CN112800333B - Recommendation method, device, equipment and storage medium for enterprise user service - Google Patents
Recommendation method, device, equipment and storage medium for enterprise user service Download PDFInfo
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Abstract
The embodiment of the application provides a recommendation method, a device, equipment and a storage medium for enterprise user services, wherein the method comprises the following steps: acquiring target enterprise user registration information, target enterprise user business information, target enterprise user operation information and target enterprise user behavior information; determining first enterprise user service recommendation information and second enterprise user service recommendation information according to target enterprise user registration information, target enterprise user business information, target enterprise user operation information and target enterprise user behavior information; summarizing and sorting the first enterprise user service recommendation information and the second enterprise user service recommendation information to obtain target enterprise user service recommendation sorting information; recommending enterprise user service recommendation information according to the target enterprise user service recommendation ranking information; the method and the device can solve the problem that the service recommendation result of the enterprise user is not matched with the real service requirement of the enterprise user.
Description
Technical Field
The application belongs to the field of computers, and particularly relates to a recommendation method, device and equipment for enterprise user services and a storage medium.
Background
The enterprise user service is different from the purchase of common commodities, the common commodities have definite price, specification and other parameters, similar purchase of similar consumables can occur, and the same enterprise user service also has different price and service results according to different requirements. For example: the software platform detection report service may include several aspects of software availability, robustness, different fields (such as combination of hardware and software equipment, AI voice recognition, telecommunication service, etc.), performance, platform security, interface suitability, workload rationality, software value, etc., while different detection reports required to be provided are different in price, different software fields, and inconsistent in detection mode and cost of manpower and material resources.
In the prior art, the recommendation of the enterprise user service is generally performed according to the behavior information of the enterprise user, the considered influence factors are few, and the recommendation result of the enterprise user service in the prior art is not matched with the real service requirement of the enterprise user.
Disclosure of Invention
The embodiment of the application provides a recommendation method, device, equipment and storage medium for enterprise user service, which can solve the technical problem that an enterprise user service recommendation result in the prior art is not matched with the real service requirement of an enterprise user.
In a first aspect, an embodiment of the present application provides a recommendation method for an enterprise user service, including:
acquiring target enterprise user information of a target enterprise user, wherein the target enterprise user information comprises: target enterprise user registration information, target enterprise user business information, target enterprise user operation information, and target enterprise user behavior information;
determining first enterprise user service recommendation information according to target enterprise user registration information and target enterprise user behavior information, wherein the first enterprise user service recommendation information comprises a plurality of first types of service recommendation information and occurrence frequency of the service recommendation information of each first type;
determining second enterprise user service recommendation information according to the target enterprise user registration information, the target enterprise user business information and the target enterprise user operation information, wherein the second enterprise user service recommendation information comprises a plurality of second types of service recommendation information and occurrence frequency of the service recommendation information of each second type;
summarizing and sorting the first enterprise user service recommendation information and the second enterprise user service recommendation information according to the occurrence frequency of each type of service recommendation information to obtain target enterprise user service recommendation sorting information;
And recommending the enterprise user service recommendation information to the enterprise user equipment according to the target enterprise user service recommendation ranking information.
Further, in one embodiment, determining the first enterprise user service recommendation information based on the target enterprise user registration information and the target enterprise user behavior information includes:
determining a plurality of first types corresponding to the target enterprise user behavior information according to mapping relation information of the types of the preset enterprise user behavior information and the service recommendation information;
classifying the target enterprise user behavior information according to a plurality of first types, and determining the number of the target enterprise user behavior information corresponding to each first type;
inputting a plurality of first types and target enterprise user registration information into a first preset algorithm, and outputting weights corresponding to the first types;
the occurrence frequency of the service recommendation information of each first type is the product of the number of the target enterprise user behavior information corresponding to the current first type and the weight corresponding to the current first type.
Further, in one embodiment, determining the second enterprise user service information based on the target enterprise user registration information, the target enterprise user business information, and the target enterprise user operation information includes:
Inputting the operation information of the target enterprise user into a second preset algorithm to perform information dimension reduction to obtain the operation information of the first target enterprise user;
inputting the first target enterprise user operation information into a third preset algorithm to perform clustering division to obtain second target enterprise user operation information;
and determining a plurality of second types of service recommendation information corresponding to the second target enterprise user operation information, the target enterprise user registration information and the target enterprise user business information and occurrence frequency of the service recommendation information of each second type according to the preset corresponding relation between the enterprise user operation information, the enterprise user registration information and the types of the enterprise user business information and the service recommendation information.
