A method and system for generating user portraits for a retail platform based on cloud computing

Abstract

The present invention discloses a method and system for generating a user portrait of a retail platform based on cloud computing. The present invention relates to the technical field of retail platforms and solves the problem that historical browsing information is not screened in detail in sequence, resulting in low accuracy of its user portrait and affecting the usage experience of the target user. The present invention locks the historical browsing information of the target user based on the confirmation of the target user, and then performs preliminary screening based on the specific characteristics of the historical browsing information, and then performs secondary screening. The screening method is inconsistent each time to determine the final retained real and valid information, and then determines the assigned value based on the specific parameters within the real and valid information. In order to make the generated user portrait more accurate, the assignment summation processing method is adopted to determine the sorting of the corresponding category, and then generate the user portrait to be pushed. The processing method is more accurate, more efficient, and more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of retail platforms, and in particular to a method and system for generating user portraits of retail platforms based on cloud computing. Background Art

[0002] A retail platform refers to an e-commerce platform used to sell products or services online. These platforms provide a convenient way for consumers to browse and purchase goods, while also providing sellers with an online sales channel. Common retail platforms include Amazon, Taobao, and JD.com. They provide transaction, payment, and logistics services to consumers and sellers, greatly facilitating e-commerce transactions.

[0003] Patent application number CN114943573A discloses a method, system, storage medium, and electronic device for constructing user portraits based on a retail SaaS platform. The method obtains product information and user information, profiles user attributes and behaviors, establishes a user feature matrix, establishes a product feature matrix, calculates product associations and user interest, generates a first recommendation list, calculates the similarity between the eigenvalues ​​in the product feature matrix of each product whose user interest ranking is within a threshold range and the eigenvalues ​​in the user feature matrix of the current user, sorts the products according to the similarity, and selects products whose similarity meets the threshold for display. The present invention performs multi-layer convolution operations on product associations and then calculates user interest, closely linking product associations with user interest, thereby improving the accuracy of user interest product searches.

[0004] When generating user portraits for corresponding users, retail platforms generally confirm the user habits of the corresponding users based on the corresponding users' usage records, and then generate user portraits for the corresponding users. However, this portrait generation method does not conduct detailed screening of historical browsing information in sequence, resulting in low accuracy of its user portraits, which affects the user experience of the target users. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a method and system for generating user portraits for a retail platform based on cloud computing, which solves the problem that historical browsing information is not screened in detail in sequence, resulting in low accuracy of user portraits and affecting the user experience of target users.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A retail platform user portrait generation system based on cloud computing, comprising:

[0007] The target user confirmation end determines the specific purchase amount of the corresponding user from the retail platform to confirm the target user;

[0008] The browsing information acquisition terminal directly extracts the target user's historical browsing information from the retail platform based on the confirmed target user, including the specific browsing time of the historical browsing information;

[0009] The information screening end performs a preliminary screening of the historical browsing information generated by the target user, removes invalid historical browsing information, and retains valid historical browsing information. The specific method is as follows:

[0010] Average the browsing time of several different historical browsing information groups, confirm that the average browsing time of several groups of historical browsing information is JS, and generate the browsing time interval of this target user [JS-X1, JS+X1], where X1 is ⅓ of JS;

[0011] Based on the browsing time of each group of browsing information, the browsing information belonging to the browsing time interval [JS-X1, JS+X1] is marked as valid historical browsing information, otherwise no marking is performed;

[0012] The information analysis terminal reanalyzes the valid historical browsing information and determines whether the corresponding browsing information is real valid information or assumed valid information by confirming the browsing speed. The specific method is as follows:

[0013] Obtain the browsing speed of each set of valid historical browsing information from the retail platform, and mark each set of browsing speed as SS i , where i represents different valid historical browsing information;

[0014] Set several groups of browsing speed SS i Perform clustering and sort the browsing speed groups from smallest to largest. Identify the middle value at the sorted position and mark it as the center value. If there are two groups of middle values, randomly select one of them.

