Back-end implementation method and system for providing user identifier association
By constructing a relationship diagram based on behavioral data and using smart ID to push information, the problems of insufficient user identification association capabilities and push information processing efficiency in the prior art are solved, and more efficient user identification association and information push are achieved.
Patent Information
- Application Number
- CN202411929116.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-25
- Publication Date
- 2025-04-22
AI Technical Summary
The existing data pulling technology has shortcomings in terms of association capabilities and push information processing efficiency, making it difficult to achieve accurate user identification association and efficient information push.
Construct a relationship diagram through behavioral data, determine the interaction relationship between user IDs, and perform unified information push based on smart IDs, improving the pull-through association capability and the processing efficiency of pushing information.
It improves the ability and accuracy of user identification association, improves the efficiency of push information processing, and ensures more efficient marketing push and user experience.
Smart Images
Figure CN119862323A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of data management, and in particular to a back-end implementation method and system for providing user identification association. Background Art
[0002] ID integration technology, also known as identity integration technology, plays a pivotal role in the field of marketing push. It provides strong technical support for precision marketing by uniformly managing and identifying user identity information scattered across different platforms, systems or applications.
[0003] As digital marketing becomes increasingly popular, companies are faced with the challenge of how to effectively reach target users and improve marketing effectiveness. ID Pull-through technology solves this problem. It breaks down information silos, allowing companies to integrate user data across platforms and systems to form a comprehensive user portrait. This includes not only basic user information such as age, gender, and region, but also dynamic data such as user consumption habits, interests, and browsing history. Based on these rich user portraits, companies can implement more accurate marketing push. For example, by analyzing users' purchase history and browsing behavior, companies can predict users' potential needs and push corresponding product or service information. This personalized push method not only improves the reach of marketing information, but also significantly enhances users' willingness to buy and satisfaction.
[0004] However, existing data pulling technology often requires strong association through information such as ID cards or phone numbers, and has poor pulling and association capabilities, resulting in low processing efficiency of pushed information.
[0005] In view of this, the present invention is proposed. Summary of the invention
[0006] The purpose of the present invention is to provide a back-end implementation method and system for providing user identification association. This scheme determines the user ID that has an interactive relationship with the user ID through behavioral data, constructs an association graph based on the behavioral data, further constructs associations based on the association graph, and performs unified information push based on a smart ID, thereby improving the ability to pull through associations and improving the processing efficiency of pushed information.
[0007] The present invention provides a back-end implementation method for providing user identification association, the method comprising the steps of:
[0008] Collect user data from various business channels, the user data including user ID, behavior data and attribute data;
[0009] Preliminarily construct an ID mapping relationship based on the user data, construct the user IDs with the mapping relationship into a mapping ID set, and provide an identification ID for the user ID in the mapping ID set;
[0010] Determine a user ID that has an interactive relationship with the user ID based on the behavior data, take an identification ID of a user ID as a reference node, take an identification ID corresponding to a user ID that has an interactive relationship with the user ID corresponding to the identification ID as an associated node, and construct an associated graph;
[0011] Determine the correlation between association graphs, and construct smart IDs for the relevant association graph identification IDs;
[0012] Unified information push is carried out to users with the same smart ID.
[0013] Adopting the above scheme, this scheme first preliminarily constructs an ID mapping relationship based on the user data. The preliminarily constructed ID mapping relationship is based on a strong ID, that is, information such as a telephone number or an email address, to construct a mapping relationship, and constructs a mapping relationship for these user IDs. Furthermore, this scheme determines the user ID that has an interactive relationship with the user ID through behavioral data, and constructs an association graph based on the behavioral data. Due to the principle that accounts operated by the same user often have the same associations, such as the principle that the QQ and WeChat friends of the same user often have high similarities, associations are further constructed based on the association graph, and unified information push is performed based on a smart ID, thereby improving the ability to pull through associations and improving the processing efficiency of pushed information.
[0014] In some embodiments of the present invention, in the step of preliminarily constructing an ID mapping relationship based on the user data, constructing the user IDs with the mapping relationship into a mapping ID set, and providing an identification ID for the user IDs in the mapping ID set;
[0015] Obtain strong ID data for each user ID, and establish a mapping relationship between user IDs based on the strong ID data;
[0016] And construct multiple user IDs with mapping relationships into a mapping ID set.
