WO2018081965A1 - 网络贷款投资用户的排序方法及装置 - Google Patents

网络贷款投资用户的排序方法及装置 Download PDF

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WO2018081965A1
WO2018081965A1 PCT/CN2016/104396 CN2016104396W WO2018081965A1 WO 2018081965 A1 WO2018081965 A1 WO 2018081965A1 CN 2016104396 W CN2016104396 W CN 2016104396W WO 2018081965 A1 WO2018081965 A1 WO 2018081965A1
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investment
user
indicator
users
indicators
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PCT/CN2016/104396
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黄诗樵
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深圳投之家金融信息服务有限公司
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Priority to CN201680062164.4A priority Critical patent/CN108475395A/zh
Priority to PCT/CN2016/104396 priority patent/WO2018081965A1/zh
Publication of WO2018081965A1 publication Critical patent/WO2018081965A1/zh

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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION 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
    • G06Q40/00Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/06Asset management; Financial planning or analysis

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  • the invention belongs to the field of network lending technology, and in particular relates to a sorting method and device for a network loan investment user.
  • P2P network loans are based on the Internet to carry out financial information services, and their accessibility is far stronger than traditional offline channels, with millions of users. From a large user group, sorting according to the value of users and discovering high-value users is a strong demand for refined user operations, and other high-end services can be pushed to such high-value users.
  • the existing online loan platform lacks the locking and sorting methods for the above-mentioned high-value users in the massive data.
  • the present invention provides a sorting method and device for network loan investment users.
  • the present invention provides a ranking method for a network loan investment user, including:
  • the behavior data is analyzed and the corresponding indicators are obtained separately;
  • the indicators of the investment strength According to the indicators of the investment strength, the indicators of investment willingness, and the indicators of active investment The user performs a score evaluation;
  • the three-dimensional scores obtained by the user through the score evaluation are comprehensively processed to obtain a sorted list of user values.
  • the analyzing the behavior data according to the investment dimension, the investment intention, and the investment active three dimensions, and respectively obtaining the corresponding indicators further includes:
  • the indicator group information composed of the indicators is stored in a database.
  • the indicators according to the investment strength, the indicators of investment intention, and the indicators of active investment respectively before the score evaluation of the user include:
  • the indicator group information composed of the indicators is read from a database.
  • the behavior data is analyzed according to the three dimensions of investment strength, investment intention and investment activity, and the corresponding indicators are respectively obtained:
  • a group of related indicators are extracted according to a preset algorithm for the purpose of evaluating the user's investment example, investment intention and investment activity.
  • the comprehensive processing user obtains the three-dimensional scores obtained by the score evaluation, and the obtained user value ranking list includes:
  • the index score of the investment strength, the index score of the investment willingness, and the index score of the active investment are weighted to obtain a total score representing the user value, and sorted according to the total score, thereby obtaining a user list sorted by value.
  • the present invention also provides a sorting device for a network loan investment user, including:
  • a user behavior data reading unit configured to obtain behavior data of a user performing financial lending
  • the indicator generating unit is configured to analyze the behavior data according to three dimensions of investment strength, investment intention and investment activity, and respectively obtain corresponding indicators;
  • a scoring unit for indicators based on the stated investment strength, indicators of investment willingness, and active investment The indicators are separately scored for the user;
  • a sorting unit is configured to comprehensively process the scores of the three dimensions obtained by the user through the score evaluation, and obtain a sorted list of user values.
  • the device further comprises:
  • the indicator storage unit is configured to save the indicator group information composed of the indicator to a database.
  • the device further comprises:
  • the indicator data reading unit is configured to read the indicator group information formed by the indicator from the database.
  • the indicator generating unit is specifically configured to:
  • a group of related indicators are extracted according to a preset algorithm for the purpose of evaluating the user's investment example, investment intention and investment activity.
  • the sorting unit is specifically configured to:
  • the index score of the investment strength, the index score of the investment willingness, and the index score of the active investment are weighted to obtain a total score representing the user value, sorted according to the total score, and then the user list sorted by value is obtained.
  • the present invention extracts the user's behavior data of financial lending, extracts the indexes of the user's investment strength, investment willingness and investment active, and comprehensively processes the indicators, and can quickly and effectively serve users of massive users.
  • the value is sorted by value, and the effective information of the user is obtained in time, and the high-value users can be pushed in parallel with other high-value businesses, thereby improving the system operation efficiency.
  • FIG. 1 is a flowchart of a specific implementation of a method for ranking a network loan investment user according to an embodiment of the present invention
  • FIG. 2 is a specific implementation of a ranking method for a network loan investment user according to another embodiment of the present invention. flow chart;
  • FIG. 3 is a flowchart of a specific implementation of a method for ranking a network loan investment user according to another embodiment of the present invention.
  • FIG. 4 is a schematic block diagram of a sorting apparatus of a network loan investment user according to an embodiment of the present invention.
  • FIG. 5 is a schematic block diagram of a sorting apparatus for a network loan investment user according to another embodiment of the present invention.
  • FIG. 6 is a schematic block diagram of a sorting apparatus of a network loan investment user according to another embodiment of the present invention.
  • FIG. 7 is an implementation architecture diagram of a sorting apparatus of the network loan investment user provided in FIG. 6.
