CN107368579A - Social user recommends method - Google Patents
Social user recommends method Download PDFInfo
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- CN107368579A CN107368579A CN201710598202.4A CN201710598202A CN107368579A CN 107368579 A CN107368579 A CN 107368579A CN 201710598202 A CN201710598202 A CN 201710598202A CN 107368579 A CN107368579 A CN 107368579A
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- 238000000034 method Methods 0.000 title claims abstract description 15
- 238000001914 filtration Methods 0.000 claims abstract description 6
- 239000000686 essence Substances 0.000 description 2
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000007873 sieving Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009182 swimming Effects 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
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- 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
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- 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
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Abstract
The present invention provides social user and recommends method, and this method includes：Interest tags storehouse is generated, the number of tags in the interest tags storehouse is more than 1；Pass tag libraries are generated, the number of tags of the pass tag libraries is more than 1；First user selects the first user interest label from the interest tags storehouse, and the number of tags of the first user interest label is more than or equal to 1；First user selects first user's pass labels from the pass tag libraries；Recessive label is generated to any social user, the social user is more than 1；First user obtains initial recommendation user according to the first user interest label from social user, and the initial recommendation user obtains consequently recommended user by the first user pass label filtrations.The present invention, which has, improves the function that stranger associates efficiency.
Description
Technical field
The present invention relates to social technical field, more particularly to a kind of social user recommends method.
Background technology
The software that social purpose is realized by network is social software.With the change in epoch, along with mobile mutual
The emergence of connection, there are many social softwares gradually at one's side in us.It is convenient to be provided in terms of stranger's friend-making, and social software is drawn
The distance between near social person to person, solve the problems, such as that stranger's contacts are present and obstacle.
But the software of stranger's contacts, the problem of friend-making efficiency is low be present.
The content of the invention
Inventor has found that the low factor of stranger's friend-making efficiency has, the method do not screened well, or friend-making data
In the presence of pack it is excessive the problem of.In view of this, the invention provides a kind of social user to recommend method, with least to a certain degree
Upper one of solve the problems, such as to exist.
Concrete technical scheme is as follows：
Social user recommends method, and this method includes：Interest tags storehouse is generated, the number of tags in the interest tags storehouse is more than 1；It is raw
Into pass tag libraries, the number of tags of the pass tag libraries is more than 1；First user selects the first user from the interest tags storehouse
Interest tags, the number of tags of the first user interest label are more than or equal to 1；First user is from pass tag libraries selection the
One user's pass labels；Recessive label is generated to any social user, the social user is more than 1；The first user root
Initial recommendation user is obtained from social user according to the first user interest label, and the initial recommendation user is by described the
One user's pass label filtrations obtain consequently recommended user.
Therefore, technical scheme provided by the invention can recommend have phase to user by way of the free interest tags of user
With the user of characteristic, realize that things of a kind come together, people of a mind fall into the same group things of a kind come together, people of a mind fall into the same group social essence.Excluding some additionally by pass labels oneself can not connect
The user received.By way of system assigns social user's recessiveness label automatically, solve user and selectively uncover asking for oneself
Topic.Improve social efficiency.
Embodiment
In order that the object, technical solutions and advantages of the present invention are clearer, below specific embodiment the present invention is carried out
It is described in detail.
It is to be appreciated that in the present invention, if being related to term " user " or similar vocabulary, may refer to set using electronics
Standby people or the equipment using electronic equipment.
A kind of social user that the one of embodiment of the present invention provides recommends method, and this method comprises the following steps.
Step 10:Interest tags storehouse is generated, the number of tags in the interest tags storehouse is more than 1；Generate pass tag libraries, institute
The number of tags for stating pass tag libraries is more than 1；Here interest tags storehouse, substantially it is considered that referring to the label that user likes；pass
Tag library refers to the label that user repels.
Here label refers to indicating the label of personal information, characteristic.Here the generation method of tag library can be normal
The mode of rule.Can also be that system shifts to an earlier date a series of label of typing, the later stage manually dynamically adds and subtracts label again；Can also be,
Label is generated by gathering the daily information cluster of existing user；It can also be that system automatic data collection screens network（It is popular）Data are given birth to
Into dynamic tag library.Can also be that several synthesis form.
Step 20:First user selects the first user interest label, first user interest from the interest tags storehouse
The number of tags of label is more than or equal to 1；First user selects first user's pass labels from the pass tag libraries；
User meets the label of oneself from interest tags storehouse, according to the characteristic of oneself selection, for example, can be chosen with user jazz,
The label such as swimming, singing.At least choose a label.User is from pass tag libraries, according to the characteristic selection pass marks not liked
Label, can not also be selected, such as bar can be chosen with user, is smoked, and distance is remote to wait label.