Further, in an embodiment, the step of summarizing and sorting the first enterprise user service recommendation information and the second enterprise user service recommendation information according to occurrence frequency of each type of service recommendation information to obtain target enterprise user service recommendation sorting information includes:
acquiring reference enterprise user information of a reference enterprise user, wherein the reference enterprise user information comprises: reference to enterprise user registration information, reference to enterprise user business information, reference to enterprise user operation information, and reference to enterprise user behavior information;
Inputting the reference enterprise user information and the target enterprise user information into a fourth preset algorithm for comparison, and outputting similar parameter information representing the similarity degree of the reference enterprise user information and the target enterprise user information;
acquiring historical enterprise user service information of a reference enterprise user;
inputting historical enterprise user service information of the reference enterprise user corresponding to similar parameter information which is not smaller than a preset threshold value into a fifth preset algorithm for filtering, and outputting third enterprise user service recommendation information;
the third enterprise user service recommendation information comprises a plurality of third types of service recommendation information and occurrence frequency of the service recommendation information of each third type;
and summarizing and sorting the first enterprise user service recommendation information, the second enterprise user service recommendation information and the third enterprise user service information according to the occurrence frequency of the service recommendation information of each type to obtain target enterprise user service recommendation sorting information.
Further, in an embodiment, according to occurrence frequency of various kinds of service recommendation information of enterprise users, summarizing and sorting the service recommendation information of the first enterprise user, the service recommendation information of the second enterprise user and the service information of the third enterprise user, to obtain service recommendation sorting information of the target enterprise user, including:
Acquiring historical service browsing information of a target enterprise user and historical service ordering information of the target enterprise user;
inputting historical service browsing information of the target enterprise user and ordering information of the historical service of the target enterprise user into a third preset algorithm for clustering, and outputting service information of a fourth enterprise user;
the fourth enterprise user service recommendation information comprises a plurality of fourth types of service recommendation information and occurrence frequency of the service recommendation information of each fourth type;
and summarizing and sorting the first enterprise user service recommendation information, the second enterprise user service information, the third enterprise user service information and the fourth service type according to the occurrence frequency of the service recommendation information of each type to obtain target enterprise user service recommendation sorting information.
In a second aspect, an embodiment of the present application provides a recommendation device for an enterprise user service, including:
the acquisition module is used for acquiring target enterprise user information of target enterprise users, wherein the target enterprise user information comprises: target enterprise user registration information, target enterprise user business information, target enterprise user operation information, and target enterprise user behavior information;
The system comprises a determining module, a judging module and a judging module, wherein the determining module is used for determining first enterprise user service recommendation information according to target enterprise user registration information and target enterprise user behavior information, the first enterprise user service recommendation information comprises a plurality of first types of service recommendation information, and occurrence frequencies of the service recommendation information of each first type;
the determining module is further configured to determine second enterprise user service recommendation information according to the target enterprise user registration information, the target enterprise user business information, and the target enterprise user operation information, where the second enterprise user service recommendation information includes a plurality of second types of service recommendation information, and occurrence frequencies of the service recommendation information of each second type;
the ordering module is used for summarizing and ordering the first enterprise user service recommendation information and the second enterprise user service recommendation information according to the occurrence frequency of the service recommendation information of each type to obtain target enterprise user service recommendation ordering information;
and the recommending module is used for recommending the enterprise user service recommending information to the enterprise user equipment according to the target enterprise user service recommending and sorting information.
Further, in one embodiment, the determining module is specifically configured to:
Determining a plurality of first types corresponding to the target enterprise user behavior information according to mapping relation information of the types of the preset enterprise user behavior information and the service recommendation information;
classifying the target enterprise user behavior information according to a plurality of first types, and determining the number of the target enterprise user behavior information corresponding to each first type;
inputting a plurality of first types and target enterprise user registration information into a first preset algorithm, and outputting weights corresponding to the first types;
the occurrence frequency of the service recommendation information of each first type is the product of the number of the target enterprise user behavior information corresponding to the current first type and the weight corresponding to the current first type.
Further, in one embodiment, the determining module is specifically configured to:
inputting the operation information of the target enterprise user into a second preset algorithm to perform information dimension reduction to obtain the operation information of the first target enterprise user;
inputting the first target enterprise user operation information into a third preset algorithm to perform clustering division to obtain second target enterprise user operation information;
and determining a plurality of second types of service recommendation information corresponding to the second target enterprise user operation information, the target enterprise user registration information and the target enterprise user business information and occurrence frequency of the service recommendation information of each second type according to the preset corresponding relation between the enterprise user operation information, the enterprise user registration information and the types of the enterprise user business information and the service recommendation information.
In a third aspect, an embodiment of the present application provides a recommendation device for an enterprise user service, including: the system comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the computer program is executed by the processor to realize the recommendation method of the enterprise user service.
In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, where a program for implementing information transfer is stored, where the program when executed by a processor implements the method for recommending enterprise user services described above.
The recommendation method, the device, the equipment and the storage medium for the enterprise user service take the real service requirements of the enterprise user into account in a multidimensional manner, the enterprise user service recommendation information is determined through the enterprise user registration information, the enterprise user business information, the enterprise user operation information and the enterprise user behavior information, and the enterprise user service recommendation information is sequenced according to the occurrence frequency of each type of service recommendation information, so that more comprehensive service recommendation information can be provided for the enterprise user, and the real service requirements of the enterprise user can be matched.
