CN104835072A - 社交网络中用户的兼容性评分 - Google Patents
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Abstract
本申请涉及社交网络中用户的兼容性评分。根据社交网络中的个体所表示的兴趣的兼容性计算这些个体的兼容性得分。任何两个兴趣之间的兼容性得分被计算为所述社交网络的成员将两个兴趣表示为其兴趣的所估计概率除以(i)所述社交网络的成员将所述两个兴趣中的第一兴趣表示为其兴趣的所估计概率和(ii)所述社交网络的成员将所述两个兴趣中的第二兴趣表示为其兴趣的所估计概率的乘积的对数。两个个体之间的兼容性得分被计算为由第一个体表示的一组兴趣中所出现的每一兴趣与由第二个体表示的一组兴趣中所出现的每一兴趣之间的兼容性得分的总和。
Description
分案申请的相关信息
本申请为发明名称为“社交网络中用户的兼容性评分”的原中国发明专利申请的分案申请。原申请的申请号为200680020711.9;原申请的申请日为2006年4月27日。
技术领域
一般来说,本发明涉及社交网络数据的处理,且更特定来说,涉及一种在线社交网络的成员之间的评分兼容性的方法。
背景技术
目前数种在线约会及交友网站正在因特网上运作。这些服务通常在功能上是类似的。其允许用户张贴简档及照片,以及搜索其他用户的简档及照片。由于通过笔名识别用户,故匿名地提供用户之间的通信。
最初,这些网站曾实施基本技术来匹配用户。这些技术仅仅相当于根据例如年龄、性别、位置及身体特征等标准的用户简档搜索。新近,这些网站已实施更完善的过程以努力为其用户找到更佳的匹配。这种过程尝试根据特别设计的测试或问卷评估个体的性格且找到具有兼容性格的用户。
发明内容
本发明将由两个个体(其为社交网络的成员)所表示的兴趣的兼容性作为所述个体的兼容性的基础,且提供用于量化兴趣兼容性、依据由所述个体所表示兴趣兼容性评分所述两个个体的兼容性及提供包括兼容性得分的兼容性结果的方法。
量化兴趣兼容性的方法包括以下步骤:计算与每一兴趣相关联的所估计概率(本文称之为“兴趣概率”)及与每一对兴趣相关联的所估计概率(本文称之为“共同概率”),及根据所述所估计概率指派每一对兴趣之间的兴趣兼容性得分。特定兴趣的所估计兴趣概率表示所述社交网络的一个成员将要将此兴趣表示为他或她的一个兴趣的概率。特定对兴趣的所估计共同概率表示所述社交网络的一个成员将要将所述对中的两个兴趣表示为他或她的兴趣的概率。
根据本发明的一个实施例,作为每一对兴趣的所估计共同概率、所述对的以及第二兴趣的所估计兴趣概率的函数计算所述对之间的兴趣兼容性得分。
所述依据兴趣兼容性评分兼容性的方法包括以下步骤:根据所述社交网络的成员所表示兴趣准备兴趣兼容性得分,及根据所述社交网络的第一成员所表示兴趣、所述社交网络的第二成员所表示兴趣及所述第一成员的所表示兴趣与所述第二成员的所表示兴趣之间的兴趣兼容性得分计算所述第一成员与所述第二成员之间的兼容性得分。任何两个所表示兴趣的兴趣兼容性得分表示所述两个所表示兴趣之间的兼容性程度。
所述提供包括(例如)对社交网络中个体的兼容性得分的兼容性结果的方法包括以下步骤:根据所述社交网络中所述个体的所表示兴趣准备兴趣兼容性得分;选择位于距所述第一个体预定分离程度中的一组个体;及所述第一个体与所述组中的每一个体之间的兼容性得分。如果将所述预定分离程度设定为一,则这意味着仅将提供所述第一个体的直接朋友的兼容性得分。将包括所述兼容性得分的兼容性结果提供为网页,且在将所述网页传输以显示之前,将所述兼容性结果以所述兼容性得分的次序分类所述兼容性结果。通过提供兼容性得分及将其链接到兴趣简档,本发明鼓励人们输入兴趣以使所述网站可找到共享相同或兼容兴趣的人。
