CN107392627B - User interaction circle relation identification method based on interaction frequency and interaction index - Google Patents

User interaction circle relation identification method based on interaction frequency and interaction index Download PDF

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CN107392627B
CN107392627B CN201710585955.1A CN201710585955A CN107392627B CN 107392627 B CN107392627 B CN 107392627B CN 201710585955 A CN201710585955 A CN 201710585955A CN 107392627 B CN107392627 B CN 107392627B
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胡东
牛桂东
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Nanjing Tandao Information Technology Corp ltd
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Abstract

The invention discloses a user contact circle relationship identification method based on contact frequency and contact index, belonging to the technical field of operator and social contact public praise and family package meal recommendation marketing, and comprising the following steps: calculating the communication frequency and the communication index between every two users; establishing a corresponding contact circle model of the user according to the contact frequency, the contact index and the contact time period preference, wherein the contact circle model comprises a working circle model, a living circle model and a comprehensive circle model; establishing a user social network model based on the corresponding contact circle information of the user; constructing a family relation model based on the user life circle model; the method effectively solves the problems that the existing single-customer group-sending marketing is poor in pertinence, low in cost return rate and easy to cause user complaints.

Description

User interaction circle relation identification method based on interaction frequency and interaction index
Technical Field
The invention belongs to the technical field of recommendation and marketing of business circle public praise and family combos of operators and society, and particularly relates to a user business circle relationship identification method based on business frequency and business index.
Background
In the internet era, the traditional single-customer large-scale mass-sending marketing mode is difficult to attract users, the marketing cost return rate is low, and the complaints of the users are easily caused.
In the prior art, as the marketing recommendation is mainly oriented to single customer marketing recommendation, the following defects exist in the marketing recommendation process:
firstly, single-customer group-distribution marketing is easy to cause user dislike and cause user complaints.
Secondly, the characteristics of the client group are not known, and the interest points of the group cannot be grasped.
Thirdly, the family composition is not known, and family packages cannot be recommended in a targeted manner.
And fourthly, no proper entry point exists for the marketing of the different networks.
In a word, the existing single-customer mass-distribution type marketing has poor pertinence and low cost return rate, and is easy to cause customer complaints.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art that the traditional single-client large-scale mass-sending marketing mode is difficult to attract users, the marketing cost return rate is low, the user complaints are easily caused and the like, the invention provides the user contact circle relationship identification method based on the contact frequency and the contact index.
The technical scheme is as follows: in order to achieve the above object, the present invention provides a method for identifying a relationship between a user and a contact circle based on contact frequency and contact index, the method comprising the steps of: the method comprises the following steps: user interaction circle relation based on interaction frequency and interaction index
A: calculating the communication frequency and the communication index between every two users;
b: establishing a corresponding contact circle model of the user according to the contact frequency, the contact index and the contact time period preference, wherein the contact circle model is used for the user
The ring model comprises a working ring model, a living ring model and a comprehensive ring model;
c: establishing a user social network model based on the corresponding contact circle information of the user;
d: and constructing a family relation model based on the user life circle model.
Further, the step B includes the steps of:
calculating the traffic frequency of the user, wherein the traffic frequency is (weight 1 is the number of calls/short messages/multimedia messages in the current month of the user + weight 2 is the number of calls/short messages/multimedia messages in the current month + weight 3 is the number of ten days of calls/short messages/multimedia messages in the current month of the user)/the number of days in the current month, and one ten days is 10 days; the weight 1, the weight 2 and the weight 3 are set by self-definition;
and calculating a work circle interaction index, a life circle interaction index and a comprehensive circle interaction index of the user based on the interaction frequency of the user.
Further, based on the communication frequency of the user, calculating a work circle communication index, a life circle communication index and a comprehensive circle communication index of the user, and specifically comprising the following steps:
the method comprises the steps of arranging a voice call ticket and a short message/multimedia message call ticket of a user, and respectively calculating the call times, the call duration and the short message/multimedia message call times of the user in a working period, a living period and an undifferentiated period;
the working circle communication index of the user is the coefficient 1, the communication frequency of the user is + the coefficient 2, the call times of the working period of the user is + the coefficient 3, the call duration of the working period of the user is + the coefficient 4, the short message call times of the working period of the user is + the coefficient 5, and the multimedia message call times of the working period of the user is calculated;
the life circle communication index of the user is the coefficient 1, the communication frequency of the user is + the coefficient 2, the call time of the life time of the user is + the coefficient 3, the call time of the life time of the user is + the coefficient 4, the short message call time of the life time of the user is + the coefficient 5, and the multimedia message call time of the life time of the user is;
the comprehensive circle interaction index of the user is the coefficient 1, the interaction frequency of the user + the coefficient 2, the call times of the user in the undistinguished time periods + the coefficient 3, the call duration of the user in the undistinguished time periods + the coefficient 4, the short message call times of the user in the undistinguished time periods + the coefficient 5, and the multimedia message call times of the user in the undistinguished time periods;
the coefficient 1, the coefficient 2, the coefficient 3 and the coefficient 4 are set by self-definition;
and taking the maximum value of the work circle interaction index, the life circle interaction index and the comprehensive circle interaction index of the user, wherein the maximum value is the interaction circle to which the user belongs.
