CN111353093A

CN111353093A - Question recommendation method and device, server and readable storage medium

Info

Publication number: CN111353093A
Application number: CN201811585301.XA
Authority: CN
Inventors: 张姣姣
Original assignee: Beijing Didi Infinity Technology and Development Co Ltd
Current assignee: Beijing Didi Infinity Technology and Development Co Ltd
Priority date: 2018-12-24
Filing date: 2018-12-24
Publication date: 2020-06-30
Anticipated expiration: 2038-12-24
Also published as: CN111353093B

Abstract

The embodiment of the application provides a problem recommendation method, a problem recommendation device, a server and a readable storage medium, wherein historical service record data are input into a pre-trained collaborative filtering model, a main class recommendation problem is predicted, then a target main class problem is selected, the historical service record data are continuously input into a deep neural network DNN model corresponding to the target main class problem, a specific sub class recommendation problem under the target main class problem is predicted, and finally an output problem recommendation menu can simultaneously comprise the main class recommendation problem and the specific sub class recommendation problem. Therefore, by using the characteristics of outstanding problem characteristics and high prediction accuracy of the main recommendation problem, the main recommendation problem is predicted first, and then the subclass recommendation problem is predicted, so that the prediction accuracy can be effectively improved.

Description

Question recommendation method and device, server and readable storage medium

Technical Field

The present application relates to the field of computer technologies, and in particular, to a problem recommendation method, apparatus, server, and readable storage medium.

Background

At present, along with the popularization of intelligent terminals, various Applications (APPs) providing life convenience services are also in endless range, and provide services (such as travel services, take-away services, and the like) for people who eat and wear the mobile terminal. During the process of using these services, users often encounter various problems to be solved and need to consult with the service platform. The existing service platform is difficult to realize accurate personalized recommendation aiming at the problem to be solved by the user, and particularly, the problem of the user in the service using process can not be quickly positioned when the user consults, so that the user experience is reduced.

Disclosure of Invention

In view of the above, an object of the embodiments of the present application is to provide a problem recommendation method, apparatus, server and readable storage medium, so as to solve or improve the above problem.

According to an aspect of embodiments of the present application, there is provided an electronic device that may include one or more storage media and one or more processors in communication with the storage media. One or more storage media store machine-readable instructions executable by a processor. When the electronic equipment runs, the processor is communicated with the storage medium through the bus, and the processor executes the machine readable instructions to execute the problem recommendation method.

According to another aspect of the embodiments of the present application, there is provided a question recommendation method applied to a server, where the method may include:

acquiring historical service record data of a service requester according to a problem acquisition request sent by a service requester terminal, wherein the historical service record data comprises one or more combinations of service requester portrait characteristics, service order data, service provider portrait characteristics and service spatio-temporal information;

inputting the historical service record data into a pre-trained collaborative filtering model to obtain the confidence coefficient of each candidate main-class problem, and selecting a target main-class problem from each candidate main-class problem according to the confidence coefficient of each candidate main-class problem;

inputting the historical service record data into a deep neural network DNN model corresponding to the target main class problem to obtain the confidence of each candidate subclass problem under the target main class problem;

and sending a question recommendation menu to the service requester terminal according to the confidence degrees of the candidate main-class questions and the confidence degrees of the candidate sub-class questions under the target main-class question, wherein the question recommendation menu comprises the main-class recommendation questions and the sub-class recommendation questions under the target main-class question.

In a possible implementation manner, the step of acquiring historical service record data of the service requester according to the question acquisition request sent by the service requester terminal may include:

acquiring user information of the service requester from the question acquisition request, wherein the user information comprises at least one of an incoming call number, a user account and a user biological characteristic;

and acquiring historical service record data of the service requester from a historical service record database stored in the server according to the user information.