附图说明
通过参照其中一些图解说明于附图中的实施例可以获得关于上述简要归纳的本发明的更特定说明,从而可更详细地理解上面所引用的本发明实施例的实施方式。但是,应注意:附图所示仅是本发明的典型实施例,不得视为限定了本发明的范围,因为本发明可容许其它等效的实施例。
图1是图形地表示社交网络中成员之间的关系的图示;
图2是图解管理在线社交网络的系统的各个组件的方块图;
图3示意性地图解根据含有兴趣数据的成员数据库计算兴趣兼容性数据的过程;
图4是图解根据含有兴趣数据的成员数据库计算兴趣兼容性数据的过程步骤的流程图;
图5是图解根据本发明的实施例计算社交网络的两个成员之间的兼容性得分的过程步骤的流程图;
图6是图解产生含有兼容性得分的成员搜索结果页面的过程步骤的流程图;
图7是用以指定成员搜索标准的样品GUI;
图8是含有兼容性得分的样品成员搜索结果页面;及
图9是图解根据本发明的另一个实施例计算社交网络的两个成员之间的兼容性得分的过程步骤的流程图。
具体实施方式
图1是以既定个体(ME)为中心的社交网络的图示。此社交网络的其他成员包括A-U,其相对于ME的位置由ME与每一其他成员之间的分离程度指代。ME的朋友(其包括A、B及C)和ME分离1度分离(1d/s)。ME的朋友的朋友和ME分离2d/s。如图所示,D、E、F、G及H各自和ME分离2d/s。ME的朋友的朋友的朋友和ME分离3d/s。图1将所有和ME分离大于3度分离的节点描绘为属于类别ALL。
社交网络中的分离程度是相对于个体所界定的。举例来说,在ME的社交网络中,H和ME分离2d/s,然而在G的社交网络中,H和G分离仅1d/s。因此,每一个体将具有其特有的一、二及三度关系组。
如所属技术领域的技术人员所理解,可将个体的社交网络扩展以包括到N度分离的节点。然而,随着度数增加超过3,节点的数量通常以爆炸速率增长且迅速地开始反映所述ALL组。
图2是图解创建及管理在线社交网络的系统的方块图。如图所示,图2图解系统250,其包括应用服务器251及一个或多个图形服务器252。系统250连接到网络260(例如,因特网)且可通过所述网络由多个计算机(共同地指定为270)存取。应用服务器250管理成员数据库254、关系数据库255及搜索数据库256。成员数据库254含有由系统250所管理的在线社交网络中每一成员的简档信息。所述简档信息除其他以外还可包括:唯一成员标识符、姓名、年龄、性别、位置、籍贯、对图像文件的引用、兴趣列表、属性及诸如此类。关系数据库255存储定义成员之间的第一度关系的信息。此外,成员数据库254的内容经加索引及优化以供搜索,且存储在搜索数据库256中。成员数据库254、关系数据库255及搜索数据库256经更新以反映新成员信息的输入及通过计算机270对现有成员信息进行的编辑。
应用服务器250还管理从远程计算机270所接收的信息交换请求。图形服务器252从应用服务器251接收查询,处理所述查询且将查询结果返回到应用服务器252。图形服务器252管理所述成员数据库中所有成员的社交网络的表达。图形服务器252具有其中存储指示所述社交网络中的所述第一度关系的邻接表的专用存储器装置253,例如随机存取存储器(RAM)。图形服务器252响应于来自应用服务器251的请求而标识所述在线社交网络的成员之间的关系及分离程度。