Step 30:Recessive label is generated to any social user, the social user is more than 1；
Recessive label corresponds to pass tag libraries, because general user will not actively stick the label of pass tag libraries, passes through
The mode of system generation generates.Every pass labels correspond to corresponding pass labels and preset behavior in pass tag libraries, work as appearance
The default behavior（Or the default behavior can be pre-set when reaching threshold value）When, stick the recessiveness to the social user
Label.Any pass labels and the label, which preset behavior, a mapping table, such as：" bar " this label, " bar " are corresponding
Default behavior have bar picture, appear in bar, daily record or chat and the vocabulary related to bar occur.When behavior reaches threshold value
When, then stick " bar " recessive label.
Step 40:First user obtains initial recommendation according to the first user interest label from social user
User, the initial recommendation user obtain consequently recommended user by the first user pass label filtrations.
First user obtains initial recommendation user according to the first user interest label from social user, specifically
Acquisition methods can take usual manner.
The initial recommendation user obtains consequently recommended user by the first user pass label filtrations：It is if any initial
Any recessive label of recommended user, appear in first user's pass labels, then the initial recommendation user filtering falls；Through sieving
Initial recommendation user after choosing is consequently recommended user
The present invention solves the problems, such as to improve social efficiency, and has reached the effect of the invalid recommendation of reduction.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention
God any modification, equivalent substitution and improvements done etc., should be included within the scope of protection of the invention with principle.
Claims (1)
1. a kind of social user recommends method, it is characterised in that this method includes：
Interest tags storehouse is generated, the number of tags in the interest tags storehouse is more than 1；
Pass tag libraries are generated, the number of tags of the pass tag libraries is more than 1；
First user selects the first user interest label, the number of tags of the first user interest label from the interest tags storehouse
More than or equal to 1；First user selects first user's pass labels from the pass tag libraries；
Recessive label is generated to any social user, the social user is more than 1；
First user obtains initial recommendation user according to the first user interest label from social user, described initial
Recommended user obtains consequently recommended user by the first user pass label filtrations.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710598202.4A CN107368579A (en) | 2017-07-21 | 2017-07-21 | Social user recommends method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710598202.4A CN107368579A (en) | 2017-07-21 | 2017-07-21 | Social user recommends method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107368579A true CN107368579A (en) | 2017-11-21 |
Family
ID=60308165
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710598202.4A Pending CN107368579A (en) | 2017-07-21 | 2017-07-21 | Social user recommends method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107368579A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108205586A (en) * | 2017-12-25 | 2018-06-26 | 佛山潮伊汇服装有限公司 | Efficient social contact method and efficient social device |
| CN110825888A (en) * | 2019-11-15 | 2020-02-21 | 海南大学 | Multidimensional hierarchical interaction mechanism capable of defining privacy ambiguities |
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| CN103377200A (en) * | 2012-04-17 | 2013-10-30 | 腾讯科技（深圳）有限公司 | Method and device for collecting user preference information |
| CN103810192A (en) * | 2012-11-09 | 2014-05-21 | 腾讯科技（深圳）有限公司 | User interest recommending method and device |
| CN103984775A (en) * | 2014-06-05 | 2014-08-13 | 网易（杭州）网络有限公司 | Friend recommending method and equipment |
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| US20140365484A1 (en) * | 2013-03-15 | 2014-12-11 | Daniel Freeman | Comprehensive user/event matching or recommendations based on awareness of entities, activities, interests, desires, location |
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| CN104601670A (en) * | 2014-12-25 | 2015-05-06 | 微梦创科网络科技（中国）有限公司 | Method and device for verifying interested object of user |
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| TW201725525A (en) * | 2016-01-15 | 2017-07-16 | 林慧隆 | System and Method for developing deep interpersonal social network based on supply-demand candidate recommendation |
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Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102244616A (en) * | 2010-05-14 | 2011-11-16 | 蒋斌 | Control method for processing chat friend search request in instant messenger (IM) |
| CN103377200A (en) * | 2012-04-17 | 2013-10-30 | 腾讯科技（深圳）有限公司 | Method and device for collecting user preference information |
| CN103810192A (en) * | 2012-11-09 | 2014-05-21 | 腾讯科技（深圳）有限公司 | User interest recommending method and device |
| US20140365484A1 (en) * | 2013-03-15 | 2014-12-11 | Daniel Freeman | Comprehensive user/event matching or recommendations based on awareness of entities, activities, interests, desires, location |
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| CN103984775A (en) * | 2014-06-05 | 2014-08-13 | 网易（杭州）网络有限公司 | Friend recommending method and equipment |
| CN104102722A (en) * | 2014-07-21 | 2014-10-15 | 梁朝阳 | Internet social contacting manner by searching custom tags |
| CN104462308A (en) * | 2014-11-27 | 2015-03-25 | 广东小天才科技有限公司 | Method and system for recommending friends in social network |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108205586A (en) * | 2017-12-25 | 2018-06-26 | 佛山潮伊汇服装有限公司 | Efficient social contact method and efficient social device |
| CN110825888A (en) * | 2019-11-15 | 2020-02-21 | 海南大学 | Multidimensional hierarchical interaction mechanism capable of defining privacy ambiguities |
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