Drawings
In order to more clearly illustrate the technical solution of the embodiments of the present application, the drawings that are needed to be used in the embodiments of the present application will be briefly described, and it is possible for a person skilled in the art to obtain other drawings according to these drawings without inventive effort.
FIG. 1 is a schematic flow chart of a recommendation method for enterprise user services according to an embodiment of the present application;
FIG. 2 is a schematic structural diagram of a recommendation device for enterprise user services according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a recommendation device for enterprise user services according to an embodiment of the present application.
Detailed Description
Features and exemplary embodiments of various aspects of the present application will be described in detail below, and in order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail below with reference to the accompanying drawings and the detailed embodiments. It should be understood that the specific embodiments described herein are merely configured to illustrate the application and are not configured to limit the application. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the application by showing examples of the application.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The conventional internet platform generally performs service recommendation only according to user behavior information, and does not consider the influence of enterprise user registration information, enterprise user business information and enterprise user operation information on the real service requirement of an enterprise user, so that the service recommendation result of the enterprise user in the prior art is not matched with the real service requirement of the enterprise user.
In order to solve the problems in the prior art, the embodiment of the application provides a recommendation method, device and equipment for enterprise user services and a storage medium. The embodiment of the application takes the real service demands of enterprise users into account in a multidimensional manner, determines the service recommendation information of the enterprise users through the registration information of the enterprise users, the business information of the enterprise users, the operation information of the enterprise users and the behavior information of the enterprise users, sorts the service recommendation information of the enterprise users according to the occurrence frequency of the service recommendation information of each type, can provide more comprehensive service recommendation information for the enterprise users, and is more matched with the real service demands of the enterprise users. The following first describes a recommendation method for enterprise user services provided by the embodiment of the present application.
FIG. 1 is a flow chart illustrating a recommendation method for enterprise user services according to one embodiment of the present application. As shown in fig. 1, the method may include the steps of:
s10, acquiring target enterprise user information of a target enterprise user, wherein the target enterprise user information comprises: target enterprise user registration information, target enterprise user business information, target enterprise user operation information, and target enterprise user behavior information.
The target enterprise user information of the enterprise user can be obtained from an internet platform, and specifically, the enterprise user registration information includes: account number, contact way, address and social unified credit code; according to the unified credit code, the basic information of the enterprise can be captured, wherein the basic information comprises fund sources (stock book composition), registered capital, investment checking reports, operation ranges, account opening banks and accounts, asset liability lists, damage lists, cash flow tables, enterprise residence places and stakeholders, names, resume and legal representatives or names, addresses and resume of responsible persons and superior administration units of the enterprise. The enterprise user business information includes: enterprise user base information, administrative license information, administrative penalty information, abnormal directory information, and trust loss information. The enterprise user operation information includes: company registration, company logout, company annual report, annual report abnormality, certificate repair, company change, etc., trademark registration, trademark protection, trademark change, trademark design, patent information, tax service information, proxy accounting, tax service, tax planning, annual report, project, transaction, money exchange, tax. The enterprise user behavior information includes: retrieval, browsing, ordering, collection, scoring, frequency, sharing, and attention.
S12, determining first enterprise user service recommendation information according to target enterprise user registration information and target enterprise user behavior information, wherein the first enterprise user service recommendation information comprises a plurality of first types of service recommendation information and occurrence frequency of the service recommendation information of each first type.
In the determining process, the target enterprise user registration information and the target enterprise user behavior information can include multiple information types, so that the determined first enterprise user service recommendation information can include multiple types.
In one embodiment, S12 may include:
and determining a plurality of first types corresponding to the target enterprise user behavior information according to the preset mapping relation information of the types of the enterprise user behavior information and the service recommendation information.
For example, if the enterprise user behavior information characterizes that the enterprise user has retrieved an auto-part, the type of service recommendation information may be determined as a plurality of service types associated with the auto-part.
Classifying the target enterprise user behavior information according to the first types, and determining the number of the target enterprise user behavior information corresponding to each first type.
And inputting the registration information of the multiple first types and the target enterprise users into a first preset algorithm, and outputting weights corresponding to the first types.
The first preset algorithm may be a weighting algorithm.
The occurrence frequency of the service recommendation information of each first type is the product of the number of the target enterprise user behavior information corresponding to the current first type and the weight corresponding to the current first type.
S14, determining second enterprise user service recommendation information according to the target enterprise user registration information, the target enterprise user business information and the target enterprise user operation information, wherein the second enterprise user service recommendation information comprises a plurality of second types of service recommendation information and occurrence frequency of the service recommendation information of each second type.
In one embodiment, S14 may include:
and inputting the operation information of the target enterprise user into a second preset algorithm to perform information dimension reduction to obtain the operation information of the first target enterprise user.