[0017] In some embodiments of the present invention, the strong ID data includes a telephone number or an email address, and in the step of establishing a mapping relationship between user IDs based on the strong ID data, a mapping relationship is established between user IDs having the same strong ID data.
[0018] By adopting the above solution, accounts opened by the same user on different platforms can be locked through a strong ID, and a mapping relationship between these accounts can be established.
[0019] In some embodiments of the present invention, in the step of determining a user ID that has an interactive relationship with the user ID based on the behavior data, the user ID of the target of the behavior corresponding to the behavior data of the user ID is determined based on the behavior data, and the identification ID of the target is determined based on the user ID of the target.
[0020] In some embodiments of the present invention, in the step of determining the user ID of the target of the behavior corresponding to the behavior data of the user ID based on the behavior data, and determining the identification ID of the target based on the user ID of the target, the corresponding behavior type is determined based on the behavior data.
[0021] In some embodiments of the present invention, in the step of constructing an association graph by taking the identification ID of a user ID as a base node and the identification ID corresponding to the user ID that has an interactive relationship with the identification ID as an associated node, each identification ID is constructed as a node, and in an association graph, the node corresponding to the identification ID of a user ID is taken as a base node, the node corresponding to the identification ID that has an interactive relationship with the identification ID is taken as an associated node, and edges are constructed between the base nodes and the associated nodes.
[0022] By adopting the above scheme, the scheme represents the identification IDs associated with each identification ID in the identification ID association graph, thereby ensuring the statistical completeness of a single identification ID.
[0023] In some embodiments of the present invention, in the step of taking the node corresponding to the identification ID that has an interactive relationship with the identification ID as an associated node and constructing an edge between the base node and the associated node, the behavior type corresponding to the behavior data is used as the type of the edge. If there are multiple behavior types between the base node and the associated node, multiple edges corresponding to the multiple behavior types are constructed to obtain the association graph.
[0024] By adopting the above scheme, if there are multiple behavior types between the reference node and the associated node, multiple edges corresponding to the multiple behavior types are constructed, which can represent all the behavior types between the two nodes.
[0025] In some embodiments of the present invention, in the step of determining the correlation between association graphs and constructing a smart ID for the relevant association graph identifier ID:
[0026] Each association graph is used as a starting analysis association graph, and the types and numbers of edges between the reference node and each association node in each association graph are counted;
[0027] Then count whether there are associated nodes with the same edge type and number in the associated graph taking the associated node as the base node, and use the associated graph as the associated graph for the association analysis;
[0028] If so, the two association graphs are related association graphs, and the association nodes with the same edge type and number existing in the association graph with the association node as the base node and the two identification IDs of the base node in the original association graph are constructed into a smart ID.
[0029] The above scheme is adopted. This scheme is based on the principle that accounts operated by the same user often have the same associations, such as the principle that QQ and WeChat friends of the same user often have high similarities. The corresponding accounts can be further constructed into a smart ID; moreover, in this scheme, although the same behavior type does not necessarily point to the same user, it is sufficient to illustrate the similarity of the two accounts. In subsequent schemes, this scheme will uniformly push accounts with the same smart ID, which can at least ensure corresponding pushes for similar accounts, thereby improving push efficiency.
[0030] In some embodiments of the present invention, in the step of constructing two identification IDs of the associated nodes with the same edge type and number existing in the associated graph with the associated node as the reference node and the reference node in the original associated graph into one smart ID, the associated nodes with the same edge type and number existing in the associated graph with the reference node in the original associated graph are used as the reference node;
[0031] The maximum number of interactions between the benchmark node and the associated nodes in the starting analysis association graph, as well as the maximum number of interactions between the benchmark node and the associated nodes with the same edge type and number in the associated analysis association graph are counted. If the two interaction relationships are the same, the identification IDs of the benchmark node in the starting analysis association graph and the corresponding associated nodes in the associated analysis association graph are constructed into a smart ID.
[0032] Using the above scheme, this scheme further ensures the similarity of the accounts under the smart ID and the accuracy of subsequent push by statistically analyzing the maximum number of interactions between the reference node and the associated node in the association graph.
[0033] Another aspect of the present invention also relates to a back-end implementation system for providing user identification association, the system comprising a computer device, the computer device comprising a processor and a memory, the memory storing computer instructions, the processor being used to execute the computer instructions stored in the memory, and when the computer instructions are executed by the processor, the system implements the steps implemented by the method.