[0015] Perform difference processing on different browsing speeds and the central value, then average several groups of differences to determine a group of averages, mark the valid historical browsing information with a difference greater than the average as presumed valid information, otherwise mark it as real valid information, and transmit the processed groups of real valid information to the assignment processing end;

[0016] The assignment processing end confirms the total browsing time and number of clicks of the corresponding information based on the real and valid information of the corresponding target user, and then locks the information feature value based on the two confirmed sets of parameters. The information feature value is then checked with the preset range to confirm the corresponding assignment of the real and valid information. The processing method is as follows:

[0017] The total browsing time of different real and effective information is calibrated as SC k , mark the number of clicks as DC k , where k represents different real and effective information;

[0018] Using TZ k =SC k ×C1+DC k ×C2 determines the corresponding information characteristic value TZ k , where C1 and C2 are preset fixed coefficient factors, the determined information characteristic value TZ k Compare with the preset intervals respectively, where the preset intervals include three groups: 0 interval [Y1, Y2], 1 interval (Y2, Y3], 2 interval (Y3, Y4], where Y1, Y2, Y3 and Y4 are all preset interval end values;

[0019] Confirm information characteristic value TZ k To which group of intervals it belongs, the corresponding interval named value is directly selected to assign the real and valid information, and the real and valid information after the assignment is completed is transmitted to the portrait generation end. If the corresponding interval is not found, no assignment is performed;

[0020] The profile generation end receives the real and valid information after the assignment process, analyzes and compares it with the sales categories of the retail platform, confirms the specific attribution of the real and valid information, and then generates a user profile for this target user based on the assignment. The specific process is as follows:

[0021] Identify the multiple major categories divided by the retail platform, and then determine the group of major categories with the largest number of subordinate categories based on the real and valid information;

[0022] Then, determine several corresponding subcategories within this large category, and then confirm the subcategory based on the specific IP ownership of real and valid information;

[0023] The values ​​of several real and effective information in each different sub-category are summed up, and the specific value obtained by the sum is calibrated as the ranking value of the corresponding sub-category;

[0024] Based on the numerical size of the sorting value, several small categories are sorted from large to small, and the top three small categories are extracted and three groups of user portraits are generated. The three groups of user portraits generated are transmitted to the push center.

[0025] Preferably, the push center includes a time period parameter acquisition unit, a parameter analysis unit and a push unit;

[0026] The time period parameter acquisition unit acquires different time periods of the target user using the retail platform and transmits the acquired different time periods to the parameter analysis unit;

[0027] The parameter analysis unit performs a comprehensive ratio analysis on the different time periods obtained and the preset pushable time periods, selects the maximum ratio, and thus determines the optimal push time period. The determination method is:

[0028] According to the rest time in the morning, noon and evening, three groups of push time periods are determined. The push time periods are all planned in advance and operated by the operators themselves;

[0029] Based on the different time periods obtained, determine the total usage time of the target users corresponding to each group of different pushable time periods, and then determine the usage percentage, where: usage percentage = total usage time ÷ total time of pushable time periods;

[0030] Determine the maximum ratio among the three groups of usage ratios, and mark the push time period to which this maximum ratio belongs as the optimal push time period;

[0031] The push unit randomly pushes the three groups of user portraits to the corresponding target user terminals during the optimal push time period and when the target users are using the retail platform.

[0032] Preferably, a method for generating a user portrait of a retail platform based on cloud computing includes:

[0033] Determine target users based on the specific purchase volume of registered users;

[0034] Extract historical browsing information generated by target users from the retail platform and ensure that the historical browsing information meets the standards;

[0035] Perform a preliminary screening of historical browsing information, eliminate invalid historical browsing information by confirming the average browsing time, and retain valid historical browsing information;

[0036] Cluster the viewing speed of valid historical browsing information, remove valid historical browsing information with large cluster deviation, and retain the real and valid information;

[0037] Based on the total browsing time and number of clicks of the real and valid information, the information characteristic value is determined, and based on this information characteristic value, the specific value assigned to the real and valid information is determined;

[0038] Based on the specific assignment and classification of different real and valid information, the specific ownership of the real and valid information is confirmed and a user profile of the corresponding target user is generated.

