CN112801561B - User relationship determination method and device, storage medium and electronic equipment - Google Patents
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Abstract
The application discloses a user relationship determination method, a user relationship determination device, a storage medium and electronic equipment. The method comprises the steps of determining a plurality of characteristic indexes and the importance of each characteristic index; calculating the relative importance of any two characteristic indexes to the fund relationship to obtain a first comparison matrix; calculating the relative importance of any two characteristic indexes to the social relationship to obtain a second contrast matrix; calculating the relative importance of any two intermediate relations to the user relation to obtain a third comparison matrix, wherein the intermediate relation is the fund relation or the social relation; determining the weight corresponding to each characteristic index according to each pair of comparison matrixes; determining a score corresponding to each characteristic index according to the two target users; calculating a user relationship score according to the score and the weight corresponding to each characteristic index; and determining the user relationship of the two target users according to the user relationship score. The user relationship determination result obtained by the embodiment of the application is more accurate.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for determining a user relationship, a storage medium, and an electronic device.
Background
People can perform social interaction in multiple aspects by relying on the Internet technology, so that the social relationship among users is increasingly tight. In many application scenarios, the relationship between two users needs to be determined, and various services can be provided for the users according to the determination result of the user relationship. In the related art, the relationship between two users can be measured in a certain dimension or some dimensions, and the dimension for measuring the relationship between the users is less, so that the measurement result is one-sided. In the related scheme for measuring the user relationship based on multiple dimensions, the importance degree of each dimension in measuring the user relationship is difficult to be objectively quantified, so that the user relationship determination result is not accurate enough.
Disclosure of Invention
In order to objectively quantify the importance degree of a plurality of factors influencing the user relationship and obtain an accurate user relationship determination result, the embodiment of the application provides a user relationship determination method, a user relationship determination device, a storage medium and electronic equipment.
In one aspect, an embodiment of the present application provides a method for determining a user relationship, where the method includes:
determining a plurality of characteristic indexes and the importance of each characteristic index;
calculating the relative importance of any two characteristic indexes to the fund relation according to the importance of each characteristic index to obtain a first comparison matrix;
calculating the relative importance of any two characteristic indexes to the social relationship according to the importance of each characteristic index to obtain a second contrast matrix;
calculating the relative importance of any two intermediate relations to the user relation to obtain a third comparison matrix, wherein the intermediate relations are the fund relation or the social relation;
determining the weight corresponding to each characteristic index according to the first contrast matrix, the second contrast matrix and the third contrast matrix;
determining a score corresponding to each characteristic index according to two target users with the user relationship to be determined;
calculating a user relationship score according to the score corresponding to each characteristic index and the corresponding weight;
and determining the user relationship of the two target users according to the user relationship score.
In another aspect, an embodiment of the present application provides an apparatus for determining a user relationship, where the apparatus includes:
the index determining module is used for determining a plurality of characteristic indexes and the importance of each characteristic index;
the first comparison matrix determining module is used for calculating the relative importance of any two characteristic indexes to the fund relation according to the importance of each characteristic index to obtain a first comparison matrix;
the second contrast matrix determining module is used for calculating the relative importance of any two characteristic indexes to the social relationship according to the importance of each characteristic index to obtain a second contrast matrix;
the third comparison matrix determining module is used for calculating the relative importance of any two intermediate relations to the user relation to obtain a third comparison matrix, wherein the intermediate relations are the fund relation or the social relation;
the weight calculation module is used for determining the weight corresponding to each characteristic index according to the first contrast matrix, the second contrast matrix and the third contrast matrix;
the single score calculation module is used for determining a score corresponding to each characteristic index according to two target users of which the user relationship is to be determined;
the weighting calculation module is used for calculating a user relationship score according to the score corresponding to each characteristic index and the corresponding weight;
and the user relationship determining module is used for determining the user relationship between the two target users according to the user relationship score.
In another aspect, an embodiment of the present application provides a computer-readable storage medium, where at least one instruction or at least one program is stored in the computer-readable storage medium, and the at least one instruction or the at least one program is loaded and executed by a processor to implement a user relationship determining method as described above.
In another aspect, an embodiment of the present application provides an electronic device, including at least one processor, and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the at least one processor implements a user relationship determination method as described above by executing the instructions stored by the memory.
The embodiment of the application provides a user relationship determination method, a user relationship determination device, a storage medium and equipment. According to the embodiment of the application, on the basis of modeling based on the analytic hierarchy process, the weight of each characteristic index is accurately calculated, and compared with a scheme of subjectively weighting the characteristic indexes in the related technology, the method is more objective, interference of human factors on a determination result is reduced, and the user relationship determination result is more accurate.
Drawings
In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the related art, the drawings used in the description of the embodiments or the related art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without inventive efforts.