The dimension-reduced information may include: operation information, establishment basic data, establishment information, bidding information, personnel, annual reports, finance, business unit, profit, tax, cost and other information of the target enterprise user.
The second preset algorithm may be a template, edge, gray scale, spatial transformation, etc. algorithm.
And inputting the first target enterprise user operation information into a third preset algorithm to perform clustering division to obtain second target enterprise user operation information.
The third preset algorithm may be a decomposition, K-means, center point, etc. algorithm.
And determining a plurality of second types of service recommendation information corresponding to the second target enterprise user operation information, the target enterprise user registration information and the target enterprise user business information and occurrence frequency of the service recommendation information of each second type according to the preset corresponding relation between the enterprise user operation information, the enterprise user registration information and the types of the enterprise user business information and the service recommendation information.
For example, the second target enterprise user operation information is intellectual property transfer, the enterprise user registration information characterizes that the number of staff of the enterprise is small, the enterprise user business information characterizes that the company funds are sufficient and the running water is more, and the second type of service recommendation information corresponding to the target enterprise user business information is intellectual property recommendation service. The second target enterprise user operation information is electronic equipment safety (no data or very much data), the enterprise user registration information characterizes that the operation range comprises electronic equipment production, manufacturing and sales, and the enterprise user business information characterizes that the enterprise transaction flow is more, and then the electronic equipment safety detection service is recommended.
And S16, summarizing and sorting the first enterprise user service recommendation information and the second enterprise user service recommendation information according to the occurrence frequency of the service recommendation information of each type to obtain target enterprise user service recommendation sorting information.
Because the service recommendation information of the same type of recommendation appears in the first enterprise user service recommendation information and the second enterprise user service recommendation information, the occurrence frequency of the service recommendation information of the same type is summarized during sorting, and then sorting is performed.
In one embodiment, S16 may include:
s160, acquiring reference enterprise user information of a reference enterprise user, wherein the reference enterprise user information comprises: reference to enterprise user registration information, reference to enterprise user business information, reference to enterprise user operation information, and reference to enterprise user behavior information.
S162, inputting the reference enterprise user information and the target enterprise user information into a fourth preset algorithm for comparison, and outputting similar parameter information representing the similarity degree of the reference enterprise user information and the target enterprise user information.
The fourth preset algorithm may be a cosine angle algorithm or a pearson correlation coefficient algorithm.
S164, acquiring historical enterprise user service information of the reference enterprise user.
Because the service requirements for similar enterprises are generally similar, a further determination of the targeted enterprise user service recommendation ranking information is made based on historical enterprise user service information of reference enterprise users.
S166, the historical enterprise user service information of the reference enterprise user corresponding to the similar parameter information which is not smaller than the preset threshold value is input into a fifth preset algorithm for filtering, and the third enterprise user service recommendation information is output.
The fifth preset algorithm may be a coordinated filtering algorithm.
The third enterprise user service recommendation information includes a plurality of third types of service recommendation information and frequency of occurrence of the respective third types of service recommendation information.
And S168, summarizing and sorting the first enterprise user service recommendation information, the second enterprise user service recommendation information and the third enterprise user service information according to the occurrence frequency of the service recommendation information of each type to obtain target enterprise user service recommendation sorting information.
In one embodiment, S168 may include:
s1680, acquiring historical service browsing information of a target enterprise user and historical service ordering information of the target enterprise user.
S1682, inputting the historical service browsing information of the target enterprise user and the historical service ordering information of the target enterprise user into a third preset algorithm to conduct partition clustering, and outputting the service information of the fourth enterprise user.
The fourth enterprise user service recommendation information includes a plurality of fourth types of service recommendation information, and frequency of occurrence of the respective fourth types of service recommendation information.
And S1684, summarizing and sorting the first enterprise user service recommendation information, the second enterprise user service information, the third enterprise user service information and the fourth service type according to the occurrence frequency of the service recommendation information of each type to obtain target enterprise user service recommendation sorting information.
S18, recommending the enterprise user service recommendation information to the enterprise user equipment according to the target enterprise user service recommendation ranking information.
According to the enterprise user service recommendation method, real service requirements of enterprise users are considered in a multidimensional manner, enterprise user service recommendation information is determined through enterprise user registration information, enterprise user business information, enterprise user operation information, enterprise user behavior information, reference enterprise user information of reference enterprise users, enterprise user historical service browsing information of enterprise users and enterprise user historical service ordering information, the enterprise user service recommendation information is ordered according to occurrence frequency of service recommendation information of each type, comprehensive service recommendation information can be provided for the enterprise users, and real service requirements of the enterprise users are matched.
FIG. 1 depicts a recommendation method for enterprise user services, and an apparatus provided by an embodiment of the present application is described below with reference to FIG. 2 and FIG. 3.