  • FIG. 1 is a specific implementation flowchart of a method for ranking a network loan investment user according to an embodiment of the present invention. As shown in FIG. 1 , the ranking method of the network loan investment user provided by this embodiment may include:
  • this step reads various investment behavior data of the user from the business system, including recharge flow record, investment flow record, cash flow record, login flow record, friend recommended flow record, and the like.
  • S200 analyzing the behavior data according to three dimensions of investment strength, investment intention and investment activity, and respectively obtaining corresponding indicators;
  • the acquisition of the investment strength indicator includes: from the flow record obtained in the previous step, for the purpose of evaluating the user's investment instance, a set of related indicators are extracted according to a preset algorithm.
  • Obtaining the investment intention index includes: from the flow record obtained in the previous step, in order to evaluate the user's willingness to invest, a set of related indicators are extracted according to a preset algorithm. For example: cumulative recharge times, cumulative investment counts, cumulative withdrawals, investment recharge ratios, cash withdrawal ratios, etc.
  • Obtaining investment activity indicators includes: from the flow record obtained in the previous step, in order to evaluate the user's investment activity, a set of related indicators are extracted according to a preset algorithm. Examples: cumulative number of logins, number of investments in the last 7 days, number of investments in the last 30 days, number of investments in the last 90 days, number of product features participating in the experience, and number of different terminals used.
  • the behavior data is analyzed according to the three dimensions of investment strength, investment intention and investment activity, and the corresponding indicators are respectively obtained:
  • a group of related indicators are extracted according to a preset algorithm for the purpose of evaluating the user's investment example, investment intention and investment activity.
  • the relevant indicators of investment strength and relevant indicators of investment willingness are scored according to a preset algorithm, and the investment strength score, investment willingness score and investment active score are obtained.
  • the integrated processing user obtains a ranking list of user values by using the scores of the three dimensions obtained by the score evaluation.
  • the comprehensive processing user obtains the three-dimensional scores obtained by the score evaluation, and the obtained user value ranking list includes:
  • the index score of the investment strength, the index score of the investment willingness, and the index score of the active investment are weighted, and the total score representing the user value is obtained, and the score is sorted according to the total score, and then the press is obtained.
  • the method provided by the embodiment of the present invention extracts the user's behavioral data of the financial strength, the willingness to invest, and the active investment by acquiring the behavior data of the user for financial lending, and comprehensively processes the indicator. It can quickly and effectively sort the value of the user value of the massive users, obtain the effective information of the user in time, and can push the high-value users in parallel with other high-value businesses, thereby improving the system operation efficiency.
  • FIG. 2 is a flowchart of a specific implementation of a method for ranking a network loan investment user according to another embodiment of the present invention.
  • the behavior data provided by the present embodiment is analyzed according to the three dimensions of investment strength, investment intention, and investment activity, and may be included after obtaining corresponding indicators respectively.
  • the indicator group information formed by the indicator is saved in a database, and the indicator can be saved in time to provide a convenient condition for further extracting the indicator.
  • FIG. 3 is a flowchart of a specific implementation of a method for ranking a network loan investment user according to another embodiment of the present invention. As shown in FIG. 3, the ranking method of the network loan investment user provided by this embodiment may further include:
  • the present embodiment can quickly and effectively obtain the indicator data by reading the indicator group information formed by the indicator from the database, thereby avoiding the time delay caused by temporarily extracting the data.
  • FIG. 4 is a schematic block diagram of a sorting apparatus of a network loan investment user according to an embodiment of the present invention, and only parts related to the present invention are shown for convenience of description.
  • the present invention further provides a sorting device for a network loan investment user, which may include:
  • a user behavior data reading unit configured to obtain behavior data of a user performing financial lending
  • An indicator generating unit is configured to analyze the behavior data according to three dimensions of investment strength, investment intention and investment activity, and respectively obtain corresponding indicators;
  • a scoring unit configured to perform a score evaluation on the user according to the indicator of the investment strength, an indicator of investment intention, and an indicator of active investment;
  • a sorting unit configured to comprehensively process the scores of the three dimensions obtained by the user through the score evaluation, and obtain a sorted list of user values.
  • FIG. 5 is a schematic block diagram of a sorting apparatus for a network loan investment user according to another embodiment of the present invention. As shown in FIG. 5, the sorting apparatus of the network loan investment user provided by the embodiment may also be used in the foregoing embodiment. include:
  • the indicator storage unit is configured to save the indicator group information formed by the indicator to a database.
  • FIG. 6 is a schematic block diagram of a sorting device for a network loan investment user according to another embodiment of the present invention
  • FIG. 7 is an implementation architecture diagram of a sorting device for a network loan investment user provided in FIG. 6.
  • the sorting apparatus of the network loan investment user provided by the embodiment may further include:
  • An indicator data reading unit configured to read indicator group information formed by the indicator from a database.
  • the apparatus provided by the embodiment of the present invention can also obtain the indicators of the three dimensions of investment strength, investment willingness, and investment active by acquiring the behavior data of the user for financial lending, and comprehensively processing the indicators. It can quickly and effectively sort the value of the user value of a large number of users, timely obtain the effective information of the user, and can perform parallel on other high-value businesses for high-value users. Pushing improves the operating efficiency of the system.