Fig. 1 is a schematic diagram of an implementation environment of a user relationship determining method according to an embodiment of the present application;
fig. 2 is a schematic flowchart of a user relationship determining method according to an embodiment of the present application;
fig. 3 is a schematic flowchart of a method for calculating a first contrast matrix according to an embodiment of the present application;
fig. 4 is a schematic flowchart of a method for calculating a third contrast matrix according to an embodiment of the present application;
FIG. 5 is a flowchart illustrating a method for calculating a second element according to an embodiment of the present disclosure;
FIG. 6 is a flowchart illustrating a method for calculating a weight of each feature index according to an embodiment of the present application;
FIG. 7 is a flowchart illustrating a method for calculating a feature index score according to an embodiment of the present disclosure;
fig. 8 is a schematic diagram illustrating cumulative distribution of feature data corresponding to common friend indicators according to an embodiment of the present application;
FIG. 9 is a schematic diagram of a user relationship determination logic provided in an embodiment of the present application;
fig. 10 is a schematic diagram of a user relationship determination effect provided in an embodiment of the present application;
fig. 11 is a block diagram of a user relationship determination apparatus according to an embodiment of the present application;
fig. 12 is a hardware structural diagram of an apparatus for implementing the method provided by the embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without any creative effort belong to the protection scope of the embodiments in the present application.
It should be noted that the terms "first", "second", and the like in the embodiments of the present application and the drawings described above are used for distinguishing similar objects and not necessarily for describing a particular order or sequence. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In order to make the objects, technical solutions and advantages disclosed in the embodiments of the present application more clearly apparent, the embodiments of the present application are described in further detail below with reference to the accompanying drawings and the embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the embodiments of the application and are not intended to limit the embodiments of the application.
In the following, the terms "first", "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present embodiment, "a plurality" means two or more unless otherwise specified. In order to facilitate understanding of the above technical solutions and the technical effects thereof in the embodiments of the present application, the embodiments of the present application first explain related terms:
artificial intelligence
: the method is a theory, method, technology and application system for simulating, extending and expanding human intelligence by using a digital computer or a machine controlled by the digital computer, sensing the environment, acquiring knowledge and obtaining the best result by using the knowledge. In other words, artificial intelligence is a comprehensive technique of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a manner similar to human intelligence. Artificial intelligence is the research of the design principle and the realization method of various intelligent machines, so that the machines have the functions of perception, reasoning and decision making.
The artificial intelligence technology is a comprehensive subject and relates to the field of extensive technology, namely the technology of a hardware level and the technology of a software level. The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
The user relationship determination belongs to a research direction in social network analysis, and the determination result of the user relationship can be widely applied to various fields, for example, the determination result of the user relationship can be applied to the security field, the recommendation field, the social field, the service field and the like. If the two users are judged to be close users, the behavior of one user can be inferred according to the behavior of the other user, so that a plurality of applications or services based on the user intimacy judgment are derived.
In some related technologies, the intimacy of two users can be measured from a single dimension, for example, if the instant message interaction frequency of the two users is high, the two users are determined to be more intimated, the intimacy determination method is one-sided, and the intimacy relationship of the users is difficult to be measured comprehensively, so that the accuracy is not high.
In other related technologies, the intimacy of two users can be measured based on multiple dimensions, but because it is difficult to quantify the importance of each dimension to intimacy determination, a corresponding weight needs to be set for each dimension subjectively, so that subjective factors are introduced during intimacy determination to influence the accuracy of the determination result.
In order to comprehensively and objectively measure the intimacy between two users and obtain an accurate intimacy determination result, the embodiment of the application provides a user relationship determination method, the user relationship determination method objectively calculates a weight of each characteristic index for user relationship determination, according to the weight, user relationship scores of two target users of a user relationship to be determined can be obtained, according to the scores, the user relationship between the two target users can be determined, and the user relationship objectively embodies the intimacy degree between the two target users.
The method provided by the embodiment of the application may relate to the technical field of cloud, for example toAnd big data
In this embodiment, the method may determine the feature index and calculate the importance of the feature index based on the big data, and may determine the cumulative distribution of the feature data corresponding to each feature index based on the big data. Big data is a data set which cannot be captured, managed and processed by a conventional software tool within a certain time range, and is a massive, high-growth-rate and diversified information asset which can have stronger decision-making power, insight discovery power and flow optimization capability only by a new processing mode. With the advent of the cloud era, big data has attracted more and more attention, and the big data needs special technology to effectively process a large amount of data within a tolerance elapsed time. The method is suitable for the technology of big data, and comprises a large-scale parallel processing database, data mining, a distributed file system, a distributed database, a cloud computing platform, the Internet and an extensible storage system.
The method provided by the embodiment of the application may also relate to the field of artificial intelligence, for example, the characteristic indexes and the importance of each characteristic index may be determined based on artificial intelligence, and the cumulative distribution of characteristic data corresponding to each characteristic index may be calculated based on artificial intelligence.
