertisement data processing method when executing the computer program.
The fourth object of the invention is realized by the following technical scheme:
a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the above-described cloud computing aggregate advertisement data processing method.
In conclusion, the beneficial technical effects of the invention are as follows:
1. by acquiring the user historical behavior data of the user and dividing the user historical behavior data into the user query data and the user use record data, the latitude of analyzing the user attribute can be increased, and the accuracy and the relevance of the pushed advertisement can be improved;
2. according to the user attribute data, the advertisement pushing result is sent to the user client, and the advertisement content which is consistent with the real use habit of the user can be pushed to the user according to the history query record of the user and the history use record of the user, so that the relevance between the advertisement and the user is improved, and the viscosity between the pushed advertisement and the user is improved.
Drawings
FIG. 1 is a flowchart of a cloud computing aggregate advertisement data processing method according to an embodiment of the present invention;
fig. 2 is a flowchart of implementing step S10 in the cloud computing aggregate advertisement data processing method according to an embodiment of the present invention;
fig. 3 is a flowchart of implementing step S20 in the cloud computing aggregate advertisement data processing method according to an embodiment of the present invention;
fig. 4 is a flowchart of implementing step S30 in the cloud computing aggregate advertisement data processing method according to an embodiment of the present invention;
fig. 5 is a flowchart of implementing step S40 in the cloud computing aggregate advertisement data processing method according to an embodiment of the present invention;
fig. 6 is a schematic block diagram of a cloud computing aggregate advertisement data processing apparatus according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a computer device according to an embodiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The first embodiment is as follows:
in an embodiment, as shown in fig. 1, the invention discloses a cloud computing aggregate advertisement data processing method, which specifically includes the following steps:
s10: and acquiring user historical behavior data, wherein the user historical behavior data comprises user query data and user usage record data.
In this embodiment, the historical behavior data refers to the behavior of a user or a player of the game software, such as searching, downloading, or using the game software in the past period of time. The past period of time may be a preset period of time, such as a year, a half year, or three months, or may be a period of time from the platform where the user registers the relevant downloaded software to the present. The user query data refers to records of searches and downloads of games in a platform for downloading game software by a user or a player. The user usage record data is a record of the user or player using and running the game downloaded from the platform.
Specifically, when a user searches for or downloads a game on the platform of the downloaded game, the name and type of game software downloaded by the user are acquired and recorded as the user query data. It can be understood that the type of the game software is recorded when the game software is developed and online on the platform.
Further, when the user uses or runs the downloaded game, the usage record of the game software used by the user is recorded. Wherein the usage record includes the frequency of use of the game by the user and the condition of use of the game. And the usage record using the game software is taken as the user usage record data.
S20: and clustering and analyzing the historical behavior data of the user to obtain user attribute data.
In the present embodiment, the user attribute data refers to data that conforms to the game software that the user is interested in and the situation in which the game software is conformed to.
Specifically, from the user historical behavior data, clustering analysis is performed on the user query data and the user usage record data respectively. And then the user attribute data is obtained.
For the data queried by the user, when the user searches, browses or downloads the game software, the types of the game software searched, browsed or downloaded by the user are recorded, and the types of the games which the user is interested in are counted.
Further, in the case that the user uses the downloaded game software, for example, the frequency of using the game software by the user, and the data representing the level of using the game by the user, for example, for a game of breaking through a gate, the number of times and the time of staying or repeatedly performing a certain gate, etc.; for a match-up type game, the time during which a user or player survives the match-up, the data generated during the match-up, and the like.
S30: the push advertisement type is obtained and associated with the corresponding user attribute data.
In this embodiment, the type of the pushed advertisement refers to the type of the game software in the pushed game advertisement. The types comprise chess and cards, roles, actions, competitions, recreation, single machines and the like, and the operation difficulty corresponding to each type.
Specifically, according to the user attribute data, the type of the pushed advertisement conforming to the user attribute data is associated with the user. The association method may be to establish a corresponding data table by using the user attribute data, record and store the type of the pushed advertisement conforming to the user attribute data in the data table, and realize association between the pushed advertisement and the corresponding user attribute data.
S40: and sending the advertisement pushing result to the user client of each user attribute data according to the association result.
In this embodiment, the advertisement push result is an advertisement of game software pushed to the user client of the user.
Specifically, when a new game advertisement is created, the game generates the advertisement push result according to the type of the created game, and the advertisement push result is sent to the user client of the user matched with the type of the game.
In this embodiment, by acquiring the user historical behavior data of the user and dividing the user historical behavior data into the user query data and the user usage record data, the latitude of analyzing the user attribute can be increased, thereby contributing to improving the accuracy and the relevance of the pushed advertisement; furthermore, the historical behavior data of the user is subjected to cluster analysis, so that the obtained user attribute data can be more accurate, meanwhile, a pushed advertisement result is sent to the user client according to the user attribute data, and advertisement content which is consistent with the real use habit of the user can be pushed to the user according to the history query record of the user and the historical use record of the user, so that the relevance of the advertisement and the user is favorably improved, and the viscosity between the pushed advertisement and the user is improved.
In an embodiment, as shown in fig. 2, in step S10, obtaining user historical behavior data, where the user historical behavior data includes user query data and user usage record data, and the method specifically includes the following steps:
s11: the user query data comprises user search data and user download data, and the user search data and the user download data are used as corresponding user search data and user download data according to user search behaviors and user download behaviors triggered by a user.
In this embodiment, the user search data refers to data of a game searched by a user or a player in a platform for downloading the game, and the user search behavior refers to data of a behavior of the user specifically implementing the search game. The user download data refers to data of a game downloaded by the user. The user downloading behavior refers to data of a behavior of a user to specifically implement downloading a game.
Wherein, the user can input the name of the game or select the type of the game first and then view the interesting game in the game class table of the type. The game name input by the user, the game type corresponding to the game name and the game type directly selected by the user can be used as the user searching behavior, and the data of all the user searching behaviors of the user form the user downloading data.
Further, if it is obtained that the user triggers the user downloading behavior to download specific game software, the name of the game software downloaded by the user and the type of the game software are used as the user downloading data.
S12: setting a first weight value for user search data, setting a second weight value for user download data, and using the user search data and the user download data as user query data according to the first weight value and the second weight value.
In this embodiment, the first weight value refers to a coefficient for identifying the degree of importance of the user search data. The second weight value is a coefficient indicating the importance of the user's data download. Preferably, a person skilled in the art can adjust the relative sizes of the first weight value and the second weight value according to actual needs, so that the user query data obtained by calculation better conforms to the actual situation.
Specifically, a first weight value is set for user search data, a second weight value is set for user download data, and the user search data and the user download data are used as user query data according to the first weight value and the second weight value.
In an embodiment, as shown in fig. 3, in step S20, that is, after performing cluster analysis on the user historical behavior data, obtaining user attribute data specifically includes the following steps:
s21: and acquiring user interest data from user query data in the user historical behavior data.
In the present embodiment, the user interest data refers to data of the type of game software in which the user is interested.
Specifically, the type of the specifically searched or downloaded game software is acquired from the user query data, and the type is composed as the user interest data.
S22: and acquiring attribute data of the user to be clustered from the user interest data.
In this embodiment, the user attribute data to be clustered refers to user attribute data that needs to be clustered.
Specifically, the user interest data is used as the user data to be clustered.
S23: user operation level data is acquired from the user usage record data.
In the present embodiment, the user operation level data refers to data of a situation in which the user operates or uses the game software.
Specifically, according to the case where the user uses the game software in step S20, the user operation level data is obtained. For example, when the user uses the game of the sports category and the user frequently dies a virtual character in the game of the sports category or the positive data such as the output injury and the amount of treatment generated are low, it may be determined that the user operation level data of the user belongs to the middle level or the low level. The determination of the "frequent casualties" or the level of the positive data of the virtual character of the user can be performed according to a scoring standard set during the development of the game.
S24: and clustering user attribute data corresponding to the user use record data from the user attribute data to be clustered according to the user operation level data.
Specifically, the attribute data of the user to be clustered is classified according to the user operation level data, and the user attribute data corresponding to the user operation level data of the user is obtained or matched from the attribute data of the user to be clustered.
In an embodiment, as shown in fig. 4, in step S30, obtaining the push advertisement type and associating the push advertisement type with the corresponding user attribute data specifically includes the following steps:
s31: and forming a data set of the type to be matched by all the pushed advertisement types, and matching in the data set of the type to be matched by using the user attribute data.
In this embodiment, the type to be matched data set refers to a data set for matching a type of a push advertisement associated with the user attribute data.
Specifically, all the types of the pushed advertisements are combined into a data set of types to be matched. Further, the user attribute data is used to match in the data set of the type to be matched, for example, the type of the game in which the user is interested, the user operation level data of each game type, and the like are included in the user attribute data.
S32: and associating the successfully matched push advertisement type with the user attribute data.
Specifically, the successfully matched push advertisement type is associated with the user attribute data.
In an embodiment, as shown in fig. 5, in step S40, that is, sending the advertisement push result to the user client of each user attribute data according to the association result, the method specifically includes the following steps:
s41: and if the advertisement creating message is acquired, acquiring advertisement attribute information from the advertisement creating message.
In the present embodiment, the advertisement creation message refers to a message for creating a new game advertisement. The advertisement attribute information is information on the type of the game and the operation difficulty in the created new game advertisement.
Specifically, when a technician creates a new game advertisement, the type of game to be pushed in order to form the advertisement is used as the advertisement attribute information. The game software in the game advertisement may be newly developed game software or already developed game software, and is not limited herein.
Further, if the advertisement creating message is obtained, the advertisement attribute information is obtained from the advertisement creating message.
S42: and classifying the advertisement attribute information according to the user attribute data.
Specifically, advertisement attribute information corresponding to the user attribute information is classified into one type based on the user attribute data.
S43: and generating an advertisement pushing result corresponding to each user attribute data according to the classification result, and sending the advertisement pushing result to the corresponding user client.
Specifically, according to the classified result, the content of the game advertisement in the advertisement creating message is generated, an advertisement pushing result corresponding to each user attribute data is generated, and the advertisement pushing result is pushed to the user client side of the advertisement attribute information corresponding to the game advertisement content.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Example two:
in an embodiment, a cloud computing aggregate advertisement data processing apparatus is provided, and the cloud computing aggregate advertisement data processing apparatus corresponds to the cloud computing aggregate advertisement data processing methods in the above embodiments one to one. As shown in fig. 6, the cloud computing aggregate advertisement data processing apparatus includes a data obtaining module 10, an analyzing module 20, an associating module 30, and a pushing module 40. The functional modules are explained in detail as follows:
the data acquisition module 10 is configured to acquire user historical behavior data, where the user historical behavior data includes user query data and user usage record data;
the analysis module 20 is configured to perform cluster analysis on the historical behavior data of the user to obtain user attribute data;
an association module 30, configured to obtain a type of a pushed advertisement, and associate the type of the pushed advertisement with corresponding user attribute data;
and the pushing module 40 is configured to send an advertisement pushing result to the user client of each user attribute data according to the association result.
Preferably, the data acquisition module 10 comprises:
the data grouping submodule 11 is used for the user to inquire the data including the user search data and the user download data, and taking the user search data and the user download data as corresponding user search data and user download data according to the user search behavior and the user download behavior triggered by the user;
the calculating submodule 12 is configured to set a first weight value for the user search data, set a second weight value for the user download data, and use the user search data and the user download data as user query data according to the first weight value and the second weight value.
Preferably, the analysis module 20 comprises:
the interest data acquisition submodule 21 is configured to acquire user interest data from user query data in the user historical behavior data;
the data to be clustered acquisition submodule 22 is used for acquiring attribute data of the user to be clustered from the user interest data;
a grade data obtaining submodule 23, configured to obtain user operation grade data from the user usage record data;
and the clustering submodule 24 is used for clustering user attribute data corresponding to the user use record data from the user attribute data to be clustered according to the user operation level data.
Preferably, the association module 30 comprises:
the matching submodule 31 is used for forming a data set of types to be matched by all the pushed advertisement types and matching the data set of the types to be matched by using the user attribute data;
and the association submodule 32 is used for associating the successfully matched push advertisement type with the user attribute data.
Preferably, the pushing module 40 includes:
an advertisement creating submodule 41, configured to, if an advertisement creating message is obtained, obtain advertisement attribute information from the advertisement creating message;
a classification submodule 42, configured to classify the advertisement attribute information according to the user attribute data;
and the pushing submodule 43 is configured to generate an advertisement pushing result corresponding to each user attribute data according to the classification result, and send the advertisement pushing result to the corresponding user client.
For specific limitations of the cloud computing aggregate advertisement data processing apparatus, reference may be made to the above limitations of the cloud computing aggregate advertisement data processing method, and details are not repeated here. The modules in the cloud computing aggregate advertisement data processing apparatus may be wholly or partially implemented by software, hardware, or 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.
Example three:
in one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 7. 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 user attribute data. 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 cloud computing aggregate advertisement data processing method.
In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
s10: acquiring historical behavior data of a user, wherein the historical behavior data of the user comprises user query data and user use record data;
s20: clustering analysis is carried out on the historical behavior data of the user to obtain user attribute data;
s30: acquiring a push advertisement type, and associating the push advertisement type with corresponding user attribute data;
s40: and sending the advertisement pushing result to the user client of each user attribute data according to the association result.
Example four:
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:
s10: acquiring historical behavior data of a user, wherein the historical behavior data of the user comprises user query data and user use record data;
s20: clustering analysis is carried out on the historical behavior data of the user to obtain user attribute data;
s30: acquiring a push advertisement type, and associating the push advertisement type with corresponding user attribute data;
s40: and sending the advertisement pushing result to the user client of each user attribute data according to the association result.
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).
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A cloud computing aggregate advertisement data processing method is characterized by comprising the following steps:
s10: acquiring historical behavior data of a user, wherein the historical behavior data of the user comprises user query data and user use record data;
s20: clustering analysis is carried out on the historical user behavior data to obtain user attribute data;
s30: acquiring a push advertisement type, and associating the push advertisement type with the corresponding user attribute data;
s40: and sending an advertisement pushing result to each user client of the user attribute data according to the association result.
2. The cloud-computing aggregate advertisement data processing method according to claim 1, wherein step S10 includes:
s11: the user query data comprises user search data and user download data, and the user search data and the user download data are used as the corresponding user search data and the corresponding user download data according to user search behaviors and user download behaviors triggered by a user;
s12: setting a first weight value for the user search data, setting a second weight value for the user download data, and using the user search data and the user download data as the user query data according to the first weight value and the second weight value.
3. The cloud-computing aggregate advertisement data processing method according to claim 1, wherein step S20 includes:
s21: acquiring user interest data from user query data in the user historical behavior data;
s22: acquiring attribute data of the user to be clustered from the user interest data;
s23: acquiring user operation grade data from the user use record data;
s24: and clustering user attribute data corresponding to the user use record data from the user attribute data to be clustered according to the user operation level data.
4. The cloud-computing aggregate advertisement data processing method according to claim 1, wherein step S30 includes:
s31: forming a type data set to be matched by all the pushed advertisement types, and matching in the type data set to be matched by using the user attribute data;
s32: and associating the successfully matched push advertisement type with the user attribute data.
5. The cloud-computing aggregate advertisement data processing method according to claim 1, wherein step S40 includes:
s41: if the advertisement creating message is obtained, obtaining advertisement attribute information from the advertisement creating message;
s42: classifying the advertisement attribute information according to the user attribute data;
s43: and generating the advertisement pushing result corresponding to each user attribute data according to the classification result, and sending the advertisement pushing result to the corresponding user client.
6. A cloud computing aggregate advertisement data processing apparatus, characterized in that the cloud computing aggregate advertisement data processing apparatus comprises:
the data acquisition module is used for acquiring historical behavior data of a user, wherein the historical behavior data of the user comprises user query data and user use record data;
the analysis module is used for carrying out cluster analysis on the historical user behavior data to obtain user attribute data;
the association module is used for acquiring the types of the pushed advertisements and associating the types of the pushed advertisements with the corresponding user attribute data;
and the pushing module is used for sending an advertisement pushing result to each user client of the user attribute data according to the correlation result.
7. The cloud computing aggregate advertisement data processing apparatus of claim 6, wherein the data acquisition module comprises:
the data grouping submodule is used for the user query data including user search data and user download data and taking the user search data and the user download data as corresponding user search data and user download data according to user search behaviors and user download behaviors triggered by a user;
and the calculation sub-module is used for setting a first weight value for the user search data, setting a second weight value for the user download data, and taking the user search data and the user download data as the user query data according to the first weight value and the second weight value.
8. The cloud computing aggregate advertisement data processing apparatus of claim 6, wherein the analysis module comprises:
the interest data acquisition sub-module is used for acquiring user interest data from user query data in the user historical behavior data;
the data to be clustered acquisition submodule is used for acquiring attribute data of the user to be clustered from the user interest data;
the grade data acquisition submodule is used for acquiring user operation grade data from the user use record data;
and the clustering submodule is used for clustering user attribute data corresponding to the user use record data from the user attribute data to be clustered according to the user operation level data.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the cloud computing aggregate advertisement data processing method according to any one of claims 1 to 5.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the cloud computing aggregate advertisement data processing method according to any one of claims 1 to 5.
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