CN106095987A - Community network-based content personalized pushing method and system - Google Patents

Community network-based content personalized pushing method and system Download PDF

Info

Publication number
CN106095987A
CN106095987A CN201610451961.3A CN201610451961A CN106095987A CN 106095987 A CN106095987 A CN 106095987A CN 201610451961 A CN201610451961 A CN 201610451961A CN 106095987 A CN106095987 A CN 106095987A
Authority
CN
China
Prior art keywords
user
cluster
user profile
degree
membership
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN201610451961.3A
Other languages
Chinese (zh)
Inventor
陈小燕
薛凯军
吴锐凯
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangzhou Zhongda Telecommunication Technology Co ltd
Original Assignee
Guangzhou Zhongda Telecommunication Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangzhou Zhongda Telecommunication Technology Co ltd filed Critical Guangzhou Zhongda Telecommunication Technology Co ltd
Priority to CN201610451961.3A priority Critical patent/CN106095987A/en
Publication of CN106095987A publication Critical patent/CN106095987A/en
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/01—Social networking
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/55—Push-based network services

Landscapes

  • Engineering & Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Business, Economics & Management (AREA)
  • Health & Medical Sciences (AREA)
  • Economics (AREA)
  • Human Resources & Organizations (AREA)
  • Marketing (AREA)
  • Primary Health Care (AREA)
  • Strategic Management (AREA)
  • Tourism & Hospitality (AREA)
  • General Health & Medical Sciences (AREA)
  • General Business, Economics & Management (AREA)
  • Computing Systems (AREA)
  • Signal Processing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Data Mining & Analysis (AREA)
  • General Engineering & Computer Science (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Information Transfer Between Computers (AREA)

Abstract

The invention discloses a content personalized pushing method and a system based on a community network, wherein the method comprises the following steps: acquiring user information accessing a community network, and clustering the user information to acquire a user information clustering result; acquiring the membership degree among all users in the cluster according to the user information clustering result; obtaining interest similarity among the users in the cluster according to membership degrees among the users in the cluster; and carrying out content personalized pushing on the target user according to the interest similarity among the users in the cluster. In the embodiment of the invention, the personalized recommendation of the information content is realized for the user through the community network, so that the user can obtain better use experience.