The method provided by the embodiment of the present application may also relate to a blockchain, that is, the method provided by the embodiment of the present application may be implemented based on the blockchain, or data related to the method provided by the embodiment of the present application may be stored based on the blockchain, or an execution subject of the method provided by the embodiment of the present application may be located in the blockchain. The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism and an encryption algorithm. Block chain
Essentially a decentralized database, is a string of data blocks associated using cryptography, each data block containing information about a batch of network transactions for verification thereofValidity of information (anti-counterfeiting) and generation of the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, and an application services layer.
The block chain underlying platform can comprise processing modules such as user management, basic service, intelligent contract and operation monitoring. The user management module is responsible for identity information management of all blockchain participants, and comprises public and private key generation maintenance (account management), key management, user real identity and blockchain address corresponding relation maintenance (authority management) and the like, and under the authorization condition, the user management module supervises and audits the transaction condition of certain real identities and provides rule configuration (wind control audit) of risk control; the basic service module is deployed on all block chain node equipment and used for verifying the validity of the service request, recording the service request to storage after consensus on the valid request is completed, for a new service request, the basic service firstly performs interface adaptation analysis and authentication processing (interface adaptation), then encrypts service information (consensus management) through a consensus algorithm, transmits the service information to a shared account (network communication) completely and consistently after encryption, and performs recording and storage; the intelligent contract module is responsible for registering and issuing contracts, triggering the contracts and executing the contracts, developers can define contract logics through a certain programming language, issue the contract logics to a block chain (contract registration), call keys or other event triggering and executing according to the logics of contract clauses, complete the contract logics and simultaneously provide the function of upgrading and canceling the contracts; the operation monitoring module is mainly responsible for deployment, configuration modification, contract setting, cloud adaptation in the product release process and visual output of real-time states in product operation, such as: alarm, monitoring network conditions, monitoring node equipment health status, and the like.
The platform product service layer provides basic capability and an implementation framework of typical application, and developers can complete block chain implementation of business logic based on the basic capability and the characteristics of the superposed business. The application service layer provides the application service based on the block chain scheme for the business participants to use.
Referring to fig. 1, a schematic diagram of an implementation environment of a user relationship determining method according to an embodiment of the present application is shown, and as shown in fig. 1, the implementation environment may include at least a client 01 and a server 02.
The client 01 may be a smart phone, a desktop computer, a tablet computer, a notebook computer, a digital assistant, a smart wearable device, a monitoring device, a voice interaction device, or other types of devices, or may be software running in the devices, such as web pages provided by some service providers to users, or applications provided by the service providers to users. Specifically, the client 01 may be configured to send an instruction to the server 02 to trigger the server 02 to determine the user relationship between the two target users, where the instruction may include user identifiers of the two target users.
The server 02 may be a server running independently, or a distributed server, or a server cluster composed of a plurality of servers. The server 02 may comprise a network communication unit, a processor and a memory, etc. Specifically, the server 02 may be configured to determine a user relationship between the two target users, where the user relationship represents a degree of intimacy between the two target users.
For convenience of description, a server is used as an executive body to describe a user relationship determination method provided by the embodiment of the present application. The present application provides method steps as described in the examples or flowcharts, but may include more or fewer steps based on conventional or non-inventive labor. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. In practice, the system or server product may be implemented in a sequential or parallel manner (e.g., parallel processor or multi-threaded environment) according to the embodiments or methods shown in the figures.
Please refer to fig. 2, which shows a flowchart of a user relationship determining method according to an embodiment of the present application. As shown in fig. 2, the method may include:
s101, determining a plurality of characteristic indexes and the importance of each characteristic index.
In some possible embodiments, the feature indicators used to determine the user relationship and the importance of each feature indicator may be determined empirically. For example, if the interaction frequency of the instant messages is high, which indicates that the user frequently communicates, the interaction frequency of the instant messages can be determined as a characteristic index; users who concern each other in the community network can often carry out community communication, whether the users concern each other or not can be determined as another characteristic index, and the determination method and the importance value of the characteristic index are not limited in the embodiment of the application.
In another possible embodiment, the characteristic index may be determined among a plurality of indexes to be determined based on the collected samples, and the determination of the characteristic index based on the samples is more objective than the determination of the characteristic index based on experience, and the determined characteristic index is more reasonable.
In one embodiment, a positive sample set and a negative sample set may be constructed, and the number of positive samples in the positive sample set and the number of negative samples in the negative sample set are not limited in the embodiments of the present disclosure, and each of the positive sample set and the negative sample set may include 200 samples, for example.
Each positive sample in the positive sample set may include data related to interaction formed by two users with close relationship in multiple dimensions, and the present disclosure embodiment does not limit the dimensions, for example, the positive sample may include frequency, number, and content of interaction messages between the two users, fund exchange of interaction between the two users, overlap of social networks between the two users, and the like.
The embodiment of the disclosure can optionally select two users in a user cluster formed by users with close relationship, and construct the positive sample according to the related data of the two users. In one possible scenario, the user cluster may be determined in combination with the actual scenario. For example, if the user relationship determination is applied to a scene of searching for a crime group, two crime members may be selected from a certain crime group that has been determined, and the positive sample may be obtained according to data related to interaction formed by the two crime members in the multiple dimensions.