[0034] In summary, the present invention has the following beneficial effects:
[0035] 1. This solution first preliminarily constructs an ID mapping relationship based on the user data. The preliminarily constructed ID mapping relationship is based on a strong ID, that is, information such as a phone number or an email address, and a mapping relationship is constructed for these user IDs. Further, this solution determines the user ID that has an interactive relationship with the user ID through behavioral data, and constructs an association graph based on behavioral data. Due to the principle that accounts operated by the same user often have the same association, such as the principle that QQ and WeChat friends of the same user often have a high similarity, further associations are constructed based on the association graph, and unified information push is performed based on a smart ID, thereby improving the ability to pull through associations and improving the processing efficiency of pushed information;
[0036] 2. This solution can lock accounts opened by the same user on different platforms through strong IDs and establish a mapping relationship between these accounts;
[0037] 3. In this solution, each ID association graph represents the IDs associated with the ID, ensuring the statistical completeness of a single ID.
[0038] 4. This solution is based on the principle that accounts operated by the same user often have the same associations, just as the QQ and WeChat friends of the same user often have high similarities. The corresponding accounts can be further constructed into a smartID. Moreover, in this solution, although the same behavior type does not necessarily point to the same user, it is sufficient to illustrate the similarity of the two accounts. In subsequent solutions, this solution will uniformly push accounts with the same smart ID, which can at least ensure corresponding pushes for similar accounts and improve push efficiency. BRIEF DESCRIPTION OF THE DRAWINGS
[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0040] Figure 1 A schematic diagram of a first implementation method of a backend implementation method for user identification association is provided for the present invention;
[0041] Figure 2 A schematic diagram of a second implementation method of a backend implementation method for user identification association is provided for the present invention;
[0042] Figure 3 Schematic diagram of the processing architecture of this solution. DETAILED DESCRIPTION
[0043] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.
[0044] The terms used in the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "the" and "the" used in the present invention and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.
[0045] The functions of ID pull-through technology include:
[0046] 1. Data silo problem: The same user has multiple identities in the system, such as mobile phone number, WeChat user ID, Alipay user ID, software application user ID, bank payment user ID and other different coded IDs. These different IDs cause user information to be scattered in different database tables, forming data silos. ID pull-through technology breaks the data silos and realizes the comprehensive integration of user information by unifying these scattered IDs into one pulled-through user coded ID.
[0047] 2. Data inconsistency: Since multiple IDs correspond to the same user, the user information under each ID may not be completely consistent. For example, attribute information such as name, gender, and age may be different. ID Pull-through technology can eliminate these inconsistencies and improve data accuracy and consistency by integrating and associating the information under these IDs.
[0048] 3. Difficulty of data analysis: When multiple IDs exist, data analysis of user behavior and attributes becomes very complicated because multiple IDs need to be mapped to the same user before accurate analysis can be performed. ID pull-through technology simplifies this process, making data analysis easier and more accurate.
[0049] 4. Business application barriers: In business applications, such as precision marketing, discount push, user portrait construction, etc., it is necessary to fully understand the user's information and behavior. If user information is scattered under multiple IDs, these business applications will be difficult to implement. ID pull-through technology provides a solid data foundation for these business applications by unifying user information.
[0050] like Figure 1 and 3As shown, the present invention provides a back-end implementation method for providing user identification association, and the steps of the method include:
[0051] Step S100, collecting user data from various business channels, the user data including user ID, behavior data and attribute data;
[0052] In the specific implementation process, in the step of collecting user data from various business channels, user data is collected from various business channels (such as POS systems, application APP systems, membership systems, payment channels, etc.), including user ID, behavior data and attribute data, etc. The attribute data includes the user's registration place and gender, etc., which can ensure the completeness and accuracy of the collected data for subsequent processing and analysis.
[0053] Specifically, the steps of this solution also include cleaning the collected data to remove duplicate, invalid or abnormal data; standardizing the data, such as unifying the data format, data unit, etc.; extracting key information, such as user ID, behavior, attributes, etc.