Description

A kind of content personalization method for pushing based on community network and system
Technical field
Planned network commending contents technical field of the present invention, particularly relates to a kind of content personalization based on community network and pushes away Delivery method and system.
Background technology
In community network, user is in the face of numerous numerous and diverse information and resource, obtains oneself the most quickly and easily interested Information be very important；But in the case of present community network technology high-speed development, user is difficult to get community The accurate information pushing that user is carried out by network, community network is not the most that user is interested to the information major part that user pushes Or the information wanted.
Community network analyze (social network analysis, SNA) be used for measuring actor individual and they residing for Community network member between intricate relation, to becoming the communication pattern between group members to carry out visual modeling, and have It is beneficial to the understanding to large-scale community network structure.SNA method can allow researcher " have an X-rayed " as see user in community network Between interaction, it is seen that they create interconnective form.
Summary of the invention
It is an object of the invention to overcome the deficiencies in the prior art, the invention provides a kind of content based on community network Personalized push method and system, realize user is carried out information content personalized recommendation by community network, make user obtain Preferably experience sense.
In order to solve above-mentioned technical problem, the invention provides a kind of content personalization propelling movement side based on community network Method, described method includes:
Obtain the user profile accessing community network, described user profile is carried out clustering processing, obtain user profile and gather Class result；
According to described user profile cluster result, obtain the degree of membership between each user in described cluster；
According to the degree of membership between user each in described cluster, obtain in described cluster Interest Similarity between each user；
Content personalization propelling movement is carried out to targeted customer according to Interest Similarity between user each in described cluster.
Preferably, the described user obtaining access community network, including:
Obtain the user profile of all users accessing described community network；Described user profile at least includes that user accesses Any one in daily record or user interest content；
In the way of the restriction of access time, described user profile is filtered, obtain nearest 1 year and access described community network The user profile of network.
Preferably, described described user profile is carried out clustering processing, including:
Described user profile is carried out information retrieval process, obtains and user profile at least includes user name, Yong Hufang Ask daily record and user interest；
Carry out clustering processing according to the user name in described user profile, user access logs and user interest, obtain and use Family information cluster result.
Preferably, described according to described user profile cluster result, obtain the degree of membership between each user in described cluster, Including:
Determine the cluster centre of described user profile cluster result, obtain the user assembled around described cluster centre；
Use user's Subject Matrix that the user assembled around described cluster centre is processed, obtain each user in cluster The degree of membership at center.
Preferably, according to the degree of membership between user each in described cluster, obtain in described cluster interest between each user Similarity, including:
Obtain in described cluster degree of membership weight between each user, use in cluster described in described degree of membership weight calculation each Interest Similarity between user.
It addition, the embodiment of the present invention additionally provides a kind of content personalization supplying system based on community network, described system System includes:
Cluster module: for obtaining the user profile accessing community network, described user profile is carried out clustering processing, obtains Take family information cluster result；
Degree of membership acquisition module: for according to described user profile cluster result, obtains in described cluster between each user Degree of membership；
Interest Similarity acquisition module: for according to the degree of membership between user each in described cluster, obtain described cluster In Interest Similarity between each user；
Pushing module: for carrying out content individual character according to Interest Similarity between user each in described cluster to targeted customer Change and push.
Preferably, described cluster module includes:
Information acquisition unit: for obtaining the user profile of all users accessing described community network；Described user believes Breath at least includes any one in user access logs or user interest content；
Information filtering unit: for described user profile being filtered in the way of the restriction of access time, obtain recently The user profile accessing described community network in 1 year.
Preferably, described cluster module also includes:
Information extraction unit: for described user profile being carried out information retrieval process, in acquisition user profile at least Including user name, user access logs and user interest；
Cluster cell: for gathering according to the user name in described user profile, user access logs and user interest Class processes, and obtains user profile cluster result.
Preferably, described degree of membership acquisition module includes:
Cluster centre determines unit: for determining the cluster centre of described user profile cluster result, obtain described cluster The user assembled around center；
Degree of membership acquiring unit: for using at user's Subject Matrix user to assembling around described cluster centre Reason, obtains each user degree of membership at cluster centre.
Preferably, described Interest Similarity acquisition module includes:
Computing unit: be used for obtaining in described cluster degree of membership weight between each user, use described degree of membership weight meter Calculate in described cluster Interest Similarity between each user.
In embodiments of the present invention, by modes such as user clusterings, calculate in this cluster the interest between user similar Degree, carries out information pushing according to Interest Similarity between user in this cluster to user；Community network can realize to Family carries out information content personalized recommendation, makes user obtain preferable experience sense.
Accompanying drawing explanation
In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing In having technology to describe, the required accompanying drawing used is briefly described, it is clear that, the accompanying drawing in describing below is only this Some embodiments of invention, for those of ordinary skill in the art, on the premise of not paying creative work, it is also possible to Other accompanying drawing is obtained according to these accompanying drawings.
Fig. 1 is the method flow signal of the content personalization method for pushing based on community network in the embodiment of the present invention Figure；
Fig. 2 is that the system structure composition of the content personalization supplying system based on community network in the embodiment of the present invention shows It is intended to.
Detailed description of the invention
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete Describe, it is clear that described embodiment is only a part of embodiment of the present invention rather than whole embodiments wholely.Based on Embodiment in the present invention, those of ordinary skill in the art obtained under not making creative work premise all other Embodiment, broadly falls into the scope of protection of the invention.
Fig. 1 is the method flow signal of the content personalization method for pushing based on community network in the embodiment of the present invention Figure, as it is shown in figure 1, the method includes:
S11: obtain the user profile accessing community network, carries out clustering processing to this user profile, obtains user profile Cluster result；
S123: according to this user profile cluster result, obtains the degree of membership between each user in this cluster；