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Abstract

本发明提供了一种网络贷款投资用户的排序方法及装置,涉及网络借贷技术领域。该方法包括:获取用户进行金融借贷的行为数据;按照投资实力、投资意愿以及投资活跃三个维度对所述行为数据进行分析,并分别获取相应的指标;根据所述投资实力的指标、投资意愿的指标以及投资活跃的指标分别对所述用户进行分数测评;综合处理用户通过所述分数测评得到的三个维度的分数,获取用户价值高低排序列表。本发明提供的网络贷款投资用户的排序方法,可快速有效的对海量用户的用户价值进行价值排序,及时获取用户有效信息,对高价值用户进行其它高价值业务的并行推送,提高了***运行效率。

Description

网络贷款投资用户的排序方法及装置 技术领域
本发明属于网络借贷技术领域,尤其涉及一种网络贷款投资用户的排序方法及装置。
背景技术
P2P网络贷款基于互联网开展金融信息服务,其获客能力远远强于传统的线下渠道,用户量数以百万计。从庞大的用户群体中,按照用户的价值进行排序,发现高价值用户,是精细化用户运营的强烈需求,可向该类高价值用户推送其它高端服务。现有网络贷款平台缺乏在海量数据中对上述高价值用户的锁定以及排序方法。
上述问题亟待解决。
发明内容
针对现有网络贷款平台缺乏在海量数据中对高价值用户的锁定以及排序方法的缺陷,本发明提供一种网络贷款投资用户的排序方法及装置。
一方面,本发明提供一种网络贷款投资用户的排序方法,包括:
获取用户进行金融借贷的行为数据;
按照投资实力、投资意愿以及投资活跃三个维度对所述行为数据进行分析,并分别获取相应的指标;
根据所述投资实力的指标、投资意愿的指标以及投资活跃的指标分别对所 述用户进行分数测评;
综合处理用户通过所述分数测评得到的三个维度的分数,获取用户价值高低排序列表。
优选的,所述按照投资实力、投资意愿以及投资活跃三个维度对所述行为数据进行分析,并分别获取相应的指标之后还包括:
将所述指标构成的指标群信息保存到数据库。
优选的,所述根据所述投资实力的指标、投资意愿的指标以及投资活跃的指标分别对所述用户进行分数测评之前还包括:
从数据库中读取所述指标构成的指标群信息。
优选的,所述按照投资实力、投资意愿以及投资活跃三个维度对所述行为数据进行分析,并分别获取相应的指标具体包括:
从所述行为数据中的流水记录中,以评估用户的投资实例、投资意愿以及投资活跃为目的,按照预设算法提取出一批相关的指标。
优选的,所述综合处理用户通过所述分数测评得到的三个维度的分数,获取用户价值高低排序列表具体包括:
将投资实力的指标分数、投资意愿的指标分数、投资活跃的指标分数,进行加权处理,得到代表用户价值的总得分,依据总得分进行排序,进而得到按价值排序的用户列表。
另一方面,本发明还提供一种网络贷款投资用户的排序装置,包括:
用户行为数据读取单元,用于获取用户进行金融借贷的行为数据;
指标生成单元,用于按照投资实力、投资意愿以及投资活跃三个维度对所述行为数据进行分析,并分别获取相应的指标;
打分单元,用于根据所述投资实力的指标、投资意愿的指标以及投资活跃 的指标分别对所述用户进行分数测评;
排序单元,用于综合处理用户通过所述分数测评得到的三个维度的分数,获取用户价值高低排序列表。
优选的,所述装置还包括:
指标入库单元,用于将所述指标构成的指标群信息保存到数据库。
优选的,所述装置还包括:
指标数据读取单元,用于从数据库中读取所述指标构成的指标群信息。