In a possible implementation, the service requester profile feature at least includes basic information of the service requester, service usage frequency, service type distribution and complaint problem distribution, the service provider portrait characteristics at least comprise basic information of the service provider, average service statistical data and complained question distribution, the service order data includes order statistics of a most recent preset number of service orders, the service spatio-temporal information includes time information, location information, and thermodynamic diagram information when the problem acquisition request is transmitted, wherein the basic information of the service requester includes age, gender, service level and occupation, the basic information of the service provider includes age, gender and service level, and the average service statistics includes average service duration per day and average income per day.

In a possible implementation, the method may further include a step of training a collaborative filtering model in advance, specifically including:

acquiring a main class training sample set, wherein the main class training sample set comprises main class problem service record data of each service requester and candidate main class problems associated with the main class problem service record data, and the problem service record data comprises service requester portraits, service order data, service provider portraits and service spatio-temporal information;

and training by adopting a collaborative filtering algorithm according to the training sample set to obtain the collaborative filtering model.

In a possible implementation manner, the method may further include a step of training a DNN model corresponding to each candidate main-class problem, specifically including:

acquiring a subclass training sample set of each candidate main class problem, wherein the subclass training sample set comprises subclass problem service record data of each service requester and candidate subclass problems associated with the subclass problem service record data, and the problem service record data comprises a service requester portrait, service order data, a service provider portrait and service spatio-temporal information;

and training by adopting a DNN algorithm according to each subclass training sample set to obtain a DNN model of each candidate main class problem.

In a possible implementation manner, the step of selecting a target major problem from the candidate major problems according to the confidence of the candidate major problems may include:

generating a sequencing result of each candidate main class problem according to the confidence coefficient of each candidate main class problem and the descending order of the confidence coefficient;

selecting at least one candidate main-class problem ranked at the top as the target main-class problem according to the ranking result; or

And taking the candidate main class problem with the confidence coefficient larger than the preset confidence coefficient as the target main class problem.

In a possible implementation manner, the step of sending a question recommendation menu to the service requester terminal according to the confidence level of each candidate main-class question and the confidence level of each candidate sub-class question under the target main-class question may include:

according to the confidence degrees of the candidate main problems, selecting a first preset number of candidate main problems as main recommendation problems according to the descending order of the confidence degrees;

selecting a second preset number of candidate subclasses of problems as subclass recommendation problems according to the confidence degrees of the candidate subclasses of problems under the target main class of problems and the descending order of the confidence degrees;

and generating a problem recommendation menu according to the main class recommendation problem and the sub class recommendation problem under the target main class problem, and sending the problem recommendation menu to the service requester terminal.

according to the confidence degree of each candidate main class problem, taking the candidate main class problem with the confidence degree larger than the first confidence degree as a main class recommendation problem;

according to the confidence degree of each candidate subclass problem under the target main class problem, taking the candidate subclass problem with the confidence degree larger than the second confidence degree as a subclass recommendation problem;

In a possible implementation manner, the step of sending the question recommendation menu to the service requester terminal may include:

and sending the voice data of the main-class recommendation problem and the sub-class recommendation problem under the target main-class problem in the problem recommendation menu to the service requester terminal so that the service requester terminal plays the voice data of the main-class recommendation problem and the sub-class recommendation problem under the target main-class problem to the service requester.

and sending the main-class recommendation problem and the sub-class recommendation problem under the target main-class problem included in the problem recommendation menu to the service requester terminal so that the service requester terminal displays the main-class recommendation problem and the sub-class recommendation problem under the target main-class problem to a user.

In a possible implementation manner, after the step of sending a question recommendation menu to the service requester terminal according to the confidence level of each candidate main-class question and the confidence level of each candidate sub-class question under the target main-class question, the method may further include:

acquiring a target recommendation problem selected by the service requester terminal from the main class recommendation problem and the sub class recommendation problem under the target main class problem;

judging whether the target recommendation problem is a main recommendation problem or not;

if the target recommendation problem is a main recommendation problem, inputting the historical service record data into a DNN model corresponding to the main recommendation problem to obtain the confidence of each candidate sub-problem under the main recommendation problem;

sending the subclass recommendation problem under the main class recommendation problem to the service requester terminal according to the confidence of each candidate subclass problem;

and if the target recommendation problem is a subclass recommendation problem, processing the subclass recommendation problem according to the problem processing strategy of the subclass recommendation problem.

