CN110517114A - A kind of information-pushing method and system based on community discovery algorithm - Google Patents
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Abstract
The invention discloses a kind of information-pushing method and system based on community discovery algorithm, comprising steps of being acquired to user data, the user data includes the behavioral data and purchase data of user；It is analyzed using behavioral data of the label propagation algorithm to user, obtains interpersonal relationships community；The preference of customer group in community is obtained using clustering algorithm；Commodity are matched according to the preference of customer group in community；The client for having bought the commodity to be recommended in the recent period is rejected；Finally, precisely being pushed to the customer group in community.Information-pushing method and system through the invention can position objective group and demand, accurately market, reach raising marketing effectiveness, and reduce cost of marketing in transverse direction by finding similar community, and then according to the similar logic of similar Community demand.
Description
Technical field
The present invention relates to data processing field, in particular to a kind of information-pushing method based on community discovery algorithm.
Background technique
Precision marketing is exactly to rely on modern information technology means to establish personalized customer's ditch on the basis of precise positioning
Logical service system, realizes the road of the mensurable expansion in low cost of enterprise.And precision marketing must be by accurately user data, so
And with the rapid development of Internet, information " huge explosion " is faced with information service field, " information resources are abundant, but obtain
Have the information of utility value difficult " the problem of.Recommender system is the main means for solving problem of information overload, it can be with user
Centered on actively push it to user on the basis of user demand is predicted in analysis and may need but be difficult to the information obtained, lead to
The behavioural characteristic crossed under the varying environment occasion according to user is that user recommends to have more the information resources of utility value.
Social networks connects the people not known each other mutually even with identical hobby by internet, to be formed
Has the characteristics that a certain group.Social networks is the platform that can be communicated with each other and exchange and can participate in interaction, is led to
Cross social networks, the characteristics of people understand voluntary publication oneself and preference, actively provide various resources (such as picture, video) or point
Enjoy their knowledge.It can be said that social networks just silently change people's lives mode and value orientation.
But existing recommender system is analyzed according to the behavioral data of independent part, is usually searched from user
Rope, browsing the enterprising product of doing business of behavior recommendation, what " is looked for " with client, just what this longitudinal mode is sentenced to lead referral
It is disconnected, customer group can not be expanded, causes marketing efficiency low.
Summary of the invention
In order to overcome the above technical problems, a kind of information-pushing method and system based on community discovery algorithm is provided, it can
To position objective group by finding similar community, and then according to the similar logic of similar Community demand in transverse direction and need
It asks, accurately markets, reach raising marketing effectiveness, and reduce cost of marketing.
In order to achieve the above-mentioned object of the invention, the present invention provides following technical schemes:
A kind of information-pushing method based on community discovery algorithm, comprising the following steps:
Step 1, user data is acquired, the user data includes the behavioral data and purchase data of user；
Step 2, it is analyzed using behavioral data of the label propagation algorithm to user, obtains interpersonal relationships community；
Step 3, the preference of customer group in community is obtained using clustering algorithm；
Step 4, commodity are matched according to the preference of customer group in community；
Step 5, the client for having bought the commodity to be recommended in the recent period is rejected；
Step 6, finally, precisely being pushed to the customer group in community.
Preferably, the label propagation algorithm is a unique label to be specified first for all nodes, later by wheel
The label for refreshing all nodes, until reaching convergent requirement；Each round is refreshed, node label refreshes, for a certain
A node, investigates the label of its all neighbor node, and is counted, and that the largest number of label will occurs and is assigned to work as prosthomere
Point；When the largest number of labels are not unique, a label is selected to be assigned to present node at random.
Preferably, further include user's similarity calculation step, calculated by forwarding similarity algorithm emerging between two users
The similarity of interest, the forwarding calculating formula of similarity between user p and q are as follows:
Wherein com (p, q) retweet refers to the content quantity that user p and q are forwarded jointly, | Dp |, | Dq | refer in user p, q forwarding
Hold quantity, mpq, mqp refer to the number that user p, q are mutually forwarded.
Preferably, the clustering algorithm are as follows:
Step-1 arbitrarily selects k object as initial cluster center from n data object first;
Step-2, according to the mean value of each clustering object, i.e. center object, calculate each object and these center objects away from
From；And corresponding object is divided again according to minimum range;
Step-3 recalculates the mean value of each cluster;
Step-4, circulation (2) to (3) is until each cluster is no longer changed.
A kind of information transmission system based on community discovery algorithm, the system include:
Data acquisition unit is acquired user data, and the user data includes the behavioral data and shopping number of user
According to；
Interpersonal relationships community confirmation unit is analyzed using behavioral data of the label propagation algorithm to user, obtains interpersonal pass
It is community；
Preference confirmation unit obtains the preference of customer group in community using clustering algorithm；
Goods matching unit matches commodity according to the preference of customer group in community
Culling unit rejects the client for having bought the commodity to be recommended in the recent period；
Information push unit precisely pushes the customer group in community.
Preferably, the label propagation algorithm is a unique label to be specified first for all nodes, later by wheel
The label for refreshing all nodes, until reaching convergent requirement；Each round is refreshed, node label refreshes, for a certain
A node, investigates the label of its all neighbor node, and is counted, and that the largest number of label will occurs and is assigned to work as prosthomere
Point；When the largest number of labels are not unique, a label is selected to be assigned to present node at random.
Preferably, further include user's similarity calculated, calculated by forwarding similarity algorithm emerging between two users
The similarity of interest, the forwarding calculating formula of similarity between user p and q are as follows:
Wherein com (p, q) retweet refers to the content quantity that user p and q are forwarded jointly, | Dp |, | Dq | refer in user p, q forwarding
Hold quantity, mpq, mqp refer to the number that user p, q are mutually forwarded.
Preferably, the clustering algorithm are as follows:
Step-1 arbitrarily selects k object as initial cluster center from n data object first;
Step-2, according to the mean value of each clustering object, i.e. center object, calculate each object and these center objects away from
From；And corresponding object is divided again according to minimum range;
Step-3 recalculates the mean value of each cluster;
Step-4, circulation (2) to (3) is until each cluster is no longer changed.
Compared with prior art, the invention has the following beneficial effects:
Existing technology is usually the recommendation of the enterprising product of doing business of behavior from search, browsing, what " is looked for " with client, just gives client
Any this longitudinal mode is recommended to judge that the information-pushing method based on community discovery algorithm through the invention can be in cross
Objective group and demand are positioned, precisely by finding similar community, and then according to the similar logic of similar Community demand to direction
Marketing, reach raising marketing effectiveness, and reduce cost of marketing.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below will be to needed in the embodiment attached
Figure is briefly described, it should be understood that the following drawings illustrates only certain embodiments of the present invention, therefore is not construed as pair
The restriction of range for those of ordinary skill in the art without creative efforts, can also be according to this
A little attached drawings obtain other relevant attached drawings.
Fig. 1, a kind of process signal for information-pushing method based on community discovery algorithm that the embodiment of the present invention 1 proposes
Figure；
Fig. 2, a kind of equipment schematic diagram for information transmission system based on community discovery algorithm that the embodiment of the present invention 2 proposes；
Fig. 3, Web Community's schematic diagram.
Specific embodiment
Hereinafter, the various embodiments of the disclosure will be described more fully.The disclosure can have various embodiments, and
It can adjust and change wherein.It should be understood, however, that: there is no the various embodiments of the disclosure are limited to spy disclosed herein
Determine the intention of embodiment, but the disclosure should be interpreted as in the spirit and scope for covering the various embodiments for falling into the disclosure
All adjustment, equivalent and/or optinal plan.
Embodiment 1
The embodiment of the present invention 1 proposes a kind of information-pushing method based on community discovery algorithm, comprising the following steps:
Step 1, user data is acquired, the user data includes the behavioral data and purchase data of user；
Step 2, it is analyzed using behavioral data of the label propagation algorithm to user, obtains interpersonal relationships community；
Step 3, the preference of customer group in community is obtained using clustering algorithm；
Step 4, commodity are matched according to the preference of customer group in community；
Step 5, the client for having bought the commodity to be recommended in the recent period is rejected；
Step 6, finally, precisely being pushed to the customer group in community.
Label propagation algorithm (LPA): specifying a unique label first for all nodes, refreshes all sections by wheel later
The label of point, until reaching convergent requirement.Each round is refreshed, node label refreshes, and for some node, investigates
The label of its all neighbor node, and counted, that the largest number of label will occur and is assigned to present node.When number most
When more labels is not unique, one is selected at random.
Forward similarity algorithm: if the content that a user forwards some user is interested, he can be checked
This content (information/commodity/activity) even forwards this content, agrees to another one user's in this way to express him
Opinion transmits a kind of information.If a user frequently checks or forwards another client to pass to his content,
It can so illustrate that the two users have certain interest similarity；Therefore, it is further to propose that forwarding similitude is come by the present invention
Calculate the similitude between user.
Forwarding calculating formula of similarity between user p and q is as follows:
Wherein com (p, q) retweet refers to the content quantity that user p and q are forwarded jointly, | Dq | refer to that the user's of user q forwarding is interior
Hold quantity, mpq refers to the number of user p forwarding user q.