优选的,所述指标生成单元具体用于:
从所述行为数据中的流水记录中,以评估用户的投资实例、投资意愿以及投资活跃为目的,按照预设算法提取出一批相关的指标。
优选的,所述排序单元具体用于:
将投资实力的指标分数、投资意愿的指标分数、投资活跃的指标分数,进行加权处理,得到代表用户价值的总得分,依据总得分进行排序,进而得到按价值排序的用户列表。
有益效果:本发明通过获取用户进行金融借贷的行为数据,提取用户在投资实力、投资意愿以及投资活跃三个维度的指标,并对所述指标进行综合处理,可快速有效的对海量用户的用户价值进行价值排序,及时获取用户有效信息,可对高价值用户进行其它高价值业务的并行推送,提高了***运行效率。
附图说明
图1是本发明实施例提供的网络贷款投资用户的排序方法的具体实现流程图;
图2是本发明另一实施例提供的网络贷款投资用户的排序方法的具体实现 流程图;
图3是本发明另一实施例提供的网络贷款投资用户的排序方法的具体实现流程图;
图4是本发明实施例提供的网络贷款投资用户的排序装置的示意性框图;
图5是本发明另一实施例提供的网络贷款投资用户的排序装置示意性框图;
图6是本发明另一实施例提供的网络贷款投资用户的排序装置的示意性框图;
图7是图6提供的网络贷款投资用户的排序装置的实现架构图。
具体实施方式
为了使本发明的目的、技术方案及优点更加清楚明白,以下结合附图及实施例,对本发明进行进一步详细说明。应当理解,此处所描述的具体实施例仅仅用以解释本发明,并不用于限定本发明。
图1是本发明实施例提供的网络贷款投资用户的排序方法的具体实现流程图。参见图1所示,本实施例提供的网络贷款投资用户的排序方法可以包括:
S100、获取用户进行金融借贷的行为数据;
具体的,本步骤从业务***,读取用户的各种投资行为数据,包括充值流水记录、投资流水记录、提现流水记录、登录流水记录、好友推荐流水记录等。
S200、按照投资实力、投资意愿以及投资活跃三个维度对所述行为数据进行分析,并分别获取相应的指标;
具体的,获取投资实力指标包括:从上一步骤得到的流水记录中,以评估用户的投资实例为目的,按照预设的算法提取出一批相关指标。举例:累计充 值金额、累计投资金额、平均单笔投资金额、历史上单日总资产最大值、最近30天日均待收等。
获取投资意愿指标包括:从上一步骤得到的流水记录中,以评估用户的投资意愿为目的,按照预设的算法提取出一批相关指标。举例:累计充值次数、累计投资次数、累计提现次数、投资充值比、提现充值比等。
获取投资活跃指标包括:从上一步骤得到的流水记录中,以评估用户的投资活跃为目的,按照预设的算法提取出一批相关指标。举例:累计登录次数、最近7天投资次数、最近30天投资次数、最近90天投资次数、参与体验的产品功能数量、使用的不同终端数量。
优选的,所述按照投资实力、投资意愿以及投资活跃三个维度对所述行为数据进行分析,并分别获取相应的指标具体包括:
从所述行为数据中的流水记录中,以评估用户的投资实例、投资意愿以及投资活跃为目的,按照预设算法提取出一批相关的指标。
S300、所述投资实力的指标、投资意愿的指标以及投资活跃的指标分别对所述用户进行分数测评;
具体的,根据投资实力相关指标、投资意愿相关指标,投资活跃相关指标按预设算法进行打分,得到投资实力得分、投资意愿得分以及投资活跃得分。
S400、综合处理用户通过所述分数测评得到的三个维度的分数,获取用户价值高低排序列表。
优选的,所述综合处理用户通过所述分数测评得到的三个维度的分数,获取用户价值高低排序列表具体包括:
将投资实力的指标分数、投资意愿的指标分数、投资活跃的指标分数,进行加权处理,得到代表用户价值的总得分,依据总得分进行排序,进而得到按 价值排序的用户列表。
由以上实施例可看出,本发明实施例提供的方法通过获取用户进行金融借贷的行为数据,提取用户在投资实力、投资意愿以及投资活跃三个维度的指标,并对所述指标进行综合处理,可快速有效的对海量用户的用户价值进行价值排序,及时获取用户有效信息,可对高价值用户进行其它高价值业务的并行推送,提高了***运行效率。
图2是本发明另一实施例提供的网络贷款投资用户的排序方法的具体实现流程图。参见图2所示,相对于上一实施例,本实施例提供的所述按照投资实力、投资意愿以及投资活跃三个维度对所述行为数据进行分析,并分别获取相应的指标之后还可以包括:
S500、将所述指标构成的指标群信息保存到数据库。
相对于上一实施例,本实施例通过将所述指标构成的指标群信息保存到数据库,可将指标及时保存,为进一步提取所述指标进行操作提供便利条件。
图3是本发明另一实施例提供的网络贷款投资用户的排序方法的具体实现流程图。参见图3所示,相对于上一实施例,本实施例提供的网络贷款投资用户的排序方法还可以包括:
S600、从数据库中读取所述指标构成的指标群信息。
相对于上一实施例,本实施例通过从数据库中读取所述指标构成的指标群信息,可快速有效的获取指标数据,避免了临时提取数据造成的时间延迟。
图4为本发明实施例提供的网络贷款投资用户的排序装置的示意性框图,为了便于说明仅仅示出了与本发明相关的部分。
如图4所示,本发明还提供一种网络贷款投资用户的排序装置,可以包括:
100、用户行为数据读取单元,用于获取用户进行金融借贷的行为数据;
200、指标生成单元,用于按照投资实力、投资意愿以及投资活跃三个维度对所述行为数据进行分析,并分别获取相应的指标;
300、打分单元,用于根据所述投资实力的指标、投资意愿的指标以及投资活跃的指标分别对所述用户进行分数测评;
400、排序单元,用于综合处理用户通过所述分数测评得到的三个维度的分数,获取用户价值高低排序列表。
图5是本发明另一实施例提供的网络贷款投资用户的排序装置示意性框图,如图5所示,相对于上一实施例,本实施例所提供的网络贷款投资用户的排序装置还可以包括:
500、指标入库单元,用于将所述指标构成的指标群信息保存到数据库。
图6是本发明另一实施例提供的网络贷款投资用户的排序装置示意性框图,图7是图6提供的网络贷款投资用户的排序装置的实现架构图。如图6和图7所示,相对于上一实施例,本实施例所提供的网络贷款投资用户的排序装置还可以包括:
600、指标数据读取单元,用于从数据库中读取所述指标构成的指标群信息。
需要说明的是,本发明实施例提供的上述***中各个模块,由于与本发明方法实施例基于同一构思,其带来的技术效果与本发明方法实施例相同,具体内容可参见本发明方法实施例中的叙述,此处不再赘述。
因此,可以看出本发明实施例提供的装置同样可以通过获取用户进行金融借贷的行为数据,提取用户在投资实力、投资意愿以及投资活跃三个维度的指标,并对所述指标进行综合处理,可快速有效的对海量用户的用户价值进行价值排序,及时获取用户有效信息,可对高价值用户进行其它高价值业务的并行 推送,提高了***运行效率。
以上所述仅为本发明的较佳实施例而已,并不用以限制本发明,凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等,均应包含在本发明的保护范围之内。