According to another aspect of the embodiments of the present application, there is provided a question recommendation apparatus, applied to a server, the apparatus including:

the acquisition module can be used for acquiring historical service record data of the service requester according to a problem acquisition request sent by a service requester terminal, wherein the historical service record data comprises one or more combinations of service requester portrait characteristics, service order data, service provider portrait characteristics and service spatio-temporal information;

the first input module may be configured to input the historical service record data into a pre-trained collaborative filtering model, obtain a confidence level of each candidate main-class problem, and select a target main-class problem from the candidate main-class problems according to the confidence level of each candidate main-class problem;

the second input module may be configured to input the historical service record data into a deep neural network DNN model corresponding to the target major problem, so as to obtain a confidence level of each candidate sub-class problem under the target major problem;

the menu sending module may be configured to send a problem recommendation menu to the service requester terminal according to the confidence level of each candidate main-class problem and the confidence level of each candidate sub-class problem under the target main-class problem, where the problem recommendation menu includes a main-class recommendation problem and a sub-class recommendation problem under the target main-class problem.

According to another aspect of embodiments of the present application, there is provided a readable storage medium, on which a computer program is stored, the computer program being executable by a processor to perform the steps of the problem recommendation method described above.

Based on any one of the above aspects, the embodiment of the application provides a problem recommendation method, device, server and readable storage medium, by inputting historical service record data into a pre-trained collaborative filtering model, a main class recommendation problem is predicted, then a target main class problem is selected, the historical service record data is continuously input into a deep neural network DNN model corresponding to the target main class problem, a specific sub class recommendation problem under the target main class problem is predicted, and a finally output problem recommendation menu can simultaneously include the main class recommendation problem and the specific sub class recommendation problem. Therefore, by using the characteristics of outstanding problem characteristics and high prediction accuracy of the main recommendation problem, the main recommendation problem is predicted first, and then the subclass recommendation problem is predicted, so that the prediction accuracy can be effectively improved.

In order to make the aforementioned objects, features and advantages of the embodiments of the present application more comprehensible, embodiments accompanied with figures are described in detail below.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.

FIG. 1 is a schematic block diagram illustrating interaction of a question recommendation system provided by an embodiment of the present application;

FIG. 2 illustrates a schematic diagram of exemplary hardware and software components of an electronic device that may implement the server, the service requester terminal, and the service provider terminal of FIG. 1 provided by an embodiment of the present application;

FIG. 3 is a flow chart illustrating a problem recommendation method provided by an embodiment of the present application;

FIG. 4 is a functional block diagram of an issue recommendation apparatus provided in an embodiment of the present application;

FIG. 5 is a block diagram of another functional module of the problem recommendation device provided in the embodiment of the present application;

fig. 6 shows another functional block diagram of the problem recommendation device provided in the embodiment of the present application.

Detailed Description

In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it should be understood that the drawings in the present application are for illustrative and descriptive purposes only and are not used to limit the scope of protection of the present application. Additionally, it should be understood that the schematic drawings are not necessarily drawn to scale. The flowcharts used in this application illustrate operations implemented according to some of the embodiments of the present application. It should be understood that the operations of the flow diagrams may be performed out of order, and steps without logical context may be performed in reverse order or simultaneously. One skilled in the art, under the guidance of this application, may add one or more other operations to, or remove one or more operations from, the flowchart.

In addition, the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.

To enable those skilled in the art to utilize the present disclosure, the following embodiments are presented in conjunction with a specific application scenario, "a network appointment scenario". It will be apparent to those skilled in the art that the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the application. Although the present application is described primarily in the context of a "net appointment scenario," it should be understood that this is merely one exemplary embodiment. The application can be applied to any other traffic type. For example, the present application may be applied to different transportation system environments, including terrestrial, marine, or airborne, among others, or any combination thereof. The vehicle of the transportation system may include a taxi, a private car, a windmill, a bus, a train, a bullet train, a high speed rail, a subway, a ship, an airplane, a spacecraft, a hot air balloon, or an unmanned vehicle, etc., or any combination thereof. The application can also comprise any service system for online taxi taking, for example, a system for sending and/or receiving express delivery, and a service system for business transaction of buyers and sellers. Applications of the system or method of the present application may include web pages, plug-ins for browsers, client terminals, customization systems, internal analysis systems, or artificial intelligence robots, among others, or any combination thereof.