In one embodiment, each negative example in the negative example set may include data related to interaction formed in the plurality of dimensions by two users without intimacy, and one negative example may be constructed according to the related data of two users located in different user clusters.
In some possible embodiments, the negative sample set and the positive sample set may be analyzed based on a preset data model, the importance degree corresponding to each dimension is calculated, and the feature index is determined according to the importance degree corresponding to each dimension
Models, decision tree models, etc.
In an embodiment, an index corresponding to a dimension having an importance greater than a preset importance threshold may be determined as the characteristic index. In another embodiment, the dimensions may be sorted according to a descending order of importance, and the indexes corresponding to a preset number of dimensions sorted before are determined as the characteristic indexes.
Illustratively, the transfer relation index, the red envelope relation index, the code scanning relation index, the certificate relation index, the equipment relation index, the friend duration index, the common group chat number index and the common friend index can be determined as the characteristic indexes.
And S102, calculating the relative importance of any two characteristic indexes to the fund relation according to the importance of each characteristic index to obtain a first comparison matrix.
The pair-wise comparison matrix is a quantitative basis in the analytic hierarchy process, and in the embodiment of the application, for the N feature indexes, a first pair-wise comparison matrix of N × N may be formed. For the characteristic index sequence formed by N characteristic indexes
Any of the characteristic indexes
And characteristic index
Characteristic index
Compared with the characteristic index
The relative importance of the fund relationship is the first comparative matrix
Middle element
The value of (c).
Referring to fig. 3, a method for calculating a first comparison matrix in an embodiment of the present application is shown, where the calculating a relative importance of any two feature indicators to a fund relationship according to the importance of each of the feature indicators to obtain the first comparison matrix includes:
and S1021, obtaining a characteristic index sequence according to the characteristic indexes.
A characteristic index sequence formed by a transfer relation index, a red envelope relation index, a code scanning relation index, a certificate relation index, a device relation index, a friend duration index, a common group chat number index and a common friend index
For example, wherein
In order to be an index of the transfer relationship,
is an index of the relation of the red envelope,
is an index of the code-scanning relationship,
is an index of the relation between the same certificate,
in order to be an index of the relation with the equipment,
is the time length index of the good friends,
in order to provide an index for the number of common group chats,
is the index of common friends.
S1022, any one first index and any one second index are obtained, where the first index and the second index are both characteristic indexes in the characteristic index sequence.
S1023, according to the positions of the first index and the second index in the characteristic index sequence, determining the position of a first element in the first contrast matrix; the first element characterizes a relative importance of the first indicator to the financial relationship as compared to the second indicator.
Illustratively, if the first index is
The second index is
If so, the first element obtained correspondingly is
Which is located in the first contrast matrix
First row and first column.
And S1024, calculating the value of the first element according to the importance of the first index and the importance of the second index.
In one embodiment, the calculating the value of the first element based on the importance of the first index and the importance of the second index includes:
s10241, determining the characteristic indexes influencing the fund relationship in the characteristic indexes to obtain a fund index set.
A characteristic index sequence formed by a transfer relation index, a red envelope relation index, a code scanning relation index, a certificate relation index, a device relation index, a friend duration index, a common group chat number index and a common friend index
For example, wherein
In order to be an index of the transfer relationship,
is an index of the relation of the red envelope,
is an index of the code-scanning relationship,
is an index of the relation between the same certificate,
the five indexes are related to the fund relationship to form a fund index set
Wherein
。
S10242, if the first indicator belongs to the fund indicator set and the second indicator does not belong to the fund indicator set, setting the value of the first element to a first preset value; the first predetermined value indicates that the relative importance of the first indicator to the fund relationship as compared to the second indicator is higher than a predetermined level.
Illustratively, if the first index is
Belonging to the set of capital indexes
The second index is
Which does not belong to the set of capital indicators
Then it can be assigned to the first element
Is determined as a first preset value. The predetermined degree is a degree range, which can be represented as (second predetermined value, first predetermined value), in one embodiment, the second predetermined value can be 1/9, the first predetermined value can be 9, and then the degree range can be represented as (1/9,9), the first predetermined value represents that the relative importance of the first index compared to the second index to the fund relationship is higher than the right boundary of the predetermined degree range.
S10243, if the first indicator does not belong to the fund indicator set and the second indicator belongs to the fund indicator set, setting the value of the first element to a second preset value; the second predetermined value indicates that the importance of the first indicator to the fund relationship is lower than the predetermined degree compared to the second indicator.
Illustratively, if the first index is
Which does not belong to the set of capital indicators
The second index is
Belonging to the set of capital indexes
Then it can be assigned to the first element
Is determined as a second predetermined value, the second predetermined value indicating that the relative importance of the first indicator compared to the second indicator for the fund relationship is lower than the left boundary of the predetermined extent.
S10244, if neither the first index nor the second index belongs to the fund index set, setting the value of the first element to a third preset value; the third preset value represents that the importance of the first index and the second index relative to the fund relationship is the same.