Further, the step C includes the steps of:
firstly, all users in a corresponding interaction circle of the users are evenly distributed into a group;
then calculating the interaction similarity among the users in the group;
calculating the contact closeness between every two users, and determining the contact similarity between the users according to the contact closeness between every two users;
attributing users with low contact similarity in the group to users with weak contact, wherein the low contact similarity is set by self-defining based on the contact closeness value among the users in the group;
dividing users with high contact similarity into a group, wherein the high contact similarity is set by self-definition based on the contact closeness value among the users in the group;
finally forming a plurality of groups with high internal interaction similarity and low external interaction similarity;
and classifying the users into the corresponding groups by an iterative method for the new users in the corresponding interaction circle.
Further, the closeness of the connection between the two users is calculated by the formula (I):
Figure BDA0001353538850000031
wherein, WjIs a period weight, the period weight comprises a user working period weight W1Life time interval weight W2And a non-discriminating period weight W3The time interval weight is set by self-definition; m isiThe number of calls in the ith week; diThe weighting value of the ith week is set by a user, and n is the number of weeks in the period.
Further, the step D includes the steps of:
calculating the communication similarity and communication time period preference among users in the user life circle model based on the user life circle model;
and constructing a family relationship model according to the ID of the same client, the family package of the same client and the account information of the same client of the user.
Further, the interaction similarity among the users in the user life circle model is calculated through the following steps:
the communication similarity calculation module between the users stores the degree of similarity calculation, and when the communication similarity calculation module is executed by the processor, the communication similarity between the users can be obtained, and the method specifically comprises the following steps:
data are processed symmetrically and returned to the database;
according to the calling subscriber sequencing call tickets, calculating the initial position of each calling subscriber and the number of the call tickets, and determining the sequenced call tickets, the initial positions of the subscribers and the number of the call tickets for calculating the similarity;
calling the compiled communication similarity calculation module to calculate the communication similarity between users, and eliminating the call tickets with lower similarity;
and according to the communication similarity calculation among the users, searching the home network/different network users with high communication similarity and bringing the home network/different network users into the same family relationship, and constructing a family relationship model.
Has the advantages that: compared with the prior art, the invention has the advantages that:
firstly, arranging voice call bill and short multimedia message call bill information, and calculating the contact and communication frequency and communication index between every two users; then, establishing a work circle model, a life circle model and a comprehensive circle model according to the communication frequency, the communication index and the preference of the contact time period; secondly, calculating common contacts among users based on the contact circle, calculating similarity indexes among the users, and establishing a social network; finally, establishing family backbone members based on the information of the same client, the same family package and the same account, calculating according to the similarity of the members and the users, and searching local network/different network users with high similarity to bring the local network/different network users into the same family relation client; the method effectively solves the problems that the existing single-customer group-sending type marketing is poor in pertinence, low in cost return rate and easy to cause user complaints, and the marketing success rate is remarkably improved by identifying and classifying the user social network relationship and the family relationship.
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FIG. 1 is a schematic view of the process flow structure of the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings.
The first embodiment is as follows:
referring to fig. 1, a method for identifying a relationship between user contact circles based on contact frequency and contact index includes the following steps: user interaction circle relation based on interaction frequency and interaction index
A: calculating the communication frequency and the communication index between every two users;
b: establishing a corresponding contact circle model of the user according to the contact frequency, the contact index and the contact time period preference, wherein the contact circle model comprises a working circle model, a living circle model and a comprehensive circle model;
c: establishing a user social network model based on the corresponding contact circle information of the user;
d: and constructing a family relation model based on the user life circle model.
Calculating the traffic frequency of the user, wherein the traffic frequency is (weight 1 + number of calls/short messages/multimedia messages in the current month of the user + weight 2 + number of cycles of calls/short messages/multimedia messages in the current month of the user + weight 3 + number of ten days of calls/short messages/multimedia messages in the current month of the user)/number of days in the current month, and one ten days is 10 days; the weight 1, the weight 2 and the weight 3 are set by self-definition; then, calculating a work circle interaction index, a life circle interaction index and a comprehensive circle interaction index of the user based on the interaction frequency of the user; based on the communication frequency of the user, calculating a work circle communication index, a living circle communication index and a comprehensive circle communication index of the user, and specifically comprising the following steps:
the method comprises the steps of arranging a voice call ticket and a short message/multimedia message call ticket of a user, and respectively calculating the call times, the call duration and the short message/multimedia message call times of the user in a working period, a living period and an undifferentiated period;
the working circle communication index of the user is the coefficient 1, the communication frequency of the user is + the coefficient 2, the call times of the working period of the user is + the coefficient 3, the call duration of the working period of the user is + the coefficient 4, the short message call times of the working period of the user is + the coefficient 5, and the multimedia message call times of the working period of the user is calculated;
the life circle communication index of the user is the coefficient 1, the communication frequency of the user is + the coefficient 2, the call time of the life time of the user is + the coefficient 3, the call time of the life time of the user is + the coefficient 4, the short message call time of the life time of the user is + the coefficient 5, and the multimedia message call time of the life time of the user is;
the comprehensive circle interaction index of the user is the coefficient 1, the interaction frequency of the user + the coefficient 2, the call times of the user in the undistinguished time periods + the coefficient 3, the call duration of the user in the undistinguished time periods + the coefficient 4, the short message call times of the user in the undistinguished time periods + the coefficient 5, and the multimedia message call times of the user in the undistinguished time periods;
the coefficient 1, the coefficient 2, the coefficient 3 and the coefficient 4 are set by self-definition;
and taking the maximum value of the work circle interaction index, the life circle interaction index and the comprehensive circle interaction index of the user, wherein the maximum value is the interaction circle to which the user belongs.
Establishing a user social network model based on the corresponding circle of contact information of the user,
the method comprises the following steps:
firstly, all users in a corresponding interaction circle of the users are evenly distributed into a group;
then calculating the interaction similarity among the users in the group;
calculating the contact closeness between every two users, and determining the contact similarity between the users according to the contact closeness between every two users; for example: if the contact closeness of the user A and the user B is close to that of the user B and the user C, the contact closeness of the user A and the user D is close to that of the user D and the user C, the contact closeness of the user A and the user E is close to that of the user E and the user C, or the contact closeness of the user A and the user C is also close to that of other same users, the fact that the contact similarity of the user A and the user C is high is indicated, the number of the common contacts of the user A and the user C is large, the specific number of the users is close to that of the user A and the user C respectively, and the user A and the user C which are high in contact similarity and large in common contacts are divided into a group according to the system condition;
attributing users with low contact similarity in the group to users with weak contact, wherein the low contact similarity is set by self-defining based on the contact closeness value among the users in the group;
dividing users with high contact similarity into a group, wherein the high contact similarity is set by self-definition based on the contact closeness value among the users in the group;
finally forming a plurality of groups with high internal interaction similarity and low external interaction similarity;
dividing the users into the corresponding groups by an iteration method for the new users in the corresponding interaction circle; the contact closeness between every two users is calculated by the formula (I):
Figure BDA0001353538850000051
wherein, WjIs a period weight, the period weight comprises a user working period weight W1Life time interval weight W2And a non-discriminating period weight W3The time interval weight is set by self-definition; m isiThe number of calls in the ith week; diThe weighting value of the ith week is set by a user, and n is the number of weeks in the period.
Grouping according to an iterative mode, dividing all users into a group, calculating the interaction similarity between the users, deleting weak connections between the users, and deleting the weak connections to avoid all the users from being classified into the same group;
the remaining users are divided into small groups with dense contact, so that how to ensure that the contact in the network is dense, users with more common contacts are preferentially divided into one group, and under the precondition, the users are sequentially divided into corresponding groups according to the call records through an iterative method.
The family relation model is built based on the user life circle model, the communication similarity and communication period preference among the users in the user life circle model are calculated based on the user life circle model, the family relation model is built according to the same client ID, the same client family package and the same client account information of the users, and the communication similarity among the users in the user life circle model is calculated and obtained through the following steps:
the communication similarity calculation module between the users stores the degree of similarity calculation, and when the communication similarity calculation module is executed by the processor, the communication similarity between the users can be obtained, and the method specifically comprises the following steps:
data are processed symmetrically and returned to the database;
according to the calling subscriber sequencing call tickets, calculating the initial position of each calling subscriber and the number of the call tickets, and determining the sequenced call tickets, the initial positions of the subscribers and the number of the call tickets for calculating the similarity;
calling the compiled communication similarity calculation module to calculate the communication similarity between users, and eliminating the call tickets with lower similarity;
and according to the communication similarity calculation among the users, searching the home network/different network users with high communication similarity and bringing the home network/different network users into the same family relationship, and constructing a family relationship model.