图3额外详细地图解成员数据库254且显示首先将其中所存储的兴趣数据转换为一组310的经规范化兴趣且然后转换为兴趣兼容性得分矩阵320。应用服务器251的处理单元实施所述到经规范化兴趣且然后到兴趣兼容性得分的转换。
所述兴趣规范化过程本质上是兴趣分类过程。其经实施以便将以不同方式表示的相同兴趣分类在相同兴趣之下。举例来说,表示为阅读的兴趣可分类在作为表示为书籍的兴趣的相同经规范化兴趣之下。在图3中所示的经规范化兴趣组310中,将所述经规范化兴趣显示为一个列表。在替代实施例中,所述经规范化兴趣可被布置为分级树。此外,本发明可应用于其中成员通过选择已由系统操作员预界定的一个或多个兴趣而输入兴趣的系统。在这种情况下,不实施所述规范化步骤且将所述所预界定兴趣组用作经规范化兴趣组310。
兴趣兼容性得分矩阵320提供表示每一对经规范化兴趣(兴趣1、兴趣2、...、兴趣N)有多兼容的数值得分。矩阵320中每一非对角线单元具有数值得分条目,所述数值得分条目指示与所述单元的行及列相关联的两个兴趣的兼容性。矩阵320中的每一对角线单元具有数值得分条目,所述数值得分条目是与所述单元的行及列相关联的兴趣的稀有性的度量。稀有兴趣具有高得分。常见兴趣具有低得分。在本文所图解的本发明的实施例中,根据已被规范化的所述成员所表示兴趣自动地编译兴趣兼容性得分。还可手动地创建所述兴趣兼容性得分或可使用自动及手动过程的组合来创建其。此外,任何自动地编译的兴趣兼容性得分可被手动地调整。
图4是图解产生矩阵320中所涉及的过程步骤的流程图。在步骤410中,将成员数据库254中所存储的所有所表示兴趣规范为经规范化兴趣组310(兴趣1、兴趣2、...、兴趣N)。步骤410中使用称为聚类的标准数据挖掘方法。计算每一经规范化兴趣(I)的概率(P(I))(步骤411)。P(1)表示成员表示对应于所述经规范化兴趣(I)的概率且为根据下式使用成员数据库254中所存储的所表示兴趣所计算:P(I)=(在成员数据库254中表示对应于经规范化兴趣(I)的兴趣的次数)/(成员数据254中所表示兴趣的总数量)。计算每一对经规范化兴趣(I1及I2)的概率(P(I1,I2))。P(I1,I2)表示将成员表示对应于所述经规范化兴趣(I1及I2)的兴趣的概率,且为根据下式使用成员数据库254所存储的所表示兴趣所计算:P(I1,I2)=(成员数据库254中表示对应于所述经规范化兴趣两者(I1及I2)的成员的数量)/(成员数据库254中所表示兴趣的总数量)。在其中I1=I2的情况中,P(I1,I2)被设定为P(I1)或P(I2)。在步骤413中,使用下式计算每一对经规范化兴趣之间的兴趣兼容性得分S(Ii,Ij):S(Ii,Ij)=log[P(Ii,lj)/(P(Ii)*P(Ij))]。因为除以[P(Ii)*P(Ij)],故通过使用这一公式,与更普遍兴趣方面的共同性相比将更高地评定稀有兴趣的共同性。
图5是图解在计算两个成员(例如,第一成员和第二成员)之间的兼容性得分时由应用服务器251的处理器所执行的过程步骤的流程图。在步骤510中,将所述第一成员的所表示兴趣规范化为第一组经规范化兴趣{I1、I2、...、Im},其中m表示在所述第一组中经规范化兴趣的数量。在步骤511中,将所述第二成员所表示的兴趣规范化为第二组经规范化兴趣{J1、J2、...、Jn},其中n表示在所述第二组中经规范化兴趣的数量。在步骤512中,根据矩阵320确定所述第一组与所述第二组中所有对的经规范化兴趣的兴趣兼容性得分。举例来说,如果所述第一组为{兴趣_1、兴趣_2}且所述第二组为{兴趣_2、兴趣_3},则从矩阵320中检索下述兼容性得分:
