CN114880310A - User behavior analysis method and device, computer equipment and storage medium - Google Patents
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Abstract
The invention discloses a user behavior analysis method, a user behavior analysis device, computer equipment and a storage medium, wherein the method comprises the steps of acquiring actual behavior data of a plurality of target users under a Web port in real time; respectively filling the obtained actual behavior data into different preference analysis models based on preset preference rules and preference information and analyzing; acquiring preference information of a target user in the preference analysis models and portraying the target user to obtain a user portrait of the target user; and grading the target user based on the user portrait, and recommending corresponding content to the target user according to the grade and the user portrait recommendation. Specific analysis can be performed according to different preferences of the user to solve the user behavior analysis of specific information. And moreover, the user portrait can be obtained by summarizing according to a plurality of preference analysis models, and corresponding contents can be recommended according to the portrait score of the user. Therefore, the accuracy of data processing can be improved, and fine and accurate pushing can be performed.
Description
Technical Field
The present invention relates to the field of data processing, and in particular, to a method and an apparatus for user behavior analysis, a computer device, and a storage medium.
Background
User portrait is a common technical means in the technical field of big data, and the characteristics of a user can be known through the user portrait so as to improve the efficiency of subsequent technical application.
In the user profile, user data is obtained, such as user preferences, which are typically counted, also referred to as user preference calculations. In the existing user data processing method, the accuracy of preference calculation needs to be improved. In the conventional processing mode, images of different users are generally obtained by adopting the same set of model, and targeted data cannot be constructed in the obtained user images, so that targeted data cannot be subjected to image analysis.
Disclosure of Invention
The present invention mainly aims to provide a user behavior analysis method, device, computer equipment and storage medium, and aims to solve the above technical problems.
In order to achieve the above object, the present invention provides a user behavior analysis method, which comprises the following steps:
acquiring user behavior data of a plurality of target users under a Web port in real time;
performing data cleaning on the user behavior data to acquire actual behavior data;
filling the obtained actual behavior data into different preference analysis models respectively based on preset preference rules and preference information and analyzing;
acquiring preference information of a target user in the preference analysis models and portraying the target user to obtain a user portrait of the target user;
and grading the target user based on the user portrait, and recommending corresponding content to the target user according to the grade and the user portrait.
In an embodiment, before the step of obtaining the user behavior data of the target user in the preset scene in real time, the user behavior analysis method further includes:
the preference rules are pre-stored in the user's access scenario to enable classification of the user behavior data according to the preference rules when it is obtained.
In an embodiment, before the step of obtaining the user behavior data of the target user at the Web port in real time, the user behavior analysis method further includes:
when a target user requests the Web server, the Web server transmits the HTML file corresponding to the page to the user, and records the request in a server log.
In an embodiment, the step of performing data cleansing on the user behavior data includes:
deleting the records except the access failure requested by the user;
and checking files with suffix names gif, jpeg, jpg, css and swf in the log files, and deleting corresponding log items.
In an embodiment, before the step of filling the obtained actual behavior data into different preference analysis models respectively based on preset preference rules and preference information and performing analysis, the user behavior analysis method further includes:
grouping user data;
and respectively carrying out time attenuation calculation on the grouped user data according to different attenuation factors to obtain the preference information of the user.
In an embodiment, the step of performing time attenuation calculation on the grouped user data according to different attenuation factors respectively to obtain the preference information of the user includes:
carrying out statistics on time and preferred word frequency on user behavior data of a target user;
and carrying out attenuation calculation of the time on the preferred word frequency to obtain data of the preferred word frequency of the user as a classification basis.
In an embodiment, the step of performing statistics of time and preferred word frequency on the user behavior data of the target user includes:
determining meal time periods of the user data, the meal time periods including a breakfast time period, a lunch time period, an afternoon tea time period, an evening time period, and an overnight time period;
and according to the meal time, carrying out statistics of the preferred word frequency on each group of data respectively.
