WO2019119635A1 - Procédé de développement d'utilisateur initial, dispositif électronique et support de stockage lisible par ordinateur - Google Patents

Procédé de développement d'utilisateur initial, dispositif électronique et support de stockage lisible par ordinateur Download PDF

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WO2019119635A1
WO2019119635A1 PCT/CN2018/076181 CN2018076181W WO2019119635A1 WO 2019119635 A1 WO2019119635 A1 WO 2019119635A1 CN 2018076181 W CN2018076181 W CN 2018076181W WO 2019119635 A1 WO2019119635 A1 WO 2019119635A1
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user
seed
expanded
seed user
users
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PCT/CN2018/076181
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English (en)
Chinese (zh)
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安欣
许开河
王建明
肖京
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平安科技（深圳）有限公司
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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
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering

Definitions

  • the present application relates to the field of computer information technology, and in particular, to a seed user extension method, an electronic device, and a computer readable storage medium.
  • the present application proposes a seed user extension method, an electronic device, and a computer readable storage medium, which combines an unsupervised learning clustering method with a distance similarity algorithm to reduce the computational complexity of seed user expansion and improve The accuracy of the expansion.
  • the present application provides an electronic device including a memory, a processor, and a seed user extension system stored on the memory and operable on the processor, the seed user
  • the expansion system is executed by the processor, the following steps are implemented:
  • Clustering analysis is performed on a predetermined number of seed users by a preset clustering method, and the seed user is divided into a plurality of seed user communities having specific characteristics;
  • the user to be expanded is divided into the specific seed user community
  • the number of the users to be expanded into the specific seed user community is counted and sorted, and the expansion rules for expanding the users to be expanded into seed users are determined according to the ranking result.
  • the specific features include the geographic location of the user, whether it is a registered user, whether a particular product has been purchased.
  • the calculating the similarity between the user to be extended and the community of each seed user comprises: calculating the similarity between the user to be expanded and the center point of each seed user, as the similarity between the user to be expanded and each seed user community.
  • the extension rule is set to: select a specified number of users to be expanded according to the order of the number from high to low, and expand the selected users to be expanded into seed users.
  • the extension rule is configured to expand the user to be extended into a seed user if the number of the users to be expanded into a specific seed user community is greater than or equal to a second preset threshold, wherein the second pre- Let the threshold be set to a predetermined ratio of the total number of all seed user communities.
  • the present application further provides a seed user extension method, which is applied to an electronic device, and the method includes:
  • Clustering analysis is performed on a predetermined number of seed users by a preset clustering method, and the seed user is divided into a plurality of seed user communities having specific characteristics;
  • the user to be expanded is divided into the specific seed user community
  • the number of the users to be expanded into the specific seed user community is counted and sorted, and the expansion rules for expanding the users to be expanded into seed users are determined according to the ranking result.
  • the specific feature includes a geographical location of the user, whether it is a registered user, whether a specific product has been purchased;
  • the calculating the similarity between the user to be expanded and each seed user community includes:
  • the similarity between the user to be expanded and the center point of each seed user community is calculated as the similarity between the user to be expanded and each seed user community.
  • the extension rule is set to: select a specified number of users to be expanded according to the order of the number from high to low, and expand the selected users to be expanded into seed users.
  • the extension rule is configured to expand the user to be extended into a seed user if the number of the users to be expanded into a specific seed user community is greater than or equal to a second preset threshold, wherein the second pre- Let the threshold be set to a predetermined ratio of the total number of all seed user communities.
  • the present application further provides a computer readable storage medium storing a seed user extension system, the seed user extension system being executable by at least one processor, such that The at least one processor performs the steps of the seed user extension method as described above.
  • the electronic device, the seed user extension method and the computer readable storage medium proposed by the present application combine the unsupervised learning clustering method with the distance similarity algorithm to reduce the computational complexity of the seed user expansion. And improve the accuracy of the expansion.
  • 1 is a schematic diagram of an optional hardware architecture of an electronic device of the present application
  • FIG. 2 is a schematic diagram of a program module of an embodiment of a seed user extension system in an electronic device of the present application
  • FIG. 3 is a schematic diagram of an implementation process of an embodiment of a seed user extension method according to the present application.
  • FIG. 1 it is a schematic diagram of an optional hardware architecture of the electronic device 2 of the present application.
  • the electronic device 2 may include, but is not limited to, a memory 21, a processor 22, and a network interface 23 that can communicate with each other through a system bus. It is pointed out that FIG. 1 only shows the electronic device 2 with the components 21-23, but it should be understood that not all illustrated components are required to be implemented, and more or fewer components may be implemented instead.
  • the electronic device 2 may be a computing device such as a rack server, a blade server, a tower server, or a rack server.
  • the electronic device 2 may be an independent server or a server cluster composed of multiple servers. .
  • the memory 21 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (eg, SD or DX memory, etc.), a random access memory (RAM), a static Random access memory (SRAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), magnetic memory, magnetic disk, optical disk, and the like.
  • the memory 21 may be an internal storage unit of the electronic device 2, such as a hard disk or memory of the electronic device 2.
  • the memory 21 may also be an external storage device of the electronic device 2, such as a plug-in hard disk equipped on the electronic device 2, a smart memory card (SMC), and a secure digital device. (Secure Digital, SD) card, flash card, etc.
  • the memory 21 may also include both an internal storage unit of the electronic device 2 and an external storage device thereof.
  • the memory 21 is generally used to store an operating system installed in the electronic device 2 and various types of application software, such as program code of the seed user extension system 20. Further, the memory 21 can also be used to temporarily store various types of data that have been output or are to be output.
  • the processor 22 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments.
  • the processor 22 is typically used to control the overall operation of the electronic device 2, such as performing control and processing related to data interaction or communication with the electronic device 2.
  • the processor 22 is configured to run program code or process data stored in the memory 21, such as running the seed user extension system 20 and the like.
  • the network interface 23 may comprise a wireless network interface or a wired network interface, which is typically used to establish a communication connection between the electronic device 2 and other electronic devices.
  • the network interface 23 is configured to connect the electronic device 2 to an external data platform through a network, and establish a data transmission channel and a communication connection between the electronic device 2 and an external data platform.
  • the network may be an intranet, an Internet, a Global System of Mobile communication (GSM), a Wideband Code Division Multiple Access (WCDMA), a 4G network, or a 5G network.
  • Wireless or wired networks such as network, Bluetooth, Wi-Fi, etc.
  • the seed user extension system 20 may be divided into one or more program modules, the one or more program modules being stored in the memory 21 and being processed by one or more processors ( This embodiment is executed by the processor 22) to complete the application.
  • the seed user extension system 20 can be divided into an analysis module 201, a calculation module 202, and an expansion module 203.
  • a program module as referred to in the present application refers to a series of computer program instruction segments capable of performing a specific function, and is more suitable than the program to describe the execution process of the seed user extension system 20 in the electronic device 2. The function of each program module 201-203 will be described in detail below.
  • the analyzing module 201 is configured to perform cluster analysis on a predetermined number (which may be a large data level) of seed users by using a preset clustering method (such as an unsupervised learning K-means clustering method), and the seed is The user is divided into a number of seed user communities with specific characteristics (or salient features).
  • the specific features include, but are not limited to, the geographic location of the user, whether it is a registered user, whether a specific product has been purchased (such as a property insurance), and the like.
  • the K-means clustering method includes the following steps:
  • k data objects are arbitrarily selected from N data objects (ie, N seed users) as cluster centers (ie, initial cluster centers);
  • (A2) Calculate the similarity of each remaining data object to each cluster center for the remaining data objects (such as Euclidean distance, the smaller the distance, the higher the similarity), and each according to the similarity
  • the remaining data objects are assigned to clusters represented by cluster centers that are most similar to them;
  • (A4) Iterate the steps A2 to A3 until the preset standard measure function begins to converge.
  • the mean square error can be used as a preset standard measure function.
  • the calculating module 202 is configured to calculate, by using a preset similarity calculation method, a similarity between the user(s) to be extended and each seed user community (ie, a seed user community having a specific feature).
  • the predetermined similarity calculation method may be: a similarity calculation method such as an Euclidean distance, an included cosine, and a Hamming distance.
  • the calculating the similarity between the user to be extended and each seed user community comprises: calculating the similarity between the user to be expanded and the center point of each seed user community (calculation method and user similarity The calculation method is consistent) as the similarity between the user to be expanded and each seed user community.
  • the seed user community is a set of users with similar characteristics, and each set is aggregated to a central point, which is the center point of the seed user community. Since there is no need to calculate similarities with each user in the seed user community, the computational complexity can be greatly reduced.
  • Representing the cosine similarity of the user a to be expanded and the center point b of a seed user community Represents the rating vector of the user a to be expanded, A scoring vector representing the center point b of a seed user community.
  • the expansion module 203 is configured to divide the user to be expanded into the specific seed user community if the similarity between the user to be expanded and the specific seed user community is greater than or equal to a first preset threshold (eg, 80%).
  • a first preset threshold eg, 80%
  • the seed user community obtained after cluster analysis includes three: B1, B2, and B3, and the similarity S1 of the user A to be expanded and the seed user community B1 is 60%, and the similarity with the seed user community B2 is S2.
  • the user A to be expanded is divided into specific seed user communities B2 and B3.
  • the expansion module 203 is further configured to count and sort the number of the users to be expanded into a specific seed user community, and determine an extension rule for expanding the user to be expanded into a seed user according to the ranking result. Among them, the higher the ranking, the higher the similarity.
  • the extension rule is set to select a user to be expanded according to a specified number (such as the first two digits) according to the number from the highest to the lowest, and expand the selected user to be expanded into a seed user.
  • a specified number such as the first two digits
  • the user A to be expanded is divided into five seed user communities at the same time, and the user B to be expanded is divided into three seed user communities at the same time. If the extended user C is simultaneously divided into two seed user communities, the users A and B will be expanded. Expanded to seed users.
  • the extension rule may be further configured to: if the number of users to be expanded into a specific seed user community is greater than or equal to a second preset threshold, expand the user to be expanded into a seed user.
  • the second preset threshold may be set to a predetermined ratio (eg, 50%) of the total number of all seed user communities (ie, seed user communities having specific characteristics). For example, assuming that the total number of all seed user communities is 4 and the predetermined ratio is 50%, the second preset threshold is 2.
  • the seed user extension system 20 proposed by the present application combines the unsupervised learning clustering method with the distance similarity algorithm, thereby reducing the computational complexity of the seed user expansion and improving the accuracy of the expansion. degree.
  • the present application also proposes a seed user extension method.
  • FIG. 3 it is a schematic flowchart of an implementation process of an embodiment of a seed user extension method of the present application.
  • the order of execution of the steps in the flowchart shown in FIG. 3 may be changed according to different requirements, and some steps may be omitted.
  • Step S31 performing cluster analysis on a predetermined number of seed users (which may be a large data level) by a preset clustering method (such as an unsupervised learning K-means clustering method), and dividing the seed user into several A seed user community with specific characteristics (or salient features).
  • the specific features include, but are not limited to, the geographic location of the user, whether it is a registered user, whether a specific product has been purchased (such as a property insurance), and the like.
  • the K-means clustering method includes the following steps:
  • k data objects are arbitrarily selected from N data objects (ie, N seed users) as cluster centers (ie, initial cluster centers);
  • (A2) Calculate the similarity of each remaining data object to each cluster center for the remaining data objects (such as Euclidean distance, the smaller the distance, the higher the similarity), and each according to the similarity
  • the remaining data objects are assigned to clusters represented by cluster centers that are most similar to them;
  • (A4) Iterate the steps A2 to A3 until the preset standard measure function begins to converge.