兼容性(兴趣_1、兴趣_2);
兼容性(兴趣_1、兴趣_3);
兼容性(兴趣_2、兴趣_2);及
兼容性(兴趣_2、兴趣_3)。
在步骤513中,总计步骤512中所确定的兼容性得分,且所述总和表示所述第一成员与所述第二成员之间的兼容性得分。
图6是图解在提供满足由所述社交网络中的一个成员所指定的一组标准的成员的兼容性得分时由应用服务器251的处理器所执行的过程步骤的流程图。在步骤610中,选择满足所述所指定标准的所述社交网络的成员。图7中图解用于指定所述组标准的样品图形用户界面(GUI)。GUI 700显示可由所述成员指定的标准。其包括:年龄、性别(男、女、男&女)、位置、搜索目的、关系现状及例如籍贯、公司、学校、亲缘关系、兴趣、最喜欢的电影、最喜欢的书籍、最喜欢的音乐及最喜欢的电视剧等选定类别的关键词。GUI 700还提供分离程度(d/s)的设置:在1d/s内的成员、在2d/s内的成员、在3d/s内的成员或所有成员。在指定所述标准之后,所述成员点击搜索按钮710,响应于此操作,应用服务器251实施对满足所述所指定标准的成员的搜索。
在步骤611中,计算指定所述标准的所述成员与满足所述所指定的搜索标准的所述社交网络的每一成员之间的兼容性得分。在步骤612中,根据满足所述所指定搜索标准的所述社交网络的成员的兼容性得分对所述成员进行排序,及在步骤613中,将含有与满足所述所指定搜索标准的所述社交网络的所述成员相关联的图像、迷你简档及超链接的网页传输到所述成员以进行显示。在步骤613中所传输的所述网页经格式化以便根据所述成员的兼容性得分(最高到最低)显示与所述成员相关联的所述图像、迷你简档及超链接。图8显示样品搜索结果页面800。
可根据针对两个成员所存储的关系信息调整所述两个成员之间的兼容性得分。在一个实施例中,根据所述成员具有的共有第一到第N度朋友的数量而增加所述两个成员之间的兼容性得分。N通常设定为2或3,但可以是任何正整数。所述兼容性得分可与所述成员具有的共有第一到第N度朋友的数量成正比例增加,其中与基于第二度朋友的增加相比基于第一度朋友的增加被更高地加权,且与基于第三度朋友的增加相比所述基于第二度朋友的增加被更高地加权,如此等等。
在另一个实施例中,根据所述社交网络的第一成员所表示的兴趣在所述第一成员的社交网络中的共同性及所述社交网络的第二成员所表示的兴趣在所述第二成员的社交网络中的共同性调整所述第一成员与所述第二成员之间的兼容性得分。图9是一个流程图,其图解在根据第一成员所表示的兴趣在所述第一成员的社交网络中的共同性及第二成员所表示的兴趣在所述第二成员的社交网络中的共同性进行调整的情况下,计算两个成员(例如,所述第一成员及所述第二成员)之间的兼容性得分时由应用服务器251的处理器所执行的过程步骤。
在步骤910中,将所述第一成员的所表示兴趣规范化为经规范化兴趣的第一组{I1、I2、...、Im},其中m表示所述第一组中经规范化兴趣的数量。在步骤911中,将所述第二成员的所表示兴趣规范化为经规范化兴趣的第二组{J1、J2、...、Jn},其中n表示所述第二组中经规范化兴趣的数量。在步骤912中,根据矩阵320确定所有对的所述第一组中的经规范化兴趣与第二组中的经规范化兴趣的兴趣兼容性得分。举例来说,如果所述第一组为{兴趣_1、兴趣_2}且所述第二组为{兴趣_2、兴趣_3},则从矩阵320检索下述兼容性得分:
兼容性(兴趣_1、兴趣_2);
兼容性(兴趣_1、兴趣_3);
兼容性(兴趣_2、兴趣_2);及