If the first index and the second index do not belong to the fund index set
Then, the first index and the second index are not important for the fund relationship, and may also be considered to be the same (neither is important), so that the value of the corresponding first element may be set as a third preset value, and in one embodiment, the third preset value is 1.
S10245, if the first index and the second index both belong to the fund index set and the importance of the first index is greater than or equal to the importance of the second index, then performing an integer process on the ratio of the importance of the first index to the importance of the second index to obtain a first target value, and determining the smaller value of the first target value and the first preset value as the value of the first element.
S10246, if the first index and the second index both belong to the fund index set and the importance of the first index is less than the importance of the second index, then the ratio of the importance of the second index to the importance of the first index is rounded to obtain a second target value, and the reciprocal of the smaller value of the second target value and the first preset value is determined as the value of the first element.
Illustratively, if the first index is
Belonging to the set of capital indexes
If the second index is
Belonging to the set of capital indexes
When is coming into contact with
Degree of importance of
Is greater than or equal to
Degree of importance of
Then, will
Is determined as the value of the first element mentioned aboveMiddle symbol
Indicating a rounding process. If it is
Degree of importance of
Is less than
Degree of importance of
Then will be
The value of the first element is determined.
S1025, obtaining the first comparison matrix according to the position of the first element in the first comparison matrix and the value of the first element.
A characteristic index sequence formed by a transfer relation index, a red envelope relation index, a code scanning relation index, a certificate relation index, a device relation index, a friend duration index, a common group chat number index and a common friend index
The first predetermined value is 9, the second predetermined value is 1/9, the third predetermined value is 1, and the first contrast matrix is taken as an example
Can be expressed as
Wherein, in the step (A),
is based on the method described in the preamble
And
and (4) calculating.
And S103, calculating the relative importance of any two characteristic indexes to the social relationship according to the importance of each characteristic index to obtain a second contrast matrix.
The second comparison matrix is calculated in the same way as the first comparison matrix. Illustratively, for the above characteristic index sequence
Characteristic index
Compared with the characteristic index
The relative importance of the social relationship is the second contrast matrix
Middle element
For the second contrast matrix
Middle element
The calculation method of (c) is explained as follows:
and S1, determining the characteristic indexes which influence the social relationship in the plurality of characteristic indexes to obtain a social index set.
Still using the transfer relation index, the red envelope relation index, the code scanning relation index, the certificate relation index, the equipment relation index and the friend duration indexCharacteristic index sequence formed by index of mark, common group chat number and common friend index
For example, wherein
Is an index of the relation between the same certificate,
in order to be an index of the relation with the equipment,
is the time length index of the good friends,
in order to provide an index for the number of common group chats,
if the five indexes are the indexes of common friends and are all related to social relations, a social index set is formed
Wherein
。
S2, determining a second contrast matrix based on the set of social indicators
Middle element
The value of (c).
In the embodiment of the present application, the calculation method of the second comparison matrix and the calculation method of the first comparison matrix are based on the same concept, and are not described herein again.
The account transfer relation index, the red envelope relation index, the code scanning relation index, the certificate relation index,Characteristic index sequence formed by same-equipment relation index, friend duration index, common group chat number index and common friend index
The first preset value is 9, the second preset value is 1/9, the third preset value is 1, and the second contrast matrix is taken as an example
Can be expressed as
。
And S104, calculating the relative importance of any two intermediate relations to the user relation to obtain a third comparison matrix, wherein the intermediate relations are the fund relation or the social relation.
Specifically, please refer to fig. 4, which illustrates a flowchart of a method for calculating a third contrast matrix in an embodiment of the present application. Calculating the relative importance of any two intermediate relationships to the user relationship to obtain a third contrast matrix, including:
s1041, obtaining a fund index set and a social index set according to the characteristic indexes; the fund index set is a set formed by characteristic indexes influencing the fund relationship in the characteristic indexes, and the social index set is a set formed by characteristic indexes influencing the social relationship in the characteristic indexes.
In this step, the capital index set and the social index set are the same as those described above, and still form a characteristic index sequence by the transfer relation index, the red envelope relation index, the code scanning relation index, the certificate-sharing relation index, the equipment-sharing relation index, the friend duration index, the common group chat number index and the common friend index
For example, a set of fund indicators may be obtained
And set of social metrics
Wherein
，
。
And S1042, determining a set sequence according to the fund index set and the social index set.
Illustratively, the sequence of sets may be represented as
。
S1043, obtaining any one first set and any one second set, wherein the first set and the second set are both sets in the set sequence.
S1044, determining the position of a second element in the third comparison matrix according to the positions of the first set and the second set in the set sequence; the second element characterizes a relative importance of the first set to the user relationship compared to the second set.
Illustratively, if the first set is
The second set is
If so, the corresponding obtained second element is
Which is located in the third contrast matrix
First row and first column.
And S1045, calculating the value of the second element according to the importance of the first set and the importance of the second set.