  • the mean square error can be used as a preset standard measure function.
  • Step S32 Calculate the similarity between the user(s) to be extended and each seed user community (ie, a seed user community with a specific feature) by a preset similarity calculation method.
  • the predetermined similarity calculation method may be: a similarity calculation method such as an Euclidean distance, an included cosine, and a Hamming distance.
  • the calculating the similarity between the user to be extended and each seed user community comprises: calculating the similarity between the user to be expanded and the center point of each seed user community (calculation method and user similarity The calculation method is consistent) as the similarity between the user to be expanded and each seed user community.
  • the seed user community is a set of users with similar characteristics, and each set is aggregated to a central point, which is the center point of the seed user community. Since there is no need to calculate similarities with each user in the seed user community, the computational complexity can be greatly reduced.
  • Representing the cosine similarity of the user a to be expanded and the center point b of a seed user community Represents the rating vector of the user a to be expanded, A scoring vector representing the center point b of a seed user community.
  • step S33 if the similarity between the user to be expanded and the specific seed user community is greater than or equal to a first preset threshold (such as 80%), the user to be expanded is divided into the specific seed user community.
  • a first preset threshold such as 80%
  • the seed user community obtained after cluster analysis includes three: B1, B2, and B3, and the similarity S1 of the user A to be expanded and the seed user community B1 is 60%, and the similarity with the seed user community B2 is S2.
  • the user A to be expanded is divided into specific seed user communities B2 and B3.
  • step S34 the number of the users to be expanded into the specific seed user community is counted and sorted, and the expansion rule of expanding the user to be expanded into the seed user is determined according to the ranking result. Among them, the higher the ranking, the higher the similarity.
  • the extension rule is set to select a user to be expanded according to a specified number (such as the first two digits) according to the number from the highest to the lowest, and expand the selected user to be expanded into a seed user.
  • a specified number such as the first two digits
  • the user A to be expanded is divided into five seed user communities at the same time, and the user B to be expanded is divided into three seed user communities at the same time. If the extended user C is simultaneously divided into two seed user communities, the users A and B will be expanded. Expanded to seed users.
  • the extension rule may be further configured to: if the number of users to be expanded into a specific seed user community is greater than or equal to a second preset threshold, expand the user to be expanded into a seed user.
  • the second preset threshold may be set to a predetermined ratio (eg, 50%) of the total number of all seed user communities (ie, seed user communities having specific characteristics). For example, assuming that the total number of all seed user communities is 4 and the predetermined ratio is 50%, the second preset threshold is 2.
  • the seed user extension method proposed by the present application combines the unsupervised learning clustering method with the distance similarity algorithm, thereby reducing the computational complexity of the seed user expansion and improving the accuracy of the expansion.
  • the present application further provides a computer readable storage medium (such as a ROM/RAM, a magnetic disk, an optical disk), the computer readable storage medium storing a seed user extension system 20, the seed user
  • the extension system 20 can be executed by at least one processor 22 to cause the at least one processor 22 to perform the steps of the seed user extension method as described below.
  • the specific features include, but are not limited to, the geographic location of the user, whether it is a registered user, whether a specific product has been purchased (such as a property insurance), and the like.
  • the K-means clustering method includes the following steps:
  • k data objects are arbitrarily selected from N data objects (ie, N seed users) as cluster centers (ie, initial cluster centers);
  • (A2) Calculate the similarity of each remaining data object to each cluster center for the remaining data objects (such as Euclidean distance, the smaller the distance, the higher the similarity), and each according to the similarity
  • the remaining data objects are assigned to clusters represented by cluster centers that are most similar to them;
  • (A4) Iterate the steps A2 to A3 until the preset standard measure function begins to converge.