In addition, to achieve the above object, the present invention also provides a user behavior analysis device, including:
the acquisition module is used for acquiring user behavior data of a plurality of target users under a Web port in real time;
the cleaning module is used for carrying out data cleaning on the user behavior data to acquire actual behavior data;
the analysis module is used for respectively filling the acquired actual behavior data into different preference analysis models based on preset preference rules and preference information and analyzing the actual behavior data;
the analysis module is also used for acquiring preference information of a target user in the plurality of preference analysis models and portraying the target user to obtain a user portrait of the target user;
and the scoring module is used for scoring the target user based on the user portrait and recommending corresponding content to the target user according to the score and the user portrait.
Furthermore, to achieve the above object, the present invention also provides a computer device, which includes a memory, a processor, and a turn signal control program stored on the memory and operable on the processor, wherein the turn signal control program, when executed by the processor, implements the steps of the user behavior analysis method as described above.
Further, to achieve the above object, the present invention also provides a computer-readable storage medium having stored thereon a turn signal lamp control program that, when executed by a processor, implements the steps of the user behavior analysis method as described above.
According to the user behavior analysis method, the user behavior analysis device, the computer equipment and the storage medium, the cleaned actual behavior data are filled into different preference analysis models based on the preset preference rules and the preference information, and specific analysis can be performed according to different preferences of the user, so that the user behavior analysis of the specific information is achieved. And moreover, the user portrait can be obtained by summarizing according to a plurality of preference analysis models, and corresponding contents can be recommended according to the portrait score of the user. Therefore, the accuracy of data processing can be improved, and fine and accurate pushing can be performed.
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FIG. 1 is a schematic diagram of an apparatus in a hardware operating environment according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating a user behavior analysis method according to a first embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, fig. 1 is a schematic device structure diagram of a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the terminal may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The processor 1001 may connect various parts of the entire computer device using various interfaces and lines, and perform various functions of the computer device and process data by running or executing software programs and/or modules stored in the memory 1005 and calling data stored in the memory 1005, thereby monitoring the computer device as a whole. A communication bus 1002 is used to enable connection communications between these components. The user interface 1003 may include a Display (Display), an input unit such as a Keyboard (Keyboard), keys, a touch panel, and the like, and the optional user interface 1003 may also include a standard wired interface, a wireless interface, and the like. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001.
Optionally, the terminal may further include a camera, a Radio Frequency (RF) circuit, a sensor, an audio circuit, a WiFi module, and the like. Of course, the hardware device may also be configured with other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, an infrared sensor, and so on, which are not described herein again.
Those skilled in the art will appreciate that the terminal structure shown in fig. 1 is not intended to be limiting and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a kind of computer storage medium, may include therein an operating system, a network communication module, a user interface module, and a turn signal control program.
In the terminal shown in fig. 1, the processor 1001 may be configured to call a turn signal control program stored in the memory 1005, and perform the following operations:
acquiring user behavior data of a plurality of target users under a Web port in real time;
performing data cleaning on the user behavior data to acquire actual behavior data;
filling the obtained actual behavior data into different preference analysis models respectively based on preset preference rules and preference information and analyzing;
acquiring preference information of a target user in the preference analysis models and portraying the target user to obtain a user portrait of the target user;
and grading the target user based on the user portrait, and recommending corresponding content to the target user according to the grade and the user portrait.
Further, the processor 1001 may call the turn light control program stored in the memory 1005, and also perform the following operations:
the preference rules are pre-stored in the user's access scenario to enable classification of the user behavior data according to the preference rules when it is obtained.
Further, the processor 1001 may call the turn light control program stored in the memory 1005, and also perform the following operations:
when a target user requests the Web server, the Web server transmits the HTML file corresponding to the page to the user, and records the request in a server log.
Further, the processor 1001 may call the turn light control program stored in the memory 1005, and also perform the following operations:
deleting the records except the access failure requested by the user;
and checking files with suffix names gif, jpeg, jpg, css and swf in the log files, and deleting corresponding log items.
Further, the processor 1001 may call the turn light control program stored in the memory 1005, and also perform the following operations:
grouping user data;
and respectively carrying out time attenuation calculation on the grouped user data according to different attenuation factors to obtain the preference information of the user.
Further, the processor 1001 may call the turn light control program stored in the memory 1005, and also perform the following operations:
carrying out statistics on time and preferred word frequency on user behavior data of a target user;
and carrying out attenuation calculation of the time on the preferred word frequency to obtain data of the preferred word frequency of the user as a classification basis.
Further, the processor 1001 may call the turn light control program stored in the memory 1005, and also perform the following operations:
determining meal time periods of the user data, the meal time periods including a breakfast time period, a lunch time period, an afternoon tea time period, an evening time period, and an overnight time period;
and according to the meal time, carrying out statistics on the preferred word frequency of each group of data.
The specific embodiment of the application computer device of the present invention is basically the same as the following embodiments of the application user behavior analysis method, and is not described herein again.
Referring to fig. 2, fig. 2 is a flowchart illustrating a user behavior analysis method according to a first embodiment of the present invention, wherein the user behavior analysis method includes the following steps:
step S10, acquiring user behavior data of a plurality of target users under the Web port in real time;
step S20, performing data cleaning on the user behavior data to acquire actual behavior data;
step S30, the obtained actual behavior data are respectively filled into different preference analysis models based on preset preference rules and preference information and are analyzed;
step S40, acquiring the preference information of the target user in the plurality of preference analysis models and portraying the target user to obtain the user portrayal of the target user;
and step S50, scoring the target user based on the user portrait, and recommending corresponding content to the target user according to the score and the user portrait.
In this embodiment, the cleaned actual behavior data is filled into different preference analysis models based on preset preference rules and preference information, so that specific analysis can be performed according to different preferences of the user, and the user behavior analysis of the specific information can be solved. And moreover, the user portrait can be obtained by summarizing according to a plurality of preference analysis models, and corresponding contents can be recommended according to the portrait score of the user. Therefore, the accuracy of data processing can be improved, and fine and accurate pushing can be performed.
Further, before the step of obtaining the user behavior data of the target user in the preset scene in real time, the user behavior analysis method further includes:
the preference rules are pre-stored in the user's access scenario to enable classification of the user behavior data according to the preference rules when it is obtained.
In this embodiment, the preference rules are pre-stored in the access scene of the user, so that when the user accesses, the acquired user behavior data can be directly classified through the preference rules after being cleaned.
Meanwhile, before the step of filling the obtained actual behavior data into different preference analysis models respectively based on the preset preference rules and the preference information and performing analysis, the user behavior analysis method further includes:
grouping user data;
and respectively carrying out time attenuation calculation on the grouped user data according to different attenuation factors to obtain the preference information of the user.
The grouping of user data may be based on the time data. For example, the time of week is divided into weekdays and non-weekdays, and the user data may be grouped by weekday and non-weekdays to obtain weekday user data and non-weekday user data.
In another specific implementation of the present invention, the time of one week may be divided into three times of monday to friday, saturday and sunday, and when grouping the user data, the user data is grouped according to three times of monday to friday, saturday and sunday to obtain the user data of monday to friday, the user data of saturday and the user data of sunday respectively.
By grouping the user data according to time, the data characteristics of the grouped user data are consistent, so that the subsequent data processing is more targeted, and the accurate determination of the data processing can be improved.
It is to be understood that the above grouping manner of grouping the user data is only an example, and in a specific implementation, the grouping of the user data may be performed in a manner of combining one or more of the time data and the geographic location data, or may also be performed in combination with other data, which is not limited herein.
In a specific implementation, after the user data is grouped, user data of a plurality of weeks within a certain time may be processed, for example, time attenuation calculation may be performed on user data of a plurality of weeks within a year according to different characteristics of the grouped data, so as to obtain preference information of the user.
Specifically, in one embodiment, the user behavior data of the target user is subjected to statistics of time and preferred word frequency;
and carrying out attenuation calculation of the time on the preferred word frequency to obtain data of the preferred word frequency of the user as a classification basis.
Further, determining meal time periods of the user data, the meal time periods including a breakfast time period, a lunch time period, an afternoon tea time period, an evening time period, and an overnight time period;
and according to the meal time, carrying out statistics on the preferred word frequency of each group of data.
The preference information of the user may include a user's order time, a type of food, taste, brand, etc. After classifying the different preference information, different portrayals can be performed by a plurality of preference analysis models.
For example, a breakfast time period may be 6 to 10, a lunch time period may be 10 to 14 pm, a afternoon tea time period may be 14 to 18, an evening time period may be 18 to 22, and an overnight time period may be 22 to 6 the next day. When it is analyzed that the user has more X1 brand points during the breakfast time period and more X2 brand points during the lunch time period, and the brand X1 and brand X2 belong to completely different categories, the recommendation can be made for the content related to the brand X1 during the breakfast time period, and the recommendation can be made for the content related to the brand X2 during the lunch time period.
In the prior art, users are generally qualified after analysis, and recommended contents are consistent in the whole using process of the users, so that the users cannot see the desired recommended contents in a corresponding time period. Compared with the prior art, different content recommendations can be performed on the user in different time periods, so that the purpose of accurate recommendation is achieved.
In the above embodiment, before the step of acquiring the user behavior data of the target user at the Web port in real time, the user behavior analysis method further includes:
when a target user requests the Web server, the Web server transmits the HTML file corresponding to the page to the user, and records the request in a server log.
The step of performing data cleansing on the user behavior data comprises:
deleting the records except the access failure requested by the user;
and checking files with suffix names gif, jpeg, jpg, css and swf in the log files, and deleting corresponding log items.
In this embodiment, invalid data is cleared, so that actual behavior data used as analysis can be quickly and accurately acquired, and the efficiency of data analysis is improved.
In addition, after the portrait of the user is obtained by gathering according to the plurality of preference analysis models and scored according to the portrait of the user, the content to be recommended, which is positioned in the scoring attachment of the user, is recommended according to the grade of the user, and corresponding recommendation is carried out according to the preference content analyzed by each preference analysis model of the user.
For example, if the score of the user is 4, the business sets with business scores of 3.8-4.2 are used as packages to be recommended for the user, and then pushing is performed respectively according to different preferences of the user in different time periods. So as to achieve the purpose of recommending different preferences on the basis of total portrait scoring.
In addition, the present invention also provides a computer-readable storage medium having a turn signal lamp control program stored thereon. The computer-readable storage medium may be the Memory 20 in the terminal in fig. 1, and may also be at least one of a ROM (Read-Only Memory)/RAM (Random Access Memory), a magnetic disk, and an optical disk, where the computer-readable storage medium includes several instructions to enable a terminal device (which may be a computer device, a smart television, a mobile phone, a computer, a server, or a network device) with a processor to execute the user behavior analysis method according to the embodiments of the present invention.
It is to be understood that throughout the description of the present specification, reference to the term "one embodiment", "another embodiment", "other embodiments", or "first through nth embodiments", etc., is intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A user behavior analysis method is characterized by comprising the following steps:
acquiring user behavior data of a plurality of target users under a Web port in real time;
performing data cleaning on the user behavior data to acquire actual behavior data;
filling the obtained actual behavior data into different preference analysis models respectively based on preset preference rules and preference information and analyzing;
acquiring preference information of a target user in the preference analysis models and portraying the target user to obtain a user portrait of the target user;
and grading the target user based on the user portrait, and recommending corresponding content to the target user according to the grade and the user portrait recommendation.
2. The user behavior analysis method according to claim 1, wherein before the step of acquiring the user behavior data of the target user in the preset scene in real time, the user behavior analysis method further comprises:
the preference rules are pre-stored in the user's access scenario to enable classification of the user behavior data according to the preference rules when it is obtained.
3. The user behavior analysis method according to claim 2, wherein before the step of obtaining the user behavior data of the target user under the Web portal in real time, the user behavior analysis method further comprises:
when a target user requests the Web server, the Web server transmits the HTML file corresponding to the page to the user, and records the request in a server log.
4. The user behavior analysis method of claim 3, wherein the step of data cleansing the user behavior data comprises:
deleting the records except the access failure requested by the user;
and checking files with suffix names gif, jpeg, jpg, css and swf in the log files, and deleting corresponding log items.
5. The user behavior analysis method according to claim 1, wherein before the step of filling the obtained actual behavior data into different preference analysis models based on preset preference rules and preference information and performing analysis, the user behavior analysis method further comprises:
grouping user data;
and respectively carrying out time attenuation calculation on the grouped user data according to different attenuation factors to obtain the preference information of the user.
6. The user behavior analysis method according to claim 5, wherein the step of performing time attenuation calculation on the grouped user data according to different attenuation factors to obtain the preference information of the user comprises:
carrying out statistics on time and preferred word frequency on user behavior data of a target user;
and carrying out attenuation calculation of the time on the preferred word frequency to obtain data of the preferred word frequency of the user as a classification basis.
7. The user behavior analysis method according to claim 1, wherein the step of performing statistics of time and preferred word frequency on the user behavior data of the target user comprises:
determining meal time periods of the user data, the meal time periods including a breakfast time period, a lunch time period, an afternoon tea time period, an evening time period, and an overnight time period;
and according to the meal time, carrying out statistics on the preferred word frequency of each group of data.
8. A user behavior analysis apparatus, characterized in that the apparatus comprises:
the acquisition module is used for acquiring user behavior data of a plurality of target users under a Web port in real time;
the cleaning module is used for carrying out data cleaning on the user behavior data to acquire actual behavior data;
the analysis module is used for respectively filling the acquired actual behavior data into different preference analysis models based on preset preference rules and preference information and analyzing the actual behavior data;
the analysis module is also used for acquiring preference information of a target user in the plurality of preference analysis models and portraying the target user to obtain a user portrait of the target user;
and the scoring module is used for scoring the target user based on the user portrait and recommending corresponding content to the target user according to the score and the user portrait.
9. A computer device comprising a memory, a processor, and a user behavior analysis program stored on the memory and executable on the processor, wherein: the user behavior analysis program when executed by the processor implementing the steps of the user behavior analysis method according to any one of claims 1 to 7.
10. A computer-readable storage medium, having a user behavior analysis program stored thereon, which, when executed by a processor, implements the steps of the user behavior analysis method according to any one of claims 1 to 7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116089712A (en) * | 2022-12-29 | 2023-05-09 | 无锡东方健康科技有限公司 | Hot conference recommending method and system based on data mining and analysis |
| CN117435449A (en) * | 2023-11-06 | 2024-01-23 | 广州丰石科技有限公司 | User portrait analysis method and device, electronic equipment and storage medium |
| CN117743848A (en) * | 2023-12-06 | 2024-03-22 | 暗物质(北京)智能科技有限公司 | User portrait generation method and device, electronic equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116089712A (en) * | 2022-12-29 | 2023-05-09 | 无锡东方健康科技有限公司 | Hot conference recommending method and system based on data mining and analysis |
| CN116089712B (en) * | 2022-12-29 | 2024-03-29 | 无锡东方健康科技有限公司 | Hot conference recommending method and system based on data mining and analysis |
| CN117435449A (en) * | 2023-11-06 | 2024-01-23 | 广州丰石科技有限公司 | User portrait analysis method and device, electronic equipment and storage medium |
| CN117743848A (en) * | 2023-12-06 | 2024-03-22 | 暗物质(北京)智能科技有限公司 | User portrait generation method and device, electronic equipment and storage medium |
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