S13: according to the degree of membership between user each in this cluster, obtains in this cluster Interest Similarity between each user；
S14: carry out content personalization propelling movement to targeted customer according to Interest Similarity between user each in this cluster.
S11 is further illustrated:
Obtain the user profile of all users accessing this community network；This user profile at least includes user access logs Or any one in user interest content；In the way of the restriction of access time, this user profile is filtered, obtain recently The user profile accessing described community network in 1 year.
This user profile is carried out information retrieval process, obtains and user profile at least includes that user name, user access Daily record and user interest；Carry out clustering processing according to the user name in this user profile, user access logs and user interest, obtain Take family information cluster result.
Further, by the way of administrator right or other, data base in community network is processed, obtain Access the user profile of all users of this community network；In this user profile at least includes user access logs or user interest Any one in appearance；After obtaining the user profile of all users accessing this community network, these user profile are entered These users, to access the time sorted lists for order, are carried out in the way of the restriction of access time by row according to this sorted lists Filtering user information, accessing limiting time can set according to the demand of user, uses in embodiments of the present invention 1 year Interval time carries out filtering user information as filtration time, thus gets the nearest user's letter accessing this community network for a year Breath.
Carry out information separation to filtering the user profile got, user profile is at least separated into user name, Yong Hufang Ask the information such as daily record and user interest, then these user profile are carried out extraction process, obtain at least bag in user profile Include user name, user access logs and user interest.Finally according to the user name in this user profile, user access logs and use Family interest carries out clustering processing, obtains user clustering result；Because clustering algorithm is more, including K-MEANS, K-MEDOIDS, The clustering algorithms such as Clara and Clarans, in the present embodiment, use K-MEANS clustering algorithm, the base of K-MEANS clustering algorithm This thought is: sort out as center with k object in space, in object space near each center object respectively It is classified as a class, by the way of successive ignition, the value of each cluster barycenter is gradually calculated renewal, until clustering barycenter the most not Become.
S12 is described further:
Determine the cluster centre of this user profile cluster result, obtain the user assembled around this cluster centre；Use and use The user assembled around described cluster centre is processed by family Subject Matrix, obtains each user degree of membership at cluster centre.
Further, higher according to user access logs, user interest similarity each other after having clustered User as cluster centre, and according to this cluster centre centered by, the situation of user around, thus obtain in this cluster The user assembled around the heart；Initialising subscriber Subject Matrix, uses the random number between 0 to 1 to carry out initialising subscriber and is subordinate to square Battle array U={ul,u2,…,un, wherein uj=(u1j,u2j..., ucj) T, uijRepresent certain user j degree of membership at the i-th apoplexy due to endogenous wind, thus Obtain a user mutual degree of membership at cluster centre.
S13 is described further:
According to the degree of membership between user each in this cluster, obtain in this cluster between each user Interest Similarity specifically Obtain in this cluster degree of membership weight between each user, use in this this cluster of degree of membership weight calculation interest phase between each user Like degree.
Further, it is converted in this cluster degree of membership weight between each user according to degree of membership and then uses this degree of membership Interest Similarity between user in this cluster of weight calculation, the mode using degree of membership to be multiplied by a coefficient calculates this cluster In Interest Similarity between user；Depending on this coefficient can be according to user's actual need.
S14 is described further:
By analyzing in targeted customer and cluster the Interest Similarity between each user, will be in targeted customer's Interest Similarity Information or web content that higher user is interested carry out personalized push to targeted customer.
Fig. 2 is that the system structure composition of the content personalization supplying system based on community network in the embodiment of the present invention shows It is intended to, as in figure 2 it is shown, this system includes:
Cluster module 11: for obtaining the user profile accessing community network, this user profile is carried out clustering processing, obtains Take family information cluster result；
Degree of membership acquisition module 12: for according to this user profile cluster result, obtains in this cluster between each user Degree of membership；
Interest Similarity acquisition module 13: for according to the degree of membership between user each in this cluster, obtain in this cluster Interest Similarity between each user；
Pushing module 14: for carrying out content individual character according to Interest Similarity between user each in this cluster to targeted customer Change and push.
Preferably, this cluster module 11 includes:
Information acquisition unit: for obtaining the user profile of all users accessing this community network；This user profile is extremely Include any one in user access logs or user interest content less；
Information filtering unit: for this user profile being filtered in the way of the restriction of access time, obtain nearest one Year accesses the user profile of this community network.
Preferably, this cluster module 11 also includes:
Information extraction unit: for this user profile being carried out information retrieval process, obtain at least bag in user profile Include user name, user access logs and user interest；
Cluster cell: for clustering according to the user name in this user profile, user access logs and user interest Process, obtain user profile cluster result.
Preferably, this degree of membership acquisition module 12 includes:
Cluster centre determines unit: for determining the cluster centre of this user profile cluster result, obtain this cluster centre The user around assembled；
Degree of membership acquiring unit: for using at user's Subject Matrix user to assembling around this cluster centre Reason, obtains each user degree of membership at cluster centre.
Preferably, this Interest Similarity acquisition module 13 includes:
Computing unit: be used for obtaining in this cluster degree of membership weight between each user, uses this degree of membership weight calculation should Interest Similarity between each user in cluster.
Specifically, the operation principle of the system related functions module of the embodiment of the present invention can be found in the relevant of embodiment of the method Describe, repeat no more here.
In embodiments of the present invention, by modes such as user clusterings, calculate in this cluster the interest between user similar Degree, carries out information pushing according to Interest Similarity between user in this cluster to user；Community network can realize to Family carries out information content personalized recommendation, makes user obtain preferable experience sense.
One of ordinary skill in the art will appreciate that all or part of step in the various methods of above-described embodiment is can Completing instructing relevant hardware by program, this program can be stored in a computer-readable recording medium, storage Medium may include that read only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), disk or CD etc..
It addition, above a kind of based on community network the content personalization method for pushing that the embodiment of the present invention is provided and System is described in detail, and should have employed specific case herein and be set forth principle and the embodiment of the present invention, The explanation of above example is only intended to help to understand method and the core concept thereof of the present invention；Simultaneously for this area one As technical staff, according to the thought of the present invention, the most all will change, to sum up institute Stating, this specification content should not be construed as limitation of the present invention.