Please refer to fig. 5, which illustrates a flowchart of a method for calculating a second element in an embodiment of the present application. The calculating a value of the second element based on the importance of the first set and the importance of the second set includes:
and S10451, counting the importance of each characteristic index in the fund index set to obtain the importance corresponding to the fund index set.
S10452, counting the importance of each characteristic index in the social index set to obtain the importance corresponding to the social index set.
Exemplary, for the set of funding indicators
Then can be
Determining the importance corresponding to the fund index set; for social index set
Then can be
And determining the importance corresponding to the social index set.
S10453, if the importance of the first set is greater than or equal to the importance of the second set, rounding the ratio of the importance of the first set to the importance of the second set to obtain a third target value, and determining the smaller value of the third target value and the first preset value as the value of the second element.
S10454, if the importance of the first set is smaller than the importance of the second set, rounding the ratio of the importance of the second set to the importance of the first set to obtain a fourth target value, and determining the reciprocal of the smaller value of the fourth target value and the first preset value as the value of the second element.
Illustratively, if the first set is
The second set is
And importance of the first set
Importance greater than or equal to the second set
Then will be
Determined as the value of the second element, wherein the symbol
Indicating a rounding process. If the importance of the first set is
Importance less than the second set
Then will be
The value of the second element is determined.
And S1046, obtaining the third contrast matrix according to the position of the second element in the third contrast matrix and the value of the second element.
Continuing to use the above example based on the same reasoning, the third contrast matrix
Can be expressed as
。
And S105, determining the weight corresponding to each characteristic index according to the first comparison matrix, the second comparison matrix and the third comparison matrix.
In an embodiment, before calculating the weight corresponding to each of the feature indicators, consistency check may be performed on at least one of the first comparison matrix, the second comparison matrix, and the third comparison matrix. The consistency check is performed mainly to prevent a logic error in the matrix assignment process from being judged, and to improve the accuracy of the finally obtained weight corresponding to each characteristic index. In this embodiment, the first comparison matrix, the second comparison matrix, and the third comparison matrix all belong to the determination matrix. Illustratively, if
Is the number of 2, and the number of the second,
2, the characteristic index for the capital relationship is shown
Specific characteristic index
2 times of importance, characteristic index
Specific characteristic index
Important 2 times, characteristic index obtained by reasoning
Index of stress ratio
4 times important; if it is
If not, it indicates that the first comparison matrix has consistency problem. In order to check the consistency problem in the judgment matrix, the embodiment of the application provides a consistency checking method, which comprises the following steps: the method comprises the steps of solving the eigenvalue and the eigenvector of any judgment matrix to be subjected to consistency check, and taking the maximum eigenvalue lambda and the eigenvector corresponding to the maximum eigenvalue lambda
(ii) a Calculating a consistency index
And n represents the decision matrix dimension. Continuing with the above example, if the dimensions of the first and second comparison matrices are 8 × 8 matrices, the corresponding n value is 8, the third comparison matrix is 2 × 2 matrix, and the corresponding n value is 2.
The embodiment of the application considers that when CI =0, the judgment matrix is high in consistency; when the CI is close to 0, the consistency of the judgment matrix is still enough; the larger the CI is, the more serious the problem of consistency of the judgment matrix is, and the judgment matrix needs to be reconstructed when a preset consistency threshold value is exceeded.
When the first comparison matrix, the second comparison matrix, and the third comparison matrix all meet the requirement of consistency, in an embodiment of the present application, a weight of each feature index may be calculated according to the first comparison matrix, the second comparison matrix, and the third comparison matrix, please refer to fig. 6, which shows a method for calculating a weight of each feature index, where the determining a weight corresponding to each feature index according to the first comparison matrix, the second comparison matrix, and the third comparison matrix includes:
s1051, determining a first weight vector according to the eigenvector corresponding to the maximum eigenvalue of the first contrast matrix.
And S1052, determining a second weight vector according to the eigenvector corresponding to the maximum eigenvalue of the second contrast matrix.
And S1053, determining a third weight vector according to the eigenvector corresponding to the maximum eigenvalue of the third comparison matrix.
In this embodiment of the application, the normalization result of the eigenvector corresponding to the maximum eigenvalue of the first contrast matrix may be determined as the first weight vector
. Based on the same idea, a second weight vector
And a third weight vector
And is also the result of the normalization of the corresponding feature vectors.
And S1054, determining the weight corresponding to each feature index according to the first weight vector, the second weight vector and the third weight vector.
Illustratively, for a sequence of features
Arbitrary characteristic index
In other words, the corresponding weight may be
。
And S106, determining the score corresponding to each characteristic index according to the two target users with the user relationship to be determined.