Fig. 2 is a schematic structural diagram of a recommendation device for enterprise user services according to an embodiment of the present application, where each module in the device shown in fig. 2 has a function of implementing each step in fig. 1, and achieves the corresponding technical effects. As shown in fig. 2, the apparatus may include:
the obtaining module 20 is configured to obtain target enterprise user information of a target enterprise user, where the target enterprise user information includes: target enterprise user registration information, target enterprise user business information, target enterprise user operation information, and target enterprise user behavior information.
The target enterprise user information of the enterprise user can be obtained from an internet platform, and specifically, the enterprise user registration information includes: account number, contact way, address and social unified credit code; according to the unified credit code, the basic information of the enterprise can be captured, wherein the basic information comprises fund sources (stock book composition), registered capital, investment checking reports, operation ranges, account opening banks and accounts, asset liability lists, damage lists, cash flow tables, enterprise residence places and stakeholders, names, resume and legal representatives or names, addresses and resume of responsible persons and superior administration units of the enterprise. The enterprise user business information includes: enterprise user base information, administrative license information, administrative penalty information, abnormal directory information, and trust loss information. The enterprise user operation information includes: company registration, company logout, company annual report, annual report abnormality, certificate repair, company change, etc., trademark registration, trademark protection, trademark change, trademark design, patent information, tax service information, proxy accounting, tax service, tax planning, annual report, project, transaction, money exchange, tax. The enterprise user behavior information includes: retrieval, browsing, ordering, collection, scoring, frequency, sharing, and attention.
The determining module 22 is configured to determine first enterprise user service recommendation information according to the target enterprise user registration information and the target enterprise user behavior information, where the first enterprise user service recommendation information includes a plurality of first types of service recommendation information, and occurrence frequencies of the service recommendation information of the first types.
In the determining process, the target enterprise user registration information and the target enterprise user behavior information can include multiple information types, so that the determined first enterprise user service recommendation information can include multiple types.
In one embodiment, the determination module 22 may include:
the determining unit is used for determining a plurality of first types corresponding to the target enterprise user behavior information according to the preset mapping relation information of the types of the enterprise user behavior information and the service recommendation information.
For example, if the enterprise user behavior information characterizes that the enterprise user has retrieved an auto-part, the type of service recommendation information may be determined as a plurality of service types associated with the auto-part.
And the determining unit is also used for classifying the target enterprise user behavior information according to the plurality of first types and determining the number of the target enterprise user behavior information corresponding to each first type.
The output unit is used for inputting a plurality of first types and target enterprise user registration information into a first preset algorithm and outputting weights corresponding to the first types.
The first preset algorithm may be a weighting algorithm.
The occurrence frequency of the service recommendation information of each first type is the product of the number of the target enterprise user behavior information corresponding to the current first type and the weight corresponding to the current first type.
The determining module 22 is configured to determine second enterprise user service recommendation information according to the target enterprise user registration information, the target enterprise user business information, and the target enterprise user operation information, where the second enterprise user service recommendation information includes a plurality of second types of service recommendation information, and occurrence frequencies of the service recommendation information of the second types.
In one embodiment, the determination module 22 may include:
and the output unit is used for inputting the operation information of the target enterprise user into a second preset algorithm to perform information dimension reduction so as to obtain the operation information of the first target enterprise user.
The dimension-reduced information may include: operation information, establishment basic data, establishment information, bidding information, personnel, annual reports, finance, business unit, profit, tax, cost and other information of the target enterprise user.
The second preset algorithm may be a template, edge, gray scale, spatial transformation, etc. algorithm.
And the output unit is used for inputting the first target enterprise user operation information into a third preset algorithm to perform partition clustering to obtain the second target enterprise user operation information.
The third preset algorithm may be a decomposition, K-means, center point, etc. algorithm.
The determining unit is used for determining a plurality of second types of service recommendation information corresponding to the second target enterprise user operation information, the target enterprise user registration information and the target enterprise user business information and occurrence frequency of the service recommendation information of each second type according to the preset corresponding relation between the enterprise user operation information, the enterprise user registration information, the enterprise user business information and the service recommendation information of each second type.
For example, the second target enterprise user operation information is intellectual property transfer, the enterprise user registration information characterizes that the number of staff of the enterprise is small, the enterprise user business information characterizes that the company funds are sufficient and the running water is more, and the second type of service recommendation information corresponding to the target enterprise user business information is intellectual property recommendation service. The second target enterprise user operation information is electronic equipment safety (no data or very much data), the enterprise user registration information characterizes that the operation range comprises electronic equipment production, manufacturing and sales, and the enterprise user business information characterizes that the enterprise transaction flow is more, and then the electronic equipment safety detection service is recommended.
The ranking module 24 is configured to aggregate and rank the first enterprise user service recommendation information and the second enterprise user service recommendation information according to the occurrence frequency of each type of service recommendation information, so as to obtain target enterprise user service recommendation ranking information.
Because the service recommendation information of the same type of recommendation appears in the first enterprise user service recommendation information and the second enterprise user service recommendation information, the occurrence frequency of the service recommendation information of the same type is summarized during sorting, and then sorting is performed.