[0054] Step S200, preliminarily constructing an ID mapping relationship based on the user data, constructing the user IDs with the mapping relationship into a mapping ID set, and providing an identification ID for the user ID in the mapping ID set;
[0055] Step S300, determining a user ID that has an interactive relationship with a user ID based on the behavior data, taking an identification ID of a user ID as a reference node, taking an identification ID corresponding to a user ID that has an interactive relationship with the user ID corresponding to the identification ID as an associated node, and constructing an associated graph;
[0056] Step S400, determining the correlation between the association graphs, and constructing a smart ID for the relevant association graph identification ID;
[0057] Step S500: uniformly push information to users with the same smart ID.
[0058] In a specific implementation process, the information pushed may be product push information.
[0059] During the specific implementation process, encryption algorithms such as MD5 are used to ensure the privacy and security of user IDs during the pull-through process and prevent the leakage of sensitive data.
[0060] Adopting the above scheme, this scheme first preliminarily constructs an ID mapping relationship based on the user data. The preliminarily constructed ID mapping relationship is based on a strong ID, that is, information such as a telephone number or an email address, to construct a mapping relationship, and constructs a mapping relationship for these user IDs. Furthermore, this scheme determines the user ID that has an interactive relationship with the user ID through behavioral data, and constructs an association graph based on the behavioral data. Due to the principle that accounts operated by the same user often have the same associations, such as the principle that the QQ and WeChat friends of the same user often have high similarities, associations are further constructed based on the association graph, and unified information push is performed based on a smart ID, thereby improving the ability to pull through associations and improving the processing efficiency of pushed information.
[0061] like Figure 2 As shown, in some embodiments of the present invention, in the step of preliminarily constructing an ID mapping relationship based on the user data, constructing the user IDs with the mapping relationship into a mapping ID set, and providing an identification ID for the user IDs in the mapping ID set, it includes:
[0062] Step S210, obtaining strong ID data of each user ID, and establishing a mapping relationship between user IDs based on the strong ID data;
[0063] Step S220, multiple user IDs having a mapping relationship are constructed into a mapping ID set.
[0064] In some embodiments of the present invention, the strong ID data includes a telephone number or an email address, and in the step of establishing a mapping relationship between user IDs based on the strong ID data, a mapping relationship is established between user IDs having the same strong ID data.
[0065] In the specific implementation process, the same user has multiple identities in the system, such as mobile phone number, WeChat user ID, Alipay user ID, software application user ID, bank payment user ID and other different coded IDs. These different IDs cause user information to be scattered in different database tables, forming data islands. The ID pull-through technology of this solution unifies these scattered IDs into a pulled-through identification ID through strong ID data, breaking the data islands and realizing the comprehensive integration of user information.
[0066] By adopting the above solution, accounts opened by the same user on different platforms can be locked through a strong ID, and a mapping relationship between these accounts can be established.
[0067] In some embodiments of the present invention, in the step of determining a user ID that has an interactive relationship with the user ID based on the behavior data, the user ID of the target of the behavior corresponding to the behavior data of the user ID is determined based on the behavior data, and the identification ID of the target is determined based on the user ID of the target.
[0068] In some embodiments of the present invention, in the step of determining the user ID of the target of the behavior corresponding to the behavior data of the user ID based on the behavior data, and determining the identification ID of the target based on the user ID of the target, the corresponding behavior type is determined based on the behavior data.
[0069] In some embodiments of the present invention, in the step of constructing an association graph by taking the identification ID of a user ID as a base node and the identification ID corresponding to the user ID that has an interactive relationship with the identification ID as an associated node, each identification ID is constructed as a node, and in an association graph, the node corresponding to the identification ID of a user ID is taken as a base node, the node corresponding to the identification ID that has an interactive relationship with the identification ID is taken as an associated node, and edges are constructed between the base nodes and the associated nodes.
[0070] By adopting the above scheme, the scheme represents the identification IDs associated with each identification ID in the identification ID association graph, thereby ensuring the statistical completeness of a single identification ID.
[0071] In some embodiments of the present invention, in the step of taking the node corresponding to the identification ID that has an interactive relationship with the identification ID as an associated node and constructing an edge between the base node and the associated node, the behavior type corresponding to the behavior data is used as the type of the edge. If there are multiple behavior types between the base node and the associated node, multiple edges corresponding to the multiple behavior types are constructed to obtain the association graph.
[0072] By adopting the above scheme, if there are multiple behavior types between the reference node and the associated node, multiple edges corresponding to the multiple behavior types are constructed, which can represent all the behavior types between the two nodes.
[0073] In some embodiments of the present invention, in the step of determining the correlation between association graphs and constructing a smart ID for the relevant association graph identifier ID:
[0074] Each association graph is used as a starting analysis association graph, and the types and numbers of edges between the reference node and each association node in each association graph are counted;
[0075] Then count whether there are associated nodes with the same edge type and number in the associated graph taking the associated node as the base node, and use the associated graph as the associated graph for the association analysis;
[0076] If so, the two association graphs are related association graphs, and the association nodes with the same edge type and number existing in the association graph with the association node as the base node and the two identification IDs of the base node in the original association graph are constructed into a smart ID.
[0077] The above scheme is adopted. This scheme is based on the principle that accounts operated by the same user often have the same associations, such as the principle that QQ and WeChat friends of the same user often have high similarities. The corresponding accounts can be further constructed into a smart ID; moreover, in this scheme, although the same behavior type does not necessarily point to the same user, it is sufficient to illustrate the similarity of the two accounts. In subsequent schemes, this scheme will uniformly push accounts with the same smart ID, which can at least ensure corresponding pushes for similar accounts, thereby improving push efficiency.
[0078] In some embodiments of the present invention, in the step of constructing two identification IDs of the associated nodes with the same edge type and number existing in the associated graph with the associated node as the reference node and the reference node in the original associated graph into one smart ID, the associated nodes with the same edge type and number existing in the associated graph with the reference node in the original associated graph are used as the reference node;
[0079] The maximum number of interactions between the benchmark node and the associated nodes in the starting analysis association graph, as well as the maximum number of interactions between the benchmark node and the associated nodes with the same edge type and number in the associated analysis association graph are counted. If the two interaction relationships are the same, the identification IDs of the benchmark node in the starting analysis association graph and the corresponding associated nodes in the associated analysis association graph are constructed into a smart ID.
[0080] In the specific implementation process, this solution uses association rule mining, cluster analysis and other technologies to discover the association relationship between different IDs. Different data sources are mapped based on multi-dimensional features such as applications, payment channels, and user IDs of internal systems. This solution defines the feature dimensions and association rules in the multi-dimensional relationship mapping model, and divides different relationship levels based on data sources.
[0081] Using the above scheme, this scheme further ensures the similarity of the accounts under the smart ID and the accuracy of subsequent push by statistically analyzing the maximum number of interactions between the reference node and the associated node in the association graph.
[0082] The beneficial effects of this program include:
[0083] 1. Data integration and association:
[0084] This solution can unify and integrate user data that was originally scattered across different channels and systems to form a complete user information view.
[0085] Through this solution, enterprises can clearly understand user behaviors, preferences and needs in different scenarios, providing a solid foundation for subsequent data analysis and business applications.
[0086] 2. Improve data accuracy and consistency:
[0087] This solution eliminates data inconsistency and improves data accuracy and reliability by integrating and associating user information under multiple IDs.
[0088] This solution helps companies understand users more accurately and develop more precise marketing strategies and personalized services.
[0089] 3. Enhance data computing efficiency:
[0090] This solution can pull together any ID, solving the problem of traditional methods being limited by the number of IDs, while ensuring the accuracy of the calculation.
[0091] 4. Precision Marketing:
[0092] Through the establishment of Smant ID, user data from different channels are integrated to achieve a panoramic customer portrait.
[0093] 5. Cross-domain tracking:
[0094] In the absence of direct identification, the multi-device usage behavior of the same user is inferred through a multi-dimensional mapping algorithm.
[0095] 6. Data Security:
[0096] All data processing is carried out in the company's internal database, and user information is encrypted to meet privacy and compliance requirements.
[0097] Another aspect of the present invention also relates to a back-end implementation system for providing user identification association, the system comprising a computer device, the computer device comprising a processor and a memory, the memory storing computer instructions, the processor being used to execute the computer instructions stored in the memory, and when the computer instructions are executed by the processor, the system implements the steps implemented by the method.
[0098] The embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the back-end implementation method for providing user identification association is implemented. The computer-readable storage medium can be a tangible storage medium, such as a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a floppy disk, a hard disk, a removable storage disk, a CD-ROM, or any other form of storage medium known in the technical field.
[0099] It should be understood by those skilled in the art that the exemplary components, systems and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software or a combination of the two. Whether it is performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present invention are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link via a data signal carried in a carrier.
[0100] It should be clear that the present invention is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present invention.
[0101] In the present invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with features of other embodiments or replace features of other embodiments.