[0039] The present invention provides a method and system for generating user profiles for a retail platform based on cloud computing. Compared with the existing technology, it has the following advantages:

[0040] The present invention locks the historical browsing information of the target user based on the confirmation of the target user, and then performs preliminary screening based on the specific characteristics of the historical browsing information, and then performs secondary screening. The screening method is different each time to determine the final retained real and valid information. Then, according to the specific parameters within the real and valid information, the assigned value is determined. In order to make the generated user portrait more accurate, the assignment summation processing method is used to determine the ranking of the corresponding category, and then the user portrait to be pushed is generated. The processing method is more accurate, more efficient, and more accurate.

[0041] Confirm the corresponding rest period of the target user, and then determine the optimal duration of the usage process based on the specific duration of the corresponding user using the corresponding platform during the corresponding rest period, so as to determine a time period with the longest usage time. Push within this time period will have a better overall push effect and can be pushed to the corresponding target user in a more timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a schematic diagram of the principle framework of the present invention;

[0043] Figure 2 This is a schematic diagram of the internal principles of the push center of the present invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0045] Example 1

[0046] See also Figure 1 , this application provides a retail platform user portrait generation system based on cloud computing, including a target user confirmation end, a browsing information acquisition end, an information screening end, an information analysis end, an assignment processing end, a portrait generation end and a push center;

[0047] The target user confirmation end, the browsing information acquisition end, the information screening end, the assignment processing end, the portrait generation end, and the push center are electrically connected in sequence from the output node to the input node, and the information analysis end and the information screening end are bidirectionally connected;

[0048] The target user confirmation terminal determines the specific purchase amount of the corresponding user from the retail platform. When the corresponding user has purchased more than ten times on this retail platform, the corresponding user is marked as a target user and the marked target user is transmitted to the browsing information acquisition terminal. Specifically, the target user is a user who has completed registration on the retail platform, and the purchase information of the registered users can be directly obtained from the retail platform.

[0049] The browsing information acquisition terminal directly extracts the target user's historical browsing information from the retail platform based on the confirmed target user, including the specific browsing time of the historical browsing information:

[0050] When the target user's historical browsing information obtained does not exceed 30 groups, the obtained historical browsing information will be transmitted to the information screening end. If it does not exceed 30 groups, the monitoring will continue until the historical browsing information obtained exceeds 30 groups and then it will be transmitted. For example: when the historical browsing information obtained is not enough, the user screen analyzed will not be standard enough and the error will be large, so it is necessary to ensure the total amount of information.

[0051] The information screening end performs a preliminary screening of the historical browsing information generated by the target user, removes invalid historical browsing information, and retains valid historical browsing information. The valid historical browsing information is then re-analyzed by the information analysis end, and the browsing speed is confirmed to determine whether the corresponding browsing information is real and valid information or assumed to be valid information. The specific method for preliminary screening of historical browsing information is as follows:

[0052] The browsing time of several groups of different historical browsing information is averaged to determine the average browsing time of several groups of historical browsing information is JS, and the browsing time interval of this target user is generated [JS-X1, JS+X1], where X1 is a preset value and is generally set to ⅓ of JS. The specific value is determined by the operator based on experience;

[0053] Based on the browsing time of each group of browsing information, the browsing information belonging to the browsing time interval [JS-X1, JS+X1] is marked as valid historical browsing information, otherwise no marking is performed;

[0054] The specific method for reanalyzing valid historical browsing information on the information analysis side is as follows:

[0055] Obtain the browsing speed of each set of valid historical browsing information from the retail platform, and mark each set of browsing speed as SS i , where i represents different valid historical browsing information;

[0056] Set several groups of browsing speed SS iPerform clustering processing and sort several groups of browsing speeds from smallest to largest. Determine the middle value at the sorted position and mark it as the center value. If there are two groups of middle values, randomly select one of them. For example, if the browsing speed is 1, 2, 3, 4, 4, 5, then the middle value is 3 or 4, and the center value is one of 3 or 4. If the browsing speed is 1, 2, 3, 4, 5, then the middle value is 3, and the center value is 3.