Clustering algorithm: arbitrarily select k object as initial cluster center from n data object first；And for institute
It is left other objects, then according to the similarity (distance) of they and these cluster centres, assigns these to respectively and its most phase
As cluster (representated by cluster centre)；Then calculate that each to obtain the cluster centre newly clustered (all right in the cluster again
The mean value of elephant)；This process is constantly repeated until canonical measure function starts convergence.Mean square deviation is generally used as mark
Quasi- measure function.K cluster has the following characteristics that each cluster itself is compact as far as possible, and divides as far as possible between respectively clustering
It opens.
Algorithm flow:
Step-1: arbitrarily select k object as initial cluster center from n data object first；
Step-2: according to the mean value (center object) of each clustering object, each object is calculated at a distance from these center objects；
And corresponding object is divided again according to minimum range；
Step-3: the mean value (center object) of each (changing) cluster is recalculated；
Step-4: circulation (2) to (3) is until each cluster is no longer changed.
Embodiment 2
The embodiment of the present invention 2 proposes a kind of information transmission system based on community discovery algorithm, comprising:
Data acquisition unit is acquired user data, and the user data includes the behavioral data and shopping number of user
According to；
Interpersonal relationships community confirmation unit is analyzed using behavioral data of the label propagation algorithm to user, obtains interpersonal pass
It is community；
Preference confirmation unit obtains the preference of customer group in community using clustering algorithm；
Goods matching unit matches commodity according to the preference of customer group in community
Culling unit rejects the client for having bought the commodity to be recommended in the recent period；
Information push unit precisely pushes the customer group in community.
Label propagation algorithm (LPA): specifying a unique label first for all nodes, refreshes all sections by wheel later
The label of point, until reaching convergent requirement.Each round is refreshed, node label refreshes, and for some node, investigates
The label of its all neighbor node, and counted, that the largest number of label will occur and is assigned to present node.When number most
When more labels is not unique, one is selected at random.
Forward similarity algorithm: if the content that a user forwards some user is interested, he can be checked
This content (information/commodity/activity) even forwards this content, agrees to another one user's in this way to express him
Opinion transmits a kind of information.If a user frequently checks or forwards another client to pass to his content,
It can so illustrate that the two users have certain interest similarity；Therefore, it is further to propose that forwarding similitude is come by the present invention
Calculate the similitude between user.
Forwarding calculating formula of similarity between user p and q is as follows:
Wherein com (p, q) retweet refers to the content quantity that user p and q are forwarded jointly, | Dq | refer to that the user's of user q forwarding is interior
Hold quantity, mpq refers to the number of user p forwarding user q.
Clustering algorithm: arbitrarily select k object as initial cluster center from n data object first；And for institute
It is left other objects, then according to the similarity (distance) of they and these cluster centres, assigns these to respectively and its most phase
As cluster (representated by cluster centre)；Then calculate that each to obtain the cluster centre newly clustered (all right in the cluster again
The mean value of elephant)；This process is constantly repeated until canonical measure function starts convergence.Mean square deviation is generally used as mark
Quasi- measure function.K cluster has the following characteristics that each cluster itself is compact as far as possible, and divides as far as possible between respectively clustering
It opens.
Algorithm flow:
Step-1: arbitrarily select k object as initial cluster center from n data object first；
Step-2: according to the mean value (center object) of each clustering object, each object is calculated at a distance from these center objects；
And corresponding object is divided again according to minimum range；
Step-3: the mean value (center object) of each (changing) cluster is recalculated；
Step-4: circulation (2) to (3) is until each cluster is no longer changed.
The above description is only a preferred embodiment of the present invention, and it cannot be said that specific implementation of the invention is confined to these says
It is bright.It, without departing from the inventive concept of the premise, can be with for the related technical personnel of the technical field of the invention
Several simple deduction or replace are made, wherein any modification, equivalent replacement, improvement and so on, should be included in of the invention
Within protection scope.
Claims (8)
1. a kind of information-pushing method based on community discovery algorithm, comprising the following steps:
Step 1, user data is acquired, the user data includes the behavioral data and purchase data of user；
Step 2, it is analyzed using behavioral data of the label propagation algorithm to user, obtains interpersonal relationships community；
Step 3, the preference of customer group in community is obtained using clustering algorithm；
Step 4, commodity are matched according to the preference of customer group in community；
Step 5, the client for having bought the commodity to be recommended in the recent period is rejected；
Step 6, finally, precisely being pushed to the customer group in community.
2. the information-pushing method as described in claim 1 based on community discovery algorithm, the label propagation algorithm are, first
A unique label first is specified for all nodes, refreshes the label of all nodes by wheel later, is until reaching convergent requirement
Only；Each round is refreshed, node label refreshes, and for some node, investigates the label of its all neighbor node, and carry out
Statistics, that the largest number of label will occurs and is assigned to present node；When the largest number of labels are not unique, one is selected at random
Label is assigned to present node.
3. the information-pushing method as described in claim 1 based on community discovery algorithm further includes user's similarity calculation step
Suddenly, the similarity that interest between two users is calculated by forwarding similarity algorithm, the forwarding similarity meter between user p and q
It is as follows to calculate formula:
Wherein com (p, q) retweet refers to the content quantity that user p and q are forwarded jointly, | Dp |, | Dq | refer in user p, q forwarding
Hold quantity, mpq, mqp refer to the number that user p, q are mutually forwarded.
4. the information-pushing method as described in claim 1 based on community discovery algorithm, the clustering algorithm are as follows:
Step-1 arbitrarily selects k object as initial cluster center from n data object first;
Step-2, according to the mean value of each clustering object, i.e. center object, calculate each object and these center objects away from
From；And corresponding object is divided again according to minimum range;
Step-3 recalculates the mean value of each cluster;
Step-4, Cyclic Rings step-2 to step-3 are until each cluster is no longer changed.
5. a kind of information transmission system based on community discovery algorithm, comprising the following steps:
Data acquisition unit is acquired user data, and the user data includes the behavioral data and shopping number of user
According to；
Interpersonal relationships community confirmation unit is analyzed using behavioral data of the label propagation algorithm to user, obtains interpersonal pass
It is community；
Preference confirmation unit obtains the preference of customer group in community using clustering algorithm；
Goods matching unit matches commodity according to the preference of customer group in community
Culling unit rejects the client for having bought the commodity to be recommended in the recent period；
Information push unit precisely pushes the customer group in community.
6. the information transmission system as described in claim 1 based on community discovery algorithm, the label propagation algorithm are, first
A unique label first is specified for all nodes, refreshes the label of all nodes by wheel later, is until reaching convergent requirement
Only；Each round is refreshed, node label refreshes, and for some node, investigates the label of its all neighbor node, and carry out
Statistics, that the largest number of label will occurs and is assigned to present node；When the largest number of labels are not unique, one is selected at random
Label is assigned to present node.
7. the information transmission system as described in claim 1 based on community discovery algorithm further includes user's similarity calculation list
Member calculates the similarity of interest between two users, the forwarding similarity meter between user p and q by forwarding similarity algorithm
It is as follows to calculate formula:
Wherein com (p, q) retweet refers to the content quantity that user p and q are forwarded jointly, | Dp |, | Dq | refer in user p, q forwarding
Hold quantity, mpq, mqp refer to the number that user p, q are mutually forwarded.
8. the information transmission system as described in claim 1 based on community discovery algorithm, the clustering algorithm are as follows:
Step-1 arbitrarily selects k object as initial cluster center from n data object first;
Step-2, according to the mean value of each clustering object, i.e. center object, calculate each object and these center objects away from
From；And corresponding object is divided again according to minimum range;
Step-3 recalculates the mean value of each cluster;
Step-4, circulation step-2 to step-3 are until each cluster is no longer changed.
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| CN111340601A (en) * | 2020-03-30 | 2020-06-26 | 贝壳技术有限公司 | Commodity information recommendation method and device, electronic equipment and storage medium |
| CN112070600A (en) * | 2020-09-03 | 2020-12-11 | 中国银行股份有限公司 | System and method for establishing overseas mutual aid sharing circle based on bank cross-border service |
| CN112070600B (en) * | 2020-09-03 | 2023-09-22 | 中国银行股份有限公司 | System and method for establishing overseas mutual sharing circle based on bank cross-border service |
| CN112184303A (en) * | 2020-09-25 | 2021-01-05 | 中国建设银行股份有限公司 | Target information pushing method and device based on clustering algorithm and storage medium |
| CN112381598A (en) * | 2020-10-26 | 2021-02-19 | 泰康保险集团股份有限公司 | Product service information pushing method and device |
| CN112381598B (en) * | 2020-10-26 | 2023-12-05 | 泰康保险集团股份有限公司 | Product service information pushing method and device |
| CN113610608A (en) * | 2021-08-19 | 2021-11-05 | 创优数字科技(广东)有限公司 | User preference recommendation method and device, electronic equipment and storage medium |
| CN114387064A (en) * | 2022-01-13 | 2022-04-22 | 福州大学 | E-commerce platform potential customer recommendation method and system based on comprehensive similarity |
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