In the embodiment of the application, the score corresponding to each characteristic index is directly related to two target users of the user relationship to be determined, and the interactive performance of the two target users on the characteristic index dimension is reflected. The embodiment of the present disclosure does not limit the specific calculation method of the score, and may refer to a correlation scheme for calculating the index score in the correlation technique. In one embodiment, please refer to fig. 7, which illustrates a flowchart of a method for calculating a feature index score according to an embodiment of the present application. The determining a score corresponding to each of the feature indicators according to two target users with a user relationship to be determined includes:
and S1061, acquiring cumulative distribution of the characteristic data corresponding to the characteristic indexes.
In the cumulative distribution of the feature data of the embodiment of the disclosure, for any abscissa x, the corresponding ordinate y represents the percentage of the feature data of which x is less than or equal to x in the total feature data.
For example, in the embodiment of the present application, the cumulative distribution of the feature data corresponding to each feature index may be determined according to the big data statistical result. In a possible embodiment, the data in the positive sample set and the negative sample set obtained above may be subjected to statistics to obtain a cumulative distribution of feature data corresponding to the feature index. Taking the cumulative distribution of feature data of the feature index of the common friend as an example, please refer to fig. 8, which shows a schematic diagram of the cumulative distribution of feature data corresponding to the common friend index. In the embodiment of the present disclosure, two users may form one user pair, and as can be seen from the statistical result in fig. 8, the user pair formed by the users whose number of common friends is less than 5 accounts for 40% of the total user pair, and the proportion of the user pair formed by the users whose number of common friends is less than or equal to 8 accounts for approximately 100% of the total user pair.
And S1062, determining target characteristic data corresponding to the characteristic indexes based on the two target users.
In this embodiment, the target feature data is feature data corresponding to the feature index determined according to the two target users. The above description of the feature data is not repeated herein, and taking the feature index of the common friend as an example, the target feature data is the number of the common friends of the two target users.
And S1063, determining a score corresponding to the characteristic index according to the target characteristic data and the accumulated distribution of the characteristic data.
In this embodiment of the present application, the target feature data may be used as an abscissa to query the feature data cumulative distribution, and a corresponding ordinate may be used as a score corresponding to the feature index.
For example, still taking the common friends in fig. 8 as an example, if the number of the common friends of the two target users is 8 or exceeds 8, the corresponding cumulative percentage may be considered to reach 100%, the score of the common friends on the characteristic index may be considered to be 1, and if the number of the common friends of the two target users is 4, the corresponding cumulative percentage may be considered to reach 40%, the characteristic index of the common friends may obtain 0.4, and the score of the characteristic index may be limited to the interval [0,1] by the cumulative percentage.
The score calculation method can fully refer to the samples obtained in the foregoing, obtain the feature data cumulative distribution with statistical significance, and can more scientifically and objectively determine the score of the feature index according to the feature data cumulative distribution, so that the final user relationship determination result is more accurate.
And S107, calculating the user relation score according to the score corresponding to each characteristic index and the corresponding weight.
In the embodiment of the application, the user relationship score is obtained through weighted summation, and the performance of the user relationship on each characteristic index is comprehensively considered, so that the user relationship score is objective and accurate.
And S108, determining the user relationship of the two target users according to the user relationship scores.
In one embodiment, the user relationship score may be directly output as a quantitative result of the user relationship or may be directly used.
In another embodiment, the user relationship may be obtained according to the user relationship score and a preset user relationship determination rule. For example, if the user relationship score is higher than a preset first score threshold, it is determined that the user relationship is an affinity relationship; if the user relationship score is lower than a preset second score threshold, the user relationship is determined to be a non-close relationship. The relationship determination rule, the first score threshold value, and the second score threshold value are not limited in the embodiments of the present disclosure.
Referring to fig. 9, which shows a schematic diagram of a user relationship determination logic according to an embodiment of the present application, an analysis model with a three-layer structure is first established based on the AHP idea, 8 feature indexes for calculating user relationships are determined in an index layer, a fund relationship and a social relationship are determined in a criterion layer, and a user relationship (intimacy) is taken as an analysis target in a target layer. After the modeling is successful, determining judgment matrixes (a first comparison matrix, a second comparison matrix and a third comparison matrix) according to the modeling results of the index layer and the criterion layer, respectively obtaining weight vectors after consistency check is carried out on the judgment matrixes, and finally calculating the user relationship (intimacy) between any two users according to the weight vectors.
Referring to fig. 10, a schematic diagram illustrating an effect of determining a user relationship by using the method in the present application and the method in the related art in one embodiment is shown, and it is obvious that a curve obtained by the user relationship determining effect provided by the present application is smoother and has better accuracy. The user relationship determining method provided by the embodiment of the application is based on modeling based on an analytic hierarchy process, and the weight of each characteristic index is accurately calculated, so that compared with a scheme of subjectively weighting the characteristic indexes in the related art, the method is more objective, interference of human factors on a determination result is reduced, and the user relationship determination result is more accurate. In fact, in the process of calculating the user relationship, the weight of the characteristic index and the score corresponding to each characteristic index are directly calculated based on big data, so that the whole user relationship determination process is hardly interfered by subjective factors, and the accuracy of the user relationship determination result is ensured.
The user relationship determining method provided by the embodiment of the application can be applied to various possible scenes. For example, in a scenario where a user relationship graph needs to be calculated, the method provided by the embodiment of the present application may be used to calculate the relationship between users. Taking the crime risk control field as an example, if a group crime needs to be researched, the relationship between related members can be calculated based on the method provided by the embodiment of the application, and a cluster formed by relatively close members is determined as a possible crime group.
In other scenarios, key user filtering or suspicious user marking may be performed based on the user relationship determination method provided by the embodiment of the present application. For example, in a certain scenario, a user having an affinity with the user a is a key user, and a user B having an affinity with the user a may be screened out based on the user relationship determination method provided in the embodiment of the present application, and the user B is used as a key user. For another example, in a certain scenario, if a user having an affinity with the user C is a suspicious user, a user D having an affinity with the user C may be screened out based on the user relationship determination method provided in the embodiment of the present application, and the user D is marked as a suspicious user.
The embodiment of the present application further discloses a user relationship determining apparatus, as shown in fig. 11, the apparatus includes:
an index determining module 10, configured to determine a plurality of characteristic indexes and an importance of each of the characteristic indexes;
a first comparison matrix determining module 20, configured to calculate, according to the importance of each of the feature indexes, the relative importance of any two feature indexes to the fund relationship, so as to obtain a first comparison matrix;
a second comparison matrix determining module 30, configured to calculate, according to the importance of each of the feature indicators, the relative importance of any two feature indicators to the social relationship, so as to obtain a second comparison matrix;
a third comparison matrix determining module 40, configured to calculate relative importance of any two intermediate relationships to the user relationship to obtain a third comparison matrix, where the intermediate relationships are the fund relationship or the social relationship;
a weight calculation module 50, configured to determine a weight corresponding to each of the feature indexes according to the first comparison matrix, the second comparison matrix, and the third comparison matrix;
a single score calculating module 60, configured to determine a score corresponding to each of the feature indexes according to two target users with a user relationship to be determined;
a weighting calculation module 70, configured to calculate a user relationship score according to the score and the corresponding weight corresponding to each feature index;
and a user relationship determining module 80, configured to determine the user relationship between the two target users according to the user relationship score.
Specifically, the embodiment of the present application discloses a user relationship determining apparatus and the corresponding method embodiments, all based on the same inventive concept. For details, please refer to the method embodiment, which is not described herein.
Embodiments of the present application also provide a computer program product or computer program comprising computer instructions stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to execute the user relationship determination method.
Embodiments of the present application further provide a computer-readable storage medium, where the computer-readable storage medium may store a plurality of instructions. The above-mentioned instructions may be adapted to be loaded by a processor and to perform a method of user relationship determination as described above in embodiments of the present application.
Further, fig. 12 is a schematic hardware configuration diagram of an apparatus for implementing the method provided in the embodiment of the present application, and the apparatus may participate in forming or containing the device or system provided in the embodiment of the present application. As shown in fig. 12, the apparatus 100 may include one or more processors (shown as 102a, 102b, … …, 102 n), which may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA, a memory 104 for storing data, and a transmission device 106 for communication functions. Besides, the method can also comprise the following steps: a display, an input/output interface (I/O interface), a Universal Serial Bus (USB) port (which may be included as one of the ports of the I/O interface), a network interface, a power source, and/or a camera. It will be understood by those skilled in the art that the structure shown in fig. 12 is only an illustration and is not intended to limit the structure of the electronic device. For example, device 100 may also include more or fewer components than shown in FIG. 12, or have a different configuration than shown in FIG. 12.
It should be noted that the one or more processors and/or other data processing circuitry described above may be referred to generally herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part in software, hardware, firmware, or any combination thereof. Further, the data processing circuitry may be a single, stand-alone processing module, or incorporated in whole or in part into any of the other elements in the device 100 (or mobile device). As referred to in the embodiments of the application, the data processing circuit acts as a processor control (e.g. selection of a variable resistance termination path connected to the interface).
The memory 104 may be used to store software programs and modules of application software, such as program instructions/data storage devices corresponding to the methods described above in the embodiments of the present application, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, so as to implement the user relationship determining method described above. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, memory 104 may further include memory located remotely from the processor, which may be connected to device 100 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used for receiving or transmitting data via a network. Specific examples of such networks may include wireless networks provided by the communication provider of the device 100. In one example, the transmission device 106 comprises a network adapter
And the base station can be connected with other network equipment so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency
And the module is used for communicating with the Internet in a wireless mode.
The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the device 100 (or mobile device).
It should be noted that: the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And specific embodiments thereof have been described above. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present application are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the device and server embodiments, since they are substantially similar to the method embodiments, the description is simple, and the relevant points can be referred to the partial description of the method embodiments.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, and the program may be stored in a computer-readable storage medium, and the storage medium may be a read-only memory, a magnetic disk or an optical disk.
The above description is only a preferred embodiment of the present application and should not be taken as limiting the present application, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present application should be included in the scope of the present application.
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