[0102] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the embodiments of the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.
Claims
1. A back-end implementation method for providing user identification association, characterized in that: The steps of the method include: Collect user data from various business channels, the user data including user ID, behavior data and attribute data; Preliminarily construct an ID mapping relationship based on the user data, construct the user IDs with the mapping relationship into a mapping ID set, and provide an identification ID for the user ID in the mapping ID set; Determine a user ID that has an interactive relationship with the user ID based on the behavior data, take an identification ID of a user ID as a reference node, take an identification ID corresponding to a user ID that has an interactive relationship with the user ID corresponding to the identification ID as an associated node, and construct an associated graph; Determine the correlation between association graphs, and construct smartID for the relevant association graph identification ID; Unified information push is carried out to users with the same smartID.
2. The back-end implementation method for providing user identification association according to claim 1, characterized in that: In the step of preliminarily constructing an ID mapping relationship based on the user data, constructing the user IDs with the mapping relationship into a mapping ID set, and providing an identification ID for the user IDs in the mapping ID set; Obtain strong ID data for each user ID, and establish a mapping relationship between user IDs based on the strong ID data; And construct multiple user IDs with mapping relationships into a mapping ID set.
3. The back-end implementation method for providing user identification association according to claim 2, characterized in that: The strong ID data includes a telephone number or an email address. In the step of establishing a mapping relationship between user IDs based on the strong ID data, a mapping relationship is established between user IDs having the same strong ID data.
4. The back-end implementation method for providing user identification association according to any one of claims 1 to 3, characterized in that: In the step of determining a user ID having an interactive relationship with the user ID based on the behavior data, the user ID of the target of the behavior corresponding to the behavior data of the user ID is determined based on the behavior data, and the identification ID of the target is determined based on the user ID of the target.
5. The back-end implementation method for providing user identification association according to claim 4, characterized in that: In the step of determining the user ID of the target of the behavior corresponding to the behavior data of the user ID based on the behavior data, and determining the identification ID of the target based on the user ID of the target, the corresponding behavior type is determined based on the behavior data.
6. The back-end implementation method for providing user identification association according to claim 5, characterized in that: In the step of constructing an association graph by taking the identification ID of a user ID as a base node and the identification ID corresponding to the user ID having an interactive relationship with the identification ID as an associated node, each identification ID is constructed as a node, and in an association graph, the node corresponding to the identification ID of a user ID is taken as a base node, the node corresponding to the identification ID having an interactive relationship with the identification ID is taken as an associated node, and edges are constructed between the base node and the associated node.
7. The back-end implementation method for providing user identification association according to claim 6, characterized in that: In the step of taking the node corresponding to the identification ID that has an interactive relationship with the identification ID as an associated node and constructing an edge between the base node and the associated node, the behavior type corresponding to the behavior data is taken as the type of the edge. If there are multiple behavior types between the base node and the associated node, multiple edges corresponding to the multiple behavior types are constructed to obtain the association graph.
8. The back-end implementation method for providing user identification association according to claim 7, characterized in that: In the step of determining the correlation between the association graphs and constructing a smartID for the relevant association graph identification ID: Count the types and number of edges between the base node and each associated node in each associated graph; Then count whether there are associated nodes with the same edge type and number in the associated graph taking the associated node as the base node; If so, the two association graphs are related association graphs, and the association nodes with the same edge type and number existing in the association graph with the association node as the base node and the two identification IDs of the base node in the original association graph are constructed into a smartID.
9. The back-end implementation method for providing user identification association according to claim 8, characterized in that: In the step of constructing two identification IDs of the associated nodes with the same edge type and number existing in the associated graph of the associated node as the reference node and the reference node in the original associated graph into one smartID, the associated nodes with the same edge type and number and the reference node in the original associated graph are used as the reference node; The maximum number of interactions between the benchmark node and the associated nodes in the starting analysis association graph, as well as the maximum number of interactions between the benchmark node and the associated nodes with the same edge type and number in the associated analysis association graph are counted. If the two interaction relationships are the same, the identification IDs of the benchmark node in the starting analysis association graph and the corresponding associated nodes in the associated analysis association graph are constructed into a smartID.
10. A back-end implementation system for providing user identification association, characterized in that: The system includes a computer device, which includes a processor and a memory, wherein the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the system implements the steps implemented by the method described in any one of claims 1 to 9.