Example two:
based on the first embodiment, the present embodiment provides a user relationship identification method based on a contact frequency and a contact index, and the basic idea of the present embodiment is: arranging the voice call bill and the short multimedia message call bill information, and calculating the contact communication frequency and the communication index between every two users;
establishing a work circle model, a life circle model and a comprehensive circle model according to the communication frequency, the communication index and the preference of the contact time period;
calculating common contacts among users based on the contact circle, calculating similarity indexes among the users, and establishing a social network;
and establishing family backbone members based on the information of the same client, the same family package and the same account, and searching local network/different network users with high similarity to bring the local network/different network users into the same family relationship client according to the similarity calculation of the members and the users.
Establishing a user social network model based on the corresponding contact circle information of the users, wherein the social network model is used for constructing a social network of the contact circle and finding out a contact dense group, and considering from the perspective of common contacts, when two users are considered to be classified into one group, the two users are expected to have more common contacts, and the main body gradually adds the users into the corresponding group in a fast iteration mode;
the family relation model is built based on the user life circle model and is used for building a family relation recognition model and calculating the similarity of two contact parties, the similarity of the two contact parties and a plurality of numbers under a client is high, the contact time interval is concentrated in the life time interval, and the possibility of the same family with the client is considered to be high;
the interaction similarity between users in the family relationship model established based on the interaction circle information corresponding to the users and the family relationship model established based on the user life circle model can be calculated through the programmed get _ same.c program called and compiled in the following steps;
calling get _ same.c program:
step1, carrying out data symmetry processing (two modes of symmetry and shake/strong), and returning to the database;
step2, according to the call list ordered by the calling user, calculating the initial position (st) of each calling user and the call list number (cnt), returning the ordered call list, st, cnt to R for calculating the communication similarity between users, as shown in table 1:
Figure BDA0001353538850000071
TABLE 1
Step3, calling the written get _ same.c program to calculate the similarity, and eliminating the call tickets with lower similarity (adjustable percentage), as shown in table 2;
Figure BDA0001353538850000072
TABLE 2
The clustering results are shown in table 3 below: g is a group number, and count (1) is the number of group users;
Figure BDA0001353538850000081
TABLE 3
The clustering is an initial skeleton group, and the isolated points removed by the get _ same.c similarity program are distributed to the skeleton group closest to the isolated points:
Figure BDA0001353538850000082
TABLE 4
After the users are grouped, the connectivity, authority index (Peclet ranking value), out degree, in degree and the like of each user are calculated according to the groups, so that the connectivity among the users can be further represented, for example, the different network users with high interaction similarity in the table 5 are searched, and the different network users with high interaction similarity are brought into the relation model;
Figure BDA0001353538850000083
TABLE 5
Example three:
based on the method of the second embodiment, the data obtained by applying the method of the invention to the wing intimacy network in the carrier Chinese telecommunication are as follows:
circle marketing
The success rate of the familiarity network marketing is obviously improved: from the end of 7 months in 2013, the total amount of family network short message dispatching is 6347971, and the marketing success rate is only 0.4%;
no. 7 and 29 in 2013, a life circle is constructed through a social network, leader characters are selected, a list is sent 9255, the number of users participating in the friendship network under the same client is 522, the marketing success rate is 5.64%, and the function of the leader characters in daily social contact is fully utilized;
no. 8 and 12 in 2013, users with concentrated two-two calls are found out in a mode of reducing call groups, the number of the orders is 203350, 2624 users participate, and the success rate is 1.3%;
in 8 middle-of-month ten days in 2013, the data of the life circle, the working circle and the comprehensive circle are recommended in a kindset way, 66354 users are assigned in total, 916 users are successfully handled, and the marketing success rate is 1.4%. At the end of 8 months in 2013, refined marketing is carried out by matching with the Huaian local network, 4249 users are selected for affection network marketing, 184 successful users are successfully carried out, the marketing success rate is 4.33%, and further the marketing success rate of the method is obviously improved.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.

Claims (2)

1. A user contact circle relation identification method based on contact frequency and contact index is characterized in that: the method comprises the following steps: user interaction circle relation based on interaction frequency and interaction index
A: calculating the communication frequency and the communication index between every two users;
calculating the traffic frequency of the user, wherein the traffic frequency is (weight 1 is the number of calls/short messages/multimedia messages in the current month of the user + weight 2 is the number of calls/short messages/multimedia messages in the current month + weight 3 is the number of ten days of calls/short messages/multimedia messages in the current month of the user)/the number of days in the current month, and one ten days is 10 days; the weight 1, the weight 2 and the weight 3 are set by self-definition;
calculating a work circle interaction index, a life circle interaction index and a comprehensive circle interaction index of the user based on the interaction frequency of the user;
b: establishing a corresponding contact circle model of the user according to the contact frequency, the contact index and the contact time period preference, wherein the contact circle model comprises a working circle model, a living circle model and a comprehensive circle model;
based on the communication frequency of the user, calculating a work circle communication index, a living circle communication index and a comprehensive circle communication index of the user, and specifically comprising the following steps:
the method comprises the steps of arranging a voice call ticket and a short message/multimedia message call ticket of a user, and respectively calculating the call times, the call duration and the short message/multimedia message call times of the user in a working period, a living period and an undifferentiated period;
the working circle communication index of the user is the coefficient 1, the communication frequency of the user is + the coefficient 2, the call times of the working period of the user is + the coefficient 3, the call duration of the working period of the user is + the coefficient 4, the short message call times of the working period of the user is + the coefficient 5, and the multimedia message call times of the working period of the user is calculated;
the life circle communication index of the user is the coefficient 1, the communication frequency of the user is + the coefficient 2, the call time of the life time of the user is + the coefficient 3, the call time of the life time of the user is + the coefficient 4, the short message call time of the life time of the user is + the coefficient 5, and the multimedia message call time of the life time of the user is;
the comprehensive circle interaction index of the user is the coefficient 1, the interaction frequency of the user + the coefficient 2, the call times of the user in the undistinguished time periods + the coefficient 3, the call duration of the user in the undistinguished time periods + the coefficient 4, the short message call times of the user in the undistinguished time periods + the coefficient 5, and the multimedia message call times of the user in the undistinguished time periods;
the coefficient 1, the coefficient 2, the coefficient 3 and the coefficient 4 are set by self-definition;
taking the maximum value of the work circle interaction index, the life circle interaction index and the comprehensive circle interaction index of the user, wherein the maximum value is the interaction circle to which the user belongs;
c: establishing a user social network model based on the corresponding contact circle information of the user;
firstly, all users in a corresponding interaction circle of the users are evenly distributed into a group;
calculating the contact closeness between every two users, and determining the contact similarity between the users according to the contact closeness between every two users;
the contact closeness between every two users is calculated by the formula (I):
Figure FDA0003125203310000021
wherein, WjIs a period weight, the period weight comprises a user working period weight W1Life time interval weight W2And a non-discriminating period weight W3The time interval weight is set by self-definition; m isiIs the ith week forThe number of times of talk; diThe weight of the ith week is set by self-definition, and n is the number of weeks in the period;
attributing users with low contact similarity in the group to users with weak contact, wherein the low contact similarity is set by self-defining based on the contact closeness value among the users in the group;
dividing users with high contact similarity into a group, wherein the high contact similarity is set by self-definition based on the contact closeness value among the users in the group;
finally forming a plurality of groups with high internal interaction similarity and low external interaction similarity;
dividing the users into the corresponding groups by an iteration method for the new users in the corresponding interaction circle;
d: the family relation model is constructed based on the user life circle model, and the method comprises the following steps:
calculating the communication similarity and communication time period preference among users in the user life circle model based on the user life circle model;
and constructing a family relationship model according to the ID of the same client, the family package of the same client and the account information of the same client of the user.
2. The method of claim 1, wherein the relationship between the user and the contact circle is identified based on contact frequency and contact index, and the method comprises: the method for calculating the interaction similarity between users in the user life circle model comprises the following steps:
the communication similarity calculation module between the users stores the degree of similarity calculation, and when the communication similarity calculation module is executed by the processor, the communication similarity between the users can be obtained, and the method specifically comprises the following steps:
data are processed symmetrically and returned to the database;
according to the calling subscriber sequencing call tickets, calculating the initial position of each calling subscriber and the number of the call tickets, and determining the sequenced call tickets, the initial positions of the subscribers and the number of the call tickets for calculating the similarity;
calling the compiled communication similarity calculation module to calculate the communication similarity between users, and eliminating the call tickets with lower similarity;
and according to the communication similarity calculation among the users, searching the home network/different network users with high communication similarity and bringing the home network/different network users into the same family relationship, and constructing a family relationship model.
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