兼容性(兴趣_2、兴趣_3)。
在步骤913中,根据所述第一成员所表示兴趣在所述第一成员的社交网络中的共同性及所述第二成员所表示兴趣在所述第二成员的社交网络中的共同性调整步骤912中所确定的兼容性得分中的每一者。举例来说,对步骤912中所确定的兼容性得分做出如下调整(K12、K13、K22、K23):
k12*兼容性(兴趣_1、兴趣_2);
k13*兼容性(兴趣_1、兴趣_3);
k22*兼容性(兴趣_2、兴趣_2);及
k23*兼容性(兴趣_2、兴趣_3)。
调整(Kij)是所述第一成员的已表示对应于兴趣_i的兴趣的第一到第N度朋友的数量及所述第二成员的已表示对应于兴趣_j的兴趣的第一到第N度朋友的数量的函数。N通常设定为3或4,但可以是任何正整数。所述调整(Kij)的性质如下:
1.kij≥1;
2.kij=kji;
3.kij与所述第一成员的已表示对应于兴趣_i的兴趣的朋友的数量成正比例增加,其中针对越靠近度的朋友的增加量被加权越高;及
4.kij与所述第二成员的已表示对应于兴趣_j的兴趣的朋友的数量成正比例增加,其中针对越靠近度的朋友的增加量被加权越高。
在步骤914中,总计步骤913中所确定的经调整兼容性得分,且所述总和表示所述第一成员与所述第二成员之间的兼容性得分。
虽然上文已图解及说明了根据本发明的特定实施例,但所属技术领域的技术人员应了解,在随附权利要求书的范围中本发明可采用各种形式及实施例。
Claims (20)
1.一种方法,包含:
接收与社交网络的第一成员相关联的查询,所述查询包括选定标准内的一个或者多个关键词,所述第一成员与指定了一个或多个标准的一个或多个值的第一简档信息相关联;
基于将所述查询的关键字与所述选定标准的值匹配,使用与一个或者多个计算机设备相关联的一个或多个处理器选择所述社交网络的一个或多个成员,所选择的每一成员与指定了一个或多个标准的一个或多个值的第二简档信息相关联;
基于与所述第一成员和所选择的成员相关联的每一个标准的值,使用与所述一个或者多个计算机设备相关联的所述一个或多个处理器为所选择的成员中的每一成员计算兼容性得分,每一兼容性得分与所述第一成员和所选择的成员中之一相关联;及
发射与所选择的成员相关联的信息。
2.如权利要求1所述的方法,其中至少一个关键词和类别相关联。
3.如权利要求2所述的方法,其中所述类别包含一个或多个兴趣。
4.如权利要求1所述的方法,其中所述查询包含相对于所述第一成员的分离程度。
5.如权利要求1所述的方法,其中所述查询包含了一个或多个标准,所述一个或多个标准包含年龄、性别、位置、搜索目的、关系现状、籍贯、一个或多个公司、一个或多个学校、一个或多个亲缘关系、一个或多个兴趣、一个或多个喜欢的电影、一本或多本喜欢的书籍、一首或多首喜欢的音乐、一部或多部喜欢的电视节目、一个或多个成员属性或上述任意组合。
6.如权利要求5所述的方法,其中所述一个或多个关键词中的一个或多个关键字与标准相关联。
7.如权利要求1所述的方法,其中基于所述查询选择所述社交网络的一个或者多个成员包含:
处理所述查询,
使用社交图形服务器,所述社交图形服务器管理表达了所述社交网络的所有成员的社交网络的社交图形。
8.如权利要求1所述的方法,其中所发射的信息包含一组超链接信息,每一个超链接与所选择的成员中之一相关联。
9.如权利要求1所述的方法,其中计算与所述第一成员和所选择的一个或多个成员中之一相关联的兼容性得分包含确定所选择的一个或多个成员中之一与所述第一成员之间的共同朋友的数量。