In one embodiment, ranking module 24 may include:
the obtaining unit 240 is configured to obtain reference enterprise user information of a reference enterprise user, where the reference enterprise user information includes: reference to enterprise user registration information, reference to enterprise user business information, reference to enterprise user operation information, and reference to enterprise user behavior information.
And the output unit 242 is configured to input the reference enterprise user information and the target enterprise user information into a fourth preset algorithm for comparison, and output similar parameter information representing the similarity degree of the reference enterprise user information and the target enterprise user information.
The fourth preset algorithm may be a cosine angle algorithm or a pearson correlation coefficient algorithm.
And an obtaining unit 240, configured to obtain historical enterprise user service information of the reference enterprise user.
Because the service requirements for similar enterprises are generally similar, a further determination of the targeted enterprise user service recommendation ranking information is made based on historical enterprise user service information of reference enterprise users.
And the output unit 242 is configured to input the historical enterprise user service information of the reference enterprise user corresponding to the similar parameter information not smaller than the preset threshold value into a fifth preset algorithm for filtering, and output the third enterprise user service recommendation information.
The fifth preset algorithm may be a coordinated filtering algorithm.
The third enterprise user service recommendation information includes a plurality of third types of service recommendation information and frequency of occurrence of the respective third types of service recommendation information.
The ranking unit 244 is configured to aggregate and rank the first enterprise user service recommendation information, the second enterprise user service recommendation information, and the third enterprise user service information according to the occurrence frequency of each type of service recommendation information, so as to obtain target enterprise user service recommendation ranking information.
In one embodiment, the sorting unit 244 may include:
the acquiring subunit 2440 is configured to acquire the historical service browsing information of the target enterprise user and the historical service ordering information of the target enterprise user.
And the input subunit 2442 is configured to input the historical service browsing information of the target enterprise user and the ordering information of the historical service of the target enterprise user into a third preset algorithm for partition and clustering, and output the service information of the fourth enterprise user.
The fourth enterprise user service recommendation information includes a plurality of fourth types of service recommendation information, and frequency of occurrence of the respective fourth types of service recommendation information.
The sorting sub-unit 2444 is configured to aggregate and sort the first enterprise user service recommendation information, the second enterprise user service information, the third enterprise user service information, and the fourth service type according to the occurrence frequency of the service recommendation information of each type, so as to obtain target enterprise user service recommendation sorting information.
And a recommending module 26, configured to recommend enterprise user service recommendation information to the enterprise user equipment according to the target enterprise user service recommendation ranking information.
The recommendation device for enterprise user service in the embodiment of the application considers the real service demands of enterprise users in a multidimensional way, determines the enterprise user service recommendation information by the enterprise user registration information, the enterprise user business information, the enterprise user operation information, the enterprise user behavior information, the reference enterprise user information of the reference enterprise user, the enterprise user history service browsing information of the enterprise user and the enterprise user history service ordering information, sorts the enterprise user service recommendation information according to the occurrence frequency of each type of service recommendation information, can provide more comprehensive service recommendation information for the enterprise user, and is more matched with the real service demands of the enterprise user.
FIG. 3 is a schematic diagram of a recommendation device for enterprise user services according to one embodiment of the present application. As shown in fig. 3, the device may include a processor 301 and a memory 302 storing computer program instructions.
In particular, the processor 301 may include a central processing unit (Central Processing Unit, CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured as one or more integrated circuits implementing embodiments of the present application.
Memory 302 may include mass storage for information or instructions. By way of example, and not limitation, memory 302 may comprise a Hard Disk Drive (HDD), floppy Disk Drive, flash memory, optical Disk, magneto-optical Disk, magnetic tape, or universal serial bus (Universal Serial Bus, USB) Drive, or a combination of two or more of the foregoing. In one example, memory 302 may include removable or non-removable (or fixed) media, or memory 302 may be a non-volatile solid state memory. Memory 302 may be internal or external to the integrated gateway disaster recovery device.
In one example, memory 302 may be Read Only Memory (ROM). In one example, the ROM may be mask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically Erasable PROM (EEPROM), electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.
The processor 301 reads and executes the computer program instructions stored in the memory 302 to implement the method in the embodiment shown in fig. 1, and achieves the corresponding technical effects achieved by executing the method in the embodiment shown in fig. 1, which will not be described herein for brevity.
In one example, the recommendation device for enterprise user services may also include a communication interface 303 and a bus 310. As shown in fig. 3, the processor 301, the memory 302, and the communication interface 303 are connected to each other by a bus 310 and perform communication with each other.
The communication interface 303 is mainly used to implement communication between each module, device, unit and/or apparatus in the embodiment of the present application.