Claims (10)

1. a content personalization method for pushing based on community network, it is characterised in that described method includes:
Obtain the user profile accessing community network, described user profile is carried out clustering processing, obtain user profile cluster knot Really；
According to described user profile cluster result, obtain the degree of membership between each user in described cluster；
According to the degree of membership between user each in described cluster, obtain in described cluster Interest Similarity between each user；
Content personalization propelling movement is carried out to targeted customer according to Interest Similarity between user each in described cluster.
Content personalization method for pushing the most according to claim 1, it is characterised in that described acquisition accesses community network User, including:
Obtain the user profile of all users accessing described community network；Described user profile at least includes user access logs Or any one in user interest content；
In the way of the restriction of access time, described user profile is filtered, obtain nearest 1 year and access described community network User profile.
Content personalization method for pushing the most according to claim 1, it is characterised in that described described user profile is carried out Clustering processing, including:
Described user profile is carried out information retrieval process, obtains and user profile at least includes that user name, user access day Will and user interest；
Carry out clustering processing according to the user name in described user profile, user access logs and user interest, obtain user's letter Breath cluster result.
Content personalization method for pushing the most according to claim 1, it is characterised in that described poly-according to described user profile Class result, obtains the degree of membership between each user in described cluster, including:
Determine the cluster centre of described user profile cluster result, obtain the user assembled around described cluster centre；
Use user's Subject Matrix that the user assembled around described cluster centre is processed, obtain each user at cluster centre Degree of membership.
Content personalization method for pushing the most according to claim 1, it is characterised in that according to user each in described cluster it Between degree of membership, obtain in described cluster Interest Similarity between each user, including:
Obtain in described cluster degree of membership weight between each user, use each user in cluster described in described degree of membership weight calculation Between Interest Similarity.
6. a content personalization supplying system based on community network, it is characterised in that described system includes:
Cluster module: for obtaining the user profile accessing community network, described user profile is carried out clustering processing, obtain and use Family information cluster result；
Degree of membership acquisition module: for according to described user profile cluster result, obtain the person in servitude between each user in described cluster Genus degree；
Interest Similarity acquisition module: for according to the degree of membership between user each in described cluster, obtain in described cluster each Interest Similarity between user；
Pushing module: push away for carrying out content personalization according to Interest Similarity between user each in described cluster to targeted customer Send.
Content personalization supplying system the most according to claim 6, it is characterised in that described cluster module includes:
Information acquisition unit: for obtaining the user profile of all users accessing described community network；Described user profile is extremely Include any one in user access logs or user interest content less；
Information filtering unit: for described user profile being filtered in the way of the restriction of access time, obtain nearest 1 year Access the user profile of described community network.
Content personalization supplying system the most according to claim 6, it is characterised in that described cluster module also includes:
Information extraction unit: for described user profile being carried out information retrieval process, obtain at least including in user profile User name, user access logs and user interest；
Cluster cell: for carrying out at cluster according to the user name in described user profile, user access logs and user interest Reason, obtains user profile cluster result.
Content personalization supplying system the most according to claim 6, it is characterised in that described degree of membership acquisition module bag Include:
Cluster centre determines unit: for determining the cluster centre of described user profile cluster result, obtain described cluster centre The user around assembled；
Degree of membership acquiring unit: for using user's Subject Matrix that the user assembled around described cluster centre is processed, Obtain each user degree of membership at cluster centre.
Content personalization supplying system the most according to claim 6, it is characterised in that described Interest Similarity obtains mould Block includes:
Computing unit: be used for obtaining in described cluster degree of membership weight between each user, use described degree of membership weight calculation institute State in cluster Interest Similarity between each user.
CN201610451961.3A 2016-06-20 2016-06-20 Community network-based content personalized pushing method and system Pending CN106095987A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201610451961.3A CN106095987A (en) 2016-06-20 2016-06-20 Community network-based content personalized pushing method and system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201610451961.3A CN106095987A (en) 2016-06-20 2016-06-20 Community network-based content personalized pushing method and system