10.如权利要求9所述的方法,其中所述兼容性得分是基于与所述共同朋友相关联的加权值的,所述加权值与所选择的一个或多个成员中之一和所述第一成员之间的分离程度相关联。
11.如权利要求1所述的方法,其中与所选择的成员相关联的信息是依照所述兼容性得分来发射的。
12.如权利要求11所述的方法,其中依照所述兼容性得分发射与所选择的成员相关联的信息包含根据所述兼容性得分将与所选择的成员相关联的信息分类。
13.如权利要求1所述的方法,进一步包含:
将所述第一成员的所述第一简档信息的一个或多个数值规范化,以获得针对所述第一成员的第一组规范化值;及
针对所选择的每一成员,将所选择的成员的所述第二简档信息的一个或多个数值规范化,以获得针对所述第二成员的第二组规范化值。
14.如权利要求13所述的方法,其中为所选择的成员中的每一成员计算兼容性得分是基于针对所述第一成员的所述第一组规范化值和针对所选择的成员的所述第二组规范化值的。
15.如权利要求1所述的方法,其中所选择的成员中的每一成员的兼容性得分是基于所述第一成员相对于所选择的成员的兼容性概率的,所述兼容性概率是基于所述第一成员的所述第一简档信息和所述第二成员的所述第二简档信息的每一个标准的每一对值的。
16.如权利要求1所述的方法,进一步包含从所述第一成员接收对所选择的标准的选择。
17.如权利要求1所述的方法,进一步包含:
访问包含有多个节点和连接所述节点的多条边的社交图形,所述节点中的一个或多个节点中的每一节点与所述社交网络的多个成员相关联,两个节点之间的每一个连接代表所述两个节点之间的关系,及
建立所述两个节点之间的单一分离程度。
18.如权利要求17所述的方法,其中:
所述第一成员与所述多个节点的第一节点相关联;及
所选择的每一成员与所述多个节点的第二节点相关联,每一第二节点在所述第一节点的分离阈值程度内。
19.一种系统,包含:
编码有指令的一个或多个非瞬时性机器可读存储媒体;
一个或多个处理器,经配置以执行所述指令以:
接收与社交网络的第一成员相关联的查询,所述查询包括选定标准内的一个或者多个关键词,所述第一成员与指定了一个或多个标准的一个或多个值的第一简档信息相关联;
基于将所述查询的关键字与所述选定标准的值匹配,选择所述社交网络的一个或多个成员,所选择的每一成员与指定了一个或多个标准的一个或多个值的第二简档信息相关联;
基于与所述第一成员和所选择的成员相关联的每一个标准的值,为所选择的成员中的每一成员计算兼容性得分,每一兼容性得分与所述第一成员和所选择的成员中之一相关联;及
发射与所选择的一个或多个成员相关联的信息。
20.一种或多种计算机可读非瞬时性存储媒体,包括软件,所述软件在由与一个或多个服务器相关联的一个或多个处理器执行时可经操作以:
接收与社交网络的第一成员相关联的查询,所述查询包括选定标准内的一个或者多个关键词,所述第一成员与指定了一个或多个标准的一个或多个值的第一简档信息相关联;
基于将所述查询的关键字与所述选定标准的值匹配,使用与一个或者多个计算机设备相关联的一个或多个处理器选择所述社交网络的一个或多个成员,所选择的每一成员与指定了一个或多个标准的一个或多个值的第二简档信息相关联;
基于与所述第一成员和所选择的成员相关联的每一个标准的值,使用与所述一个或者多个计算机设备相关联的所述一个或多个处理器为所选择的成员中的每一成员计算兼容性得分,每一兼容性得分与所述第一成员和所选择的成员中之一相关联;及
发射与所选择的成员相关联的信息。
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