Bus 310 includes hardware, software, or both that couple the components of the online information-flow billing device to each other. By way of example, and not limitation, the buses may include an accelerated graphics port (Accelerated Graphics Port, AGP) or other graphics Bus, an enhanced industry standard architecture (Extended Industry Standard Architecture, EISA) Bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an industry standard architecture (Industry Standard Architecture, ISA) Bus, an infiniband interconnect, a Low Pin Count (LPC) Bus, a memory Bus, a micro channel architecture (MCa) Bus, a Peripheral Component Interconnect (PCI) Bus, a PCI-Express (PCI-X) Bus, a Serial Advanced Technology Attachment (SATA) Bus, a video electronics standards association local (VLB) Bus, or other suitable Bus, or a combination of two or more of the above. Bus 310 may include one or more buses, where appropriate. Although embodiments of the application have been described and illustrated with respect to a particular bus, the application contemplates any suitable bus or interconnect.
The recommendation equipment of the enterprise user service can execute the recommendation method of the enterprise user service in the embodiment of the application, thereby realizing the corresponding technical effects of the recommendation method of the enterprise user service described in fig. 1.
In addition, in combination with the recommendation method of the enterprise user service in the above embodiment, the embodiment of the application can be implemented by providing a computer storage medium. The computer storage medium has stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement a recommendation method for an enterprise user service in any of the above embodiments.
It should be understood that the application is not limited to the particular arrangements and instrumentality described above and shown in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between steps, after appreciating the spirit of the present application.
The functional blocks shown in the above-described structural block diagrams may be implemented in hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by an information signal carried in a carrier wave. A "machine-readable medium" may include any medium that can store or transfer information. Examples of machine-readable media include electronic circuitry, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and the like. The code segments may be downloaded via computer networks such as the internet, intranets, etc.
It should also be noted that the exemplary embodiments mentioned in this disclosure describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, or may be performed in a different order from the order in the embodiments, or several steps may be performed simultaneously.
Aspects of the present application are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable information processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable information processing apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to being, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware which performs the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In the foregoing, only the specific embodiments of the present application are described, and it will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the systems, modules and units described above may refer to the corresponding processes in the foregoing method embodiments, which are not repeated herein. It should be understood that the scope of the present application is not limited thereto, and any equivalent modifications or substitutions can be easily made by those skilled in the art within the technical scope of the present application, and they should be included in the scope of the present application.
Claims (8)
1. A method for recommending enterprise user services, comprising:
obtaining target enterprise user information of a target enterprise user, wherein the target enterprise user information comprises: target enterprise user registration information, target enterprise user business information, target enterprise user operation information, and target enterprise user behavior information;
determining first enterprise user service recommendation information according to the target enterprise user registration information and the target enterprise user behavior information, wherein the first enterprise user service recommendation information comprises a plurality of first types of service recommendation information and occurrence frequency of the service recommendation information of each first type;
Determining second enterprise user service recommendation information according to the target enterprise user registration information, the target enterprise user business information and the target enterprise user operation information, wherein the second enterprise user service recommendation information comprises a plurality of second types of service recommendation information and occurrence frequencies of the service recommendation information of each second type;
summarizing and sorting the first enterprise user service recommendation information and the second enterprise user service recommendation information according to the occurrence frequency of each type of service recommendation information to obtain target enterprise user service recommendation sorting information;
recommending enterprise user service recommendation information to the enterprise user equipment according to the target enterprise user service recommendation ranking information;
wherein determining the first enterprise user service recommendation information according to the target enterprise user registration information and the target enterprise user behavior information includes:
determining a plurality of first types corresponding to the target enterprise user behavior information according to mapping relation information of types of preset enterprise user behavior information and service recommendation information;
classifying the target enterprise user behavior information according to the plurality of first types, and determining the number of the target enterprise user behavior information corresponding to each first type;
Inputting the first types and the target enterprise user registration information into a first preset algorithm, and outputting weights corresponding to the first types;
the occurrence frequency of the service recommendation information of each first type is the product of the number of the target enterprise user behavior information corresponding to the current first type and the weight corresponding to the current first type.
2. The method of claim 1, wherein determining the second enterprise user service recommendation information based on the target enterprise user registration information, the target enterprise user business information, and the target enterprise user operation information comprises:
inputting the target enterprise user operation information into a second preset algorithm to perform information dimension reduction to obtain first target enterprise user operation information;
inputting the first target enterprise user operation information into a third preset algorithm to perform clustering division to obtain second target enterprise user operation information;
and determining the second target enterprise user operation information, the target enterprise user registration information, a plurality of second types of service recommendation information corresponding to the target enterprise user business information and the occurrence frequency of the service recommendation information of each second type according to the preset corresponding relation between the enterprise user operation information, the enterprise user registration information, the enterprise user business information and the types of the service recommendation information.