Claims (10)

  1. 一种网络贷款投资用户的排序方法,其特征在于,包括:
    获取用户进行金融借贷的行为数据;
    按照投资实力、投资意愿以及投资活跃三个维度对所述行为数据进行分析,并分别获取相应的指标;
    根据所述投资实力的指标、投资意愿的指标以及投资活跃的指标分别对所述用户进行分数测评;
    综合处理用户通过所述分数测评得到的三个维度的分数,获取用户价值高低排序列表。
  2. 如权利要求1所述的方法,其特征在于,所述按照投资实力、投资意愿以及投资活跃三个维度对所述行为数据进行分析,并分别获取相应的指标之后还包括:
    将所述指标构成的指标群信息保存到数据库。
  3. 如权利要求1所述的方法,其特征在于,所述根据所述投资实力的指标、投资意愿的指标以及投资活跃的指标分别对所述用户进行分数测评之前还包括:
    从数据库中读取所述指标构成的指标群信息。
  4. 如权利要求1所述的方法,其特征在于,所述按照投资实力、投资意愿以及投资活跃三个维度对所述行为数据进行分析,并分别获取相应的指标具体包括:
    从所述行为数据中的流水记录中,以评估用户的投资实例、投资意愿以及投资活跃为目的,按照预设算法提取出一批相关的指标。
  5. 如权利要求1所述的方法,其特征在于,所述综合处理用户通过所述 分数测评得到的三个维度的分数,获取用户价值高低排序列表具体包括:
    将投资实力的指标分数、投资意愿的指标分数、投资活跃的指标分数,进行加权处理,得到代表用户价值的总得分,依据总得分进行排序,进而得到按价值排序的用户列表。
  6. 一种网络贷款投资用户的排序装置,其特征在于,包括:
    用户行为数据读取单元,用于获取用户进行金融借贷的行为数据;
    指标生成单元,用于按照投资实力、投资意愿以及投资活跃三个维度对所述行为数据进行分析,并分别获取相应的指标;
    打分单元,用于根据所述投资实力的指标、投资意愿的指标以及投资活跃的指标分别对所述用户进行分数测评;
    排序单元,用于综合处理用户通过所述分数测评得到的三个维度的分数,获取用户价值高低排序列表。
  7. 如权利要求6所述的装置,其特征在于,所述装置还包括:
    指标入库单元,用于将所述指标构成的指标群信息保存到数据库。
  8. 如权利要求6所述的装置,其特征在于,所述装置还包括:
    指标数据读取单元,用于从数据库中读取所述指标构成的指标群信息。
  9. 如权利要求6所述的装置,其特征在于,所述指标生成单元具体用于:
    从所述行为数据中的流水记录中,以评估用户的投资实例、投资意愿以及投资活跃为目的,按照预设算法提取出一批相关的指标。
  10. 如权利要求6所述的装置,其特征在于,所述排序单元具体用于:
    将投资实力的指标分数、投资意愿的指标分数、投资活跃的指标分数,进行加权处理,得到代表用户价值的总得分,依据总得分进行排序,进而得到按价值排序的用户列表。
PCT/CN2016/104396 2016-11-02 2016-11-02 网络贷款投资用户的排序方法及装置 WO2018081965A1 (zh)

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US20140129421A1 (en) * 2012-11-08 2014-05-08 John Turnham Method and Apparatus for Searching and Acquiring Whole Loans
CN105427171A (zh) * 2015-11-30 2016-03-23 北京口袋财富信息科技有限公司 一种互联网借贷平台评级的数据处理方法

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140129421A1 (en) * 2012-11-08 2014-05-08 John Turnham Method and Apparatus for Searching and Acquiring Whole Loans
CN105427171A (zh) * 2015-11-30 2016-03-23 北京口袋财富信息科技有限公司 一种互联网借贷平台评级的数据处理方法

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