Publications (1)

Publication Number Publication Date
CN106095987A true CN106095987A (en) 2016-11-09

Family

ID=57238085

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201610451961.3A Pending CN106095987A (en) 2016-06-20 2016-06-20 Community network-based content personalized pushing method and system

Country Status (1)

Country Link
CN (1) CN106095987A (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109408733A (en) * 2018-09-26 2019-03-01 西安理工大学 A kind of diversified interest community method for building up based on VR environment
CN109978075A (en) * 2019-04-04 2019-07-05 江苏满运软件科技有限公司 Vehicle dummy location information identifying method, device, electronic equipment, storage medium
CN110209931A (en) * 2019-05-17 2019-09-06 腾讯科技（深圳）有限公司 Method for pushing and device, storage medium, the electronic device of media content

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102750647A (en) * 2012-06-29 2012-10-24 南京大学 Merchant recommendation method based on transaction network
CN104462383A (en) * 2014-12-10 2015-03-25 山东科技大学 Movie recommendation method based on feedback of users' various behaviors

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102750647A (en) * 2012-06-29 2012-10-24 南京大学 Merchant recommendation method based on transaction network
CN104462383A (en) * 2014-12-10 2015-03-25 山东科技大学 Movie recommendation method based on feedback of users' various behaviors

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109408733A (en) * 2018-09-26 2019-03-01 西安理工大学 A kind of diversified interest community method for building up based on VR environment
CN109978075A (en) * 2019-04-04 2019-07-05 江苏满运软件科技有限公司 Vehicle dummy location information identifying method, device, electronic equipment, storage medium
CN109978075B (en) * 2019-04-04 2021-09-28 江苏满运软件科技有限公司 Vehicle false position information identification method and device, electronic equipment and storage medium
CN110209931A (en) * 2019-05-17 2019-09-06 腾讯科技（深圳）有限公司 Method for pushing and device, storage medium, the electronic device of media content
CN110209931B (en) * 2019-05-17 2023-08-25 腾讯科技（深圳）有限公司 Media content pushing method and device, storage medium and electronic device

Similar Documents

Publication Publication Date Title
Liu et al. Detecting community structure in complex networks using simulated annealing with k-means algorithms
CN105005589B (en) A kind of method and apparatus of text classification
CN108595585A (en) Sample data sorting technique, model training method, electronic equipment and storage medium
CN103049496B (en) A kind of multiple users are carried out the method for customer group division, device and equipment
CN105874753A (en) Systems and methods for behavioral segmentation of users in a social data network
CN102880719A (en) User trajectory similarity mining method for location-based social network
CN109376610B (en) Pedestrian unsafe behavior detection method based on image concept network in video monitoring
CN111866196B (en) Domain name traffic characteristic extraction method, device and equipment and readable storage medium
CN113904872B (en) Feature extraction method and system for anonymous service website fingerprint attack
CN106776928A (en) Recommend method in position based on internal memory Computational frame, fusion social environment and space-time data
CN109871504A (en) A kind of Course Recommendation System based on Heterogeneous Information network and deep learning
CN106095987A (en) Community network-based content personalized pushing method and system
CN110348494A (en) A kind of human motion recognition method based on binary channels residual error neural network
CN110516533A (en) A kind of pedestrian based on depth measure discrimination method again
CN109949174A (en) A kind of isomery social network user entity anchor chain connects recognition methods
Han et al. Linking social network accounts by modeling user spatiotemporal habits
Zhang et al. Multi-view clustering of microbiome samples by robust similarity network fusion and spectral clustering
CN108830106A (en) A kind of private data providing method and device
CN106649380A (en) Hot spot recommendation method and system based on tag
Zhou et al. Using global t-SNE to preserve intercluster data structure
CN113704373A (en) User identification method and device based on movement track data and storage medium
Wang et al. Action recognition using linear dynamic systems
CN108647739A (en) A kind of myspace discovery method based on improved density peaks cluster
CN105975897A (en) Image human face gender recognition method and system
CN114970684A (en) Community detection method for extracting network core structure by combining VAE

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20161109