[0057] Perform difference processing on different browsing speeds and the central value, and then perform average processing on several groups of differences to determine a group of means. The valid historical browsing information with a difference greater than the mean is marked as assumed valid information. Otherwise, it is marked as real valid information, and the processed groups of real valid information are transmitted to the assignment processing end. Specifically, the so-called assumed valid information generates corresponding historical browsing information when the corresponding interface is opened, but no one views it, and it is closed after reaching the specified time. This type of historical browsing information should be eliminated, which will affect the subsequent confirmation of the corresponding target user portrait.

[0058] The assignment processing end confirms the total browsing time and number of clicks of the corresponding information based on the real and valid information of the corresponding target user, and then locks the information feature value based on the two confirmed sets of parameters. The information feature value is then checked with the preset range to confirm the corresponding assignment of the real and valid information and perform the assignment processing. The specific processing method is as follows:

[0059] The total browsing time of different real and effective information is calibrated as SC k , mark the number of clicks as DC k , where k represents different real and effective information;

[0060] Using TZ k =SC k ×C1+DC k ×C2 determines the corresponding information characteristic value TZ k , where C1 and C2 are preset fixed coefficient factors, and their specific values ​​are determined by the operator based on experience. k Compare with the preset intervals respectively, where the preset intervals include three groups: 0 interval [Y1, Y2], 1 interval (Y2, Y3], 2 interval (Y3, Y4], where Y1, Y2, Y3 and Y4 are all preset interval end values, which are formulated by the operator based on experience;

[0061] Confirm information characteristic value TZ kTo determine which group of intervals the information belongs to, the corresponding interval named value is directly selected to assign a value to the real and valid information, and the real and valid information after the assignment is completed is transmitted to the portrait generation end. If the interval is not found, no assignment is performed, which may cause data anomalies due to accidental touches.

[0062] Example: Different real and valid information has different total browsing time and number of clicks. Excluding accidental touches, when the total browsing time and number of clicks of the corresponding information are valid values, then the larger the value, the longer this type of information is viewed, and the corresponding information feature value generated is also larger, so the assigned value is large, that is, "2". Conversely, when the total browsing time and number of clicks are smaller, the assigned value is smaller, that is, "0".

[0063] The profile generation end receives the real and valid information after the assignment process, analyzes and compares it with the sales categories of the retail platform, confirms the specific attribution of the real and valid information, and then generates a user profile for the target user based on the assignment. The specific generation process is as follows:

[0064] Identify the multiple major categories divided by the retail platform, and then determine the group of major categories with the largest number of subordinate categories based on the real and valid information;

[0065] Then, determine several corresponding subcategories within this large category, and then confirm the subcategory based on the specific IP ownership of real and valid information;

[0066] The values ​​of several real and effective information in each different sub-category are summed up, and the specific value obtained by the sum is calibrated as the ranking value of the corresponding sub-category;

[0067] Based on the numerical values ​​of the ranking values, several small categories are sorted from large to small, and the top three small categories are extracted and three sets of user portraits are generated. The three sets of user portraits generated are transmitted to the push center;

[0068] For example, the major category is pants, but the minor categories are different styles of pants, such as shorts, trousers, jeans, etc., all of which can be selected and confirmed in the retail platform. The retail platform has specific divisions. In order to make the generated user portrait more accurate, the assignment and summation processing method is used to determine the sorting of the corresponding categories, and then the user portrait to be pushed is generated. The processing method is more accurate, efficient, and precise.

[0069] Combine Figure 2 The push center includes a time period parameter acquisition unit, a parameter analysis unit, and a push unit. The main function of the push center is to randomly push the three groups of user portraits to the target user's terminal at the appropriate time;

[0070] The time period parameter acquisition unit acquires different time periods during which the target user uses the retail platform, and transmits the acquired different time periods to the parameter analysis unit;

[0071] The parameter analysis unit performs a comprehensive ratio analysis on the different time periods obtained and the preset pushable time periods, selects the maximum ratio, and thus determines the optimal push time period. The determination method is as follows:

[0072] According to the rest time in the morning, noon and evening, three groups of push time periods are determined. The push time periods are all planned in advance and operated by the operators themselves;

[0073] Based on the different time periods obtained, determine the total usage time of the target users corresponding to each group of different pushable time periods, and then determine the usage percentage, where: usage percentage = total usage time ÷ total time of pushable time periods;

[0074] From the three groups of usage ratio values, the maximum ratio is determined, and the push time period to which the maximum ratio belongs is marked as the optimal push time period.