  • the mean square error can be used as a preset standard measure function.
  • the predetermined similarity calculation method may be: a similarity calculation method such as an Euclidean distance, an included cosine, and a Hamming distance.
  • the calculating the similarity between the user to be extended and each seed user community comprises: calculating the similarity between the user to be expanded and the center point of each seed user community (calculation method and user similarity The calculation method is consistent) as the similarity between the user to be expanded and each seed user community.
  • the seed user community is a set of users with similar characteristics, and each set is aggregated to a central point, which is the center point of the seed user community. Since there is no need to calculate similarities with each user in the seed user community, the computational complexity can be greatly reduced.
  • Representing the cosine similarity of the user a to be expanded and the center point b of a seed user community Represents the rating vector of the user a to be expanded, A scoring vector representing the center point b of a seed user community.
  • the seed user community obtained after cluster analysis includes three: B1, B2, and B3, and the similarity S1 of the user A to be expanded and the seed user community B1 is 60%, and the similarity with the seed user community B2 is S2.
  • the user A to be expanded is divided into specific seed user communities B2 and B3.
  • the extension rule is set to select a user to be expanded according to a specified number (such as the first two digits) according to the number from the highest to the lowest, and expand the selected user to be expanded into a seed user.
  • a specified number such as the first two digits
  • the user A to be expanded is divided into five seed user communities at the same time, and the user B to be expanded is divided into three seed user communities at the same time. If the extended user C is simultaneously divided into two seed user communities, the users A and B will be expanded. Expanded to seed users.
  • the extension rule may be further configured to: if the number of users to be expanded into a specific seed user community is greater than or equal to a second preset threshold, expand the user to be expanded into a seed user.
  • the second preset threshold may be set to a predetermined ratio (eg, 50%) of the total number of all seed user communities (ie, seed user communities having specific characteristics). For example, assuming that the total number of all seed user communities is 4 and the predetermined ratio is 50%, the second preset threshold is 2.
  • the computer readable storage medium proposed by the present application combines the unsupervised learning clustering method with the distance similarity algorithm, thereby reducing the computational complexity of the seed user expansion and improving the computational complexity. The accuracy of the expansion.
  • the foregoing embodiment method can be implemented by means of software plus a necessary general hardware platform, and can also be implemented by hardware, but in many cases, the former is A better implementation.
  • the technical solution of the present application which is essential or contributes to the prior art, may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM, disk,
  • the optical disc includes a number of instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the methods described in various embodiments of the present application.

Abstract

La présente invention concerne un procédé de développement d'utilisateur initial, le procédé comprenant les étapes : la réalisation d'une analyse de groupement sur un nombre prédéterminé d'utilisateurs initiaux en utilisant un procédé de groupement prédéfini, et la division des utilisateurs initiaux en plusieurs communautés d'utilisateurs initiaux qui ont des caractéristiques spécifiques ; le calcul, en utilisant un procédé de calcul de similarité prédéfini, de la similarité entre un utilisateur à développer et chaque communauté d'utilisateurs initiaux ; si la similarité entre l'utilisateur à développer et la communauté spécifique d'utilisateurs initiaux est supérieure ou égale à une première valeur de seuil prédéfinie, la classification de l'utilisateur à développer dans la communauté spécifique d'utilisateurs initiaux ; et la compilation de statistiques du nombre de communautés spécifiques d'utilisateurs initiaux, dans lesquelles chaque utilisateur à développer est classifié, et le classement de celles-ci, et la détermination, selon un résultat de classement, d'une règle de développement pour développer un utilisateur à développer en l'utilisateur initial. La présente invention peut réduire la complexité de calcul d'un développement d'utilisateur initial et améliorer la précision de développement.
PCT/CN2018/076181 2017-12-18 2018-02-10 Procédé de développement d'utilisateur initial, dispositif électronique et support de stockage lisible par ordinateur WO2019119635A1 (fr)

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