3. The method for recommending enterprise user services of claim 1, wherein the step of summarizing and ranking the first enterprise user service recommendation information and the second enterprise user service recommendation information according to the occurrence frequency of each type of service recommendation information to obtain target enterprise user service recommendation ranking information comprises:
acquiring reference enterprise user information of a reference enterprise user, wherein the reference enterprise user information comprises: reference to enterprise user registration information, reference to enterprise user business information, reference to enterprise user operation information, and reference to enterprise user behavior information;
inputting the reference enterprise user information and the target enterprise user information into a fourth preset algorithm for comparison, and outputting similar parameter information representing the similarity degree of the reference enterprise user information and the target enterprise user information;
acquiring historical enterprise user service information of the reference enterprise user;
inputting the historical enterprise user service information of the reference enterprise user corresponding to the similar parameter information which is not smaller than a preset threshold value into a fifth preset algorithm for filtering, and outputting third enterprise user service recommendation information;
The third enterprise user service recommendation information comprises a plurality of third types of service recommendation information and occurrence frequency of the service recommendation information of each third type;
and summarizing and sorting the first enterprise user service recommendation information, the second enterprise user service recommendation information and the third enterprise user service recommendation information according to the occurrence frequency of each type of service recommendation information to obtain the target enterprise user service recommendation sorting information.
4. The method for recommending enterprise user services of claim 3, wherein the summarizing and ranking the first enterprise user service recommendation information, the second enterprise user service recommendation information, and the third enterprise user service recommendation information according to occurrence frequencies of various types of enterprise user service recommendation information to obtain the target enterprise user service recommendation ranking information comprises:
acquiring historical service browsing information of a target enterprise user and historical service ordering information of the target enterprise user;
inputting the historical service browsing information of the target enterprise user and the ordering information of the historical service of the target enterprise user into a third preset algorithm for partition clustering, and outputting the recommendation information of the fourth enterprise user service;
The fourth enterprise user service recommendation information comprises a plurality of fourth types of service recommendation information and occurrence frequency of the service recommendation information of each fourth type;
and summarizing and sorting the first enterprise user service recommendation information, the second enterprise user service recommendation information, the third enterprise user service recommendation information and the fourth enterprise user service recommendation information according to the occurrence frequency of each type of service recommendation information to obtain the target enterprise user service recommendation sorting information.
5. A recommendation device for enterprise user services, comprising:
the system comprises an acquisition module, a storage module and a storage module, wherein the acquisition module is used for acquiring target enterprise user information of target enterprise users, and the target enterprise user information comprises: target enterprise user registration information, target enterprise user business information, target enterprise user operation information, and target enterprise user behavior information;
the determining module is used for determining first enterprise user service recommendation information according to the target enterprise user registration information and the target enterprise user behavior information, wherein the first enterprise user service recommendation information comprises a plurality of first types of service recommendation information and occurrence frequencies of the service recommendation information of each first type;
The determining module is further configured to determine second enterprise user service recommendation information according to the target enterprise user registration information, the target enterprise user business information, and the target enterprise user operation information, where the second enterprise user service recommendation information includes a plurality of second types of service recommendation information, and occurrence frequencies of the service recommendation information of each second type;
the ordering module is used for summarizing and ordering the first enterprise user service recommendation information and the second enterprise user service recommendation information according to the occurrence frequency of the service recommendation information of each type to obtain target enterprise user service recommendation ordering information;
the recommending module is used for recommending the enterprise user service recommending information to the enterprise user equipment according to the target enterprise user service recommending and sorting information;
the determining module is specifically configured to:
determining a plurality of first types corresponding to the target enterprise user behavior information according to mapping relation information of types of preset enterprise user behavior information and service recommendation information;
classifying the target enterprise user behavior information according to the plurality of first types, and determining the number of the target enterprise user behavior information corresponding to each first type;
Inputting the first types and the target enterprise user registration information into a first preset algorithm, and outputting weights corresponding to the first types;
the occurrence frequency of the service recommendation information of each first type is the product of the number of the target enterprise user behavior information corresponding to the current first type and the weight corresponding to the current first type.
6. The recommendation device for enterprise user services of claim 5, wherein the determination module is specifically configured to:
inputting the target enterprise user operation information into a second preset algorithm to perform information dimension reduction to obtain first target enterprise user operation information;
inputting the first target enterprise user operation information into a third preset algorithm to perform clustering division to obtain second target enterprise user operation information;
and determining the second target enterprise user operation information, the target enterprise user registration information, a plurality of second types of service recommendation information corresponding to the target enterprise user business information and the occurrence frequency of the service recommendation information of each second type according to the preset corresponding relation between the enterprise user operation information, the enterprise user registration information, the enterprise user business information and the types of the service recommendation information.
7. A recommendation device for enterprise user services, comprising: memory, a processor and a computer program stored on the memory and executable on the processor, which when executed by the processor implements the recommendation method for enterprise user services according to any of claims 1 to 4.
8. A computer-readable storage medium, wherein a program for realizing information transfer is stored on the computer-readable storage medium, and when executed by a processor, the program realizes the recommendation method for the enterprise user service according to any one of claims 1 to 4.
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