[0075] The push unit randomly pushes three groups of user portraits to the corresponding target user terminals during the optimal push time period and when the target users are using the retail platform.

[0076] Specifically, in order to ensure that the pushed user portrait can be pushed to the target user more appropriately, we must first confirm the corresponding rest period of the target user, and then determine the optimal duration of the usage process based on the specific duration of the corresponding user using the corresponding platform during the corresponding rest period, thereby determining a time period with the longest usage time. Pushing within this time period will have a better overall push effect, and can be pushed to the corresponding target user more timely, and the corresponding target user will also have sufficient time to view such information and place an order.

[0077] Example 2

[0078] In the specific implementation process of this embodiment, compared with the first embodiment, this embodiment mainly operates on the method for generating user portraits;

[0079] A method for generating user portraits for a retail platform based on cloud computing, comprising:

[0080] Determine target users based on the specific purchase volume of registered users;

[0081] Extract historical browsing information generated by target users from the retail platform and ensure that the historical browsing information meets the standards;

[0082] Perform a preliminary screening of historical browsing information, eliminate invalid historical browsing information by confirming the average browsing time, and retain valid historical browsing information;

[0083] Cluster the viewing speed of valid historical browsing information, remove valid historical browsing information with large cluster deviation, and retain the real and valid information;

[0084] Based on the total browsing time and number of clicks of the real and valid information, the information characteristic value is determined, and based on this information characteristic value, the specific value assigned to the real and valid information is determined;

[0085] Based on the specific assignment and classification of different real and valid information, the specific ownership of the real and valid information is confirmed and a user profile of the corresponding target user is generated.

[0086] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0087] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A retail platform user portrait generation system based on cloud computing, characterized in that: include: The target user confirmation end determines the specific purchase amount of the corresponding user from the retail platform to confirm the target user; The browsing information acquisition terminal directly extracts the target user's historical browsing information from the retail platform based on the confirmed target user, including the specific browsing time of the historical browsing information; The information screening end performs preliminary screening on the historical browsing information generated by the target user, removes invalid historical browsing information, and retains valid historical browsing information; The information analysis terminal re-analyzes the valid historical browsing information and determines whether the corresponding browsing information is real valid information or assumed valid information by confirming the browsing speed; The assignment processing end confirms the total browsing time and number of clicks of the corresponding information based on the real and valid information of the corresponding target user, and then locks the information feature value based on the two confirmed sets of parameters. The information feature value is then checked with the preset range to confirm the corresponding assignment of the real and valid information. The specific processing method is as follows: The total browsing time of different real and effective information is calibrated as SC k , mark the number of clicks as DC k , where k represents different real and effective information; Using TZ k =SC k ×C1+DC k ×C2 determines the corresponding information characteristic value TZ k , where C1 and C2 are preset fixed coefficient factors, the determined information characteristic value TZ k Compare with the preset intervals respectively, where the preset intervals include three groups: 0 interval [Y1, Y2], 1 interval (Y2, Y3], 2 interval (Y3, Y4], where Y1, Y2, Y3 and Y4 are all preset interval end values; Confirm information characteristic value TZ k To which group of intervals it belongs, the corresponding interval named value is directly selected to assign the real and valid information, and the real and valid information after the assignment is completed is transmitted to the portrait generation end. If the corresponding interval is not found, no assignment is performed; The portrait generation end receives the real and valid information after the assignment process, analyzes and compares it with the sales categories of the retail platform, confirms the specific ownership of the real and valid information, and then generates a user portrait for this target user based on the assignment: Identify the multiple major categories divided by the retail platform, and then determine the group of major categories with the largest number of subordinate categories based on the real and valid information; Then, determine several corresponding subcategories within this large category, and then confirm the subcategory based on the specific IP ownership of real and valid information; The values ​​of several real and effective information in each different sub-category are summed up, and the specific value obtained by the sum is calibrated as the ranking value of the corresponding sub-category; Based on the numerical size of the sorting value, several small categories are sorted from large to small, and the top three small categories are extracted and three groups of user portraits are generated. The three groups of user portraits generated are transmitted to the push center.

2. A retail platform user portrait generation system based on cloud computing according to claim 1, characterized in that: The target user confirmation terminal shown will mark the corresponding user as a target user when the corresponding user has made purchases more than ten times on this retail platform.

3. The cloud computing-based retail platform user portrait generation system according to claim 1, characterized in that: The browsing information acquisition end transmits all the acquired historical browsing information to the information screening end when the acquired historical browsing information of the target user does not exceed 30 groups. If it does not exceed 30 groups, the end continues to monitor until the acquired historical browsing information exceeds 30 groups and then transmits it.

4. The cloud computing-based retail platform user portrait generation system according to claim 1, characterized in that: The specific method of the information screening terminal for preliminary screening of historical browsing information is as follows: Average the browsing time of several different historical browsing information groups, confirm that the average browsing time of several groups of historical browsing information is JS, and generate the browsing time interval of this target user [JS-X1, JS+X1], where X1 is ⅓ of JS; Based on the browsing time of each group of browsing information, the browsing information belonging to this browsing time interval [JS-X1, JS+X1] is marked as valid historical browsing information, otherwise no marking is performed.

5. The cloud computing-based retail platform user portrait generation system according to claim 1, characterized in that: The specific method by which the information analysis terminal analyzes the valid historical browsing information is as follows: Obtain the browsing speed of each set of valid historical browsing information from the retail platform, and mark each set of browsing speed as SS i , where i represents different valid historical browsing information; Set several groups of browsing speed SS i Perform clustering processing and sort several groups of browsing speeds from smallest to largest. Determine the middle value at the sorted position and mark it as the center value. If there are two groups of middle values, randomly select one group of values. Different browsing speeds are processed with the center value, and then several groups of differences are averaged to determine a group of means. The valid historical browsing information with a difference greater than the mean is marked as assumed valid information. Otherwise, it is marked as real valid information, and the processed groups of real valid information are transmitted to the assignment processing end.

6. The cloud computing-based retail platform user portrait generation system according to claim 1, characterized in that: The push center includes a time period parameter acquisition unit, a parameter analysis unit and a push unit; The time period parameter acquisition unit acquires different time periods of the target user using the retail platform and transmits the acquired different time periods to the parameter analysis unit; The parameter analysis unit performs a comprehensive ratio analysis on the different time periods obtained and the preset pushable time periods, selects the maximum ratio, and thus determines the optimal push time period.

7. The cloud computing-based retail platform user portrait generation system according to claim 6, characterized in that: The parameter analysis unit determines the optimal push time in the following manner: According to the rest time in the morning, noon and evening, three groups of push time periods are determined. The push time periods are all planned in advance and operated by the operators themselves; Based on the different time periods obtained, determine the total usage time of the target users corresponding to each group of different pushable time periods, and then determine the usage percentage, where: usage percentage = total usage time ÷ total time of pushable time periods; Determine the maximum ratio among the three groups of usage ratios, and mark the push time period to which this maximum ratio belongs as the optimal push time period; The push unit randomly pushes the three groups of user portraits to the corresponding target user terminals during the optimal push time period and when the target users are using the retail platform.

8. A method for generating a user portrait of a retail platform based on cloud computing, the method being applied to the user portrait generation system according to any one of claims 1 to 7, characterized in that: include: Determine target users based on the specific purchase volume of registered users; Extract historical browsing information generated by target users from the retail platform and ensure that the historical browsing information meets the standards; Perform a preliminary screening of historical browsing information, eliminate invalid historical browsing information by confirming the average browsing time, and retain valid historical browsing information; Cluster the viewing speed of valid historical browsing information, remove valid historical browsing information with large cluster deviation, and retain the real and valid information; Based on the total browsing time and number of clicks of the real and valid information, the information characteristic value is determined, and based on this information characteristic value, the specific value assigned to the real and valid information is determined; Based on the specific assignment and classification of different real and valid information, the specific ownership of the real and valid information is confirmed and a user profile of the corresponding target user is generated.

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