It should be noted that in the embodiments of the present application, the term "comprising" is used to indicate the presence of the features stated hereinafter, but does not exclude the addition of further features.

The terms "passenger," "requestor," "service person," "service requestor," and "customer" are used interchangeably in this application to refer to an individual, entity, or tool that can request or order a service. The terms "driver," "provider," "service provider," and "provider" are used interchangeably in this application to refer to an individual, entity, or tool that can provide a service. The term "user" in this application may refer to an individual, entity or tool that requests a service, subscribes to a service, provides a service, or facilitates the provision of a service. For example, the user may be a passenger, a driver, an operator, etc., or any combination thereof. In the present application, "passenger" and "passenger terminal" may be used interchangeably, and "driver" and "driver terminal" may be used interchangeably.

In light of the above background, a scenario in which a user makes a service call is further described below. At present, when a user dials a customer service call, a recording menu is issued to the user for playing. Taking the car booking service as an example, the recording menu issued to the user may include these service options: passenger questions press 1, driver questions press 2, cost questions press 3, safety questions press 4, manual service press 5, etc., and generally, passenger questions, driver questions, cost questions, safety questions may include at least one level of other service options. For example, when the user presses the charge question 3, the user may also play the questions of the charge question, such as the detour question press 1, the surcharge question press 2, the charge question press 3 even if the charging is over, the charge question press 4 if the user is not seated, and so on.

Therefore, when a user consults a problem, the user needs to listen to the menu for broadcasting in multiple levels, and accurate recommendation and personalized recommendation cannot be achieved, so that the waiting time of the user is increased, the problem of loss is serious, and the problem of the user is difficult to solve in time.

Before the technical solutions provided by the following embodiments are provided, the inventor of the present application finds, through research, that in an existing solution, a plurality of specific problems known by a service platform are generally collected as classification targets, and ranking is performed according to probability values appearing in the classification targets, so as to recommend a problem ranked first, for example, a problem ranked top ten, to a user who consults a problem. However, the above scheme has a problem that a low probability problem is not easily predicted, resulting in a low overall prediction accuracy. Moreover, the features among all the classification targets are not obvious, and even if the classification targets are used for training the deep network model for prediction, the accuracy of actual prediction of the finally-trained deep network model is low.

In addition, the inventor also finds that in the existing scheme, a multi-layer menu mode can be used, the overall major problem of the first-layer menu is firstly predicted, and the detailed minor problem prediction is carried out on the second-time menu under the overall major problem. However, the multi-layer menu mode always requires the user to repeatedly perform the multi-layer menu operation, which increases the waiting time of the user and is difficult to accurately position the user. Because it is necessary for the user to directly solve the problem concerned, that is, it is more desirable to directly predict the problem required by the user to solve, specifically solving the problem.

Based on the above technical findings, embodiments of the present application provide a problem recommendation method, apparatus, server, and readable storage medium, where historical service record data of a user is analyzed according to a target service item selected by the user from a distributed problem recommendation menu, a problem recommendation path is determined according to an analysis result, and then a problem acquisition request is distributed according to the determined problem recommendation path, so that intelligent distribution of the problem acquisition request is achieved according to historical service record data of different users, and the user can find a channel for solving a problem of the user more quickly, thereby improving user experience.

FIG. 1 is an architectural diagram of a problem recommendation system 100 provided by an alternative embodiment of the present application. For example, the issue recommendation system 100 may be an online transportation service platform relied upon for transportation services such as taxi service, designated drive service, express service, carpool service, bus service, driver rental service, or regular service, or a combination of any of the above. The issue recommendation system 100 may include a server 110, a network 120, a service requester terminal 130, a service provider terminal 140, and a database 150, and the server 110 may include a processor therein that performs an instruction operation. The question recommendation system 100 shown in FIG. 1 is only one possible example, and in other possible embodiments, the question recommendation system 100 may include only some of the components shown in FIG. 1 or may include other components.

In some embodiments, the server 110 may be a single server or a group of servers. The set of servers can be centralized or distributed (e.g., the servers 110 can be a distributed system). In some embodiments, the server 110 may be local or remote to the terminal. For example, the server 110 may access information stored in the service requester terminal 130, the service provider terminal 140, and the database 150, or any combination thereof, via the network 120. As another example, the server 110 may be directly connected to at least one of the service requester terminal 130, the service provider terminal 140, and the database 150 to access information and/or data stored therein. In some embodiments, the server 110 may be implemented on a cloud platform; by way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud (community cloud), a distributed cloud, an inter-cloud, a multi-cloud, and the like, or any combination thereof. In some embodiments, the server 110 may be implemented on an electronic device 200 having one or more of the components shown in FIG. 2 in the present application.

In some embodiments, the server 110 may include a processor. The processor may process information and/or data related to the service request to perform one or more of the functions described herein. For example, in a express service, the processor may determine the target vehicle based on a service request obtained from the service requester terminal 130. A processor may include one or more processing cores (e.g., a single-core processor (S) or a multi-core processor (S)). Merely by way of example, a Processor may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an Application Specific Instruction Set Processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller Unit, a reduced Instruction Set computer (reduced Instruction Set computer), a microprocessor, or the like, or any combination thereof.

Network 120 may be used for the exchange of information and/or data. In some embodiments, one or more components (e.g., server 110, service requester terminal 130, service provider terminal 140, and database 150) in the issue recommendation system 100 may send information and/or data to other components. For example, the server 110 may obtain a service request from the service requester terminal 130 via the network 120. In some embodiments, the network 120 may be any type of wired or wireless network, or combination thereof. Merely by way of example, Network 130 may include a wired Network, a Wireless Network, a fiber optic Network, a telecommunications Network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a WLAN, a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a Public Switched Telephone Network (PSTN), a bluetooth Network, a ZigBee Network, a Near Field Communication (NFC) Network, or the like, or any combination thereof. In some embodiments, network 120 may include one or more network access points. For example, the network 120 may include wired or wireless network access points, such as base stations and/or network switching nodes, through which one or more components of the issue recommendation system 100 may connect to the network 120 to exchange data and/or information.

In some embodiments, the user of the service requestor terminal 130 may be someone other than the actual demander of the service. For example, the user a of the service requester terminal 130 may use the service requester terminal 130 to initiate a service request for the service actual demander B (for example, the user a may call a car for his friend B), or receive service information or instructions from the server 110. In some embodiments, the user of the service provider terminal 140 may be the actual provider of the service or may be another person than the actual provider of the service. For example, user C of the service provider terminal 140 may use the service provider terminal 140 to receive a service request serviced by the service provider entity D (e.g., user C may pick up an order for driver D employed by user C), and/or information or instructions from the server 110. In some embodiments, "service requester" and "service requester terminal" may be used interchangeably, and "service provider" and "service provider terminal" may be used interchangeably.

In some embodiments, the service requester terminal 130 may comprise a mobile device, a tablet computer, a laptop computer, or a built-in device in a motor vehicle, etc., or any combination thereof. In some embodiments, the mobile device may include a smart home device, a wearable device, a smart mobile device, a virtual reality device, an augmented reality device, or the like, or any combination thereof. In some embodiments, the smart home devices may include smart lighting devices, control devices for smart electrical devices, smart monitoring devices, smart televisions, smart cameras, or walkie-talkies, or the like, or any combination thereof. In some embodiments, the wearable device may include a smart bracelet, a smart lace, smart glass, a smart helmet, a smart watch, a smart garment, a smart backpack, a smart accessory, and the like, or any combination thereof. In some embodiments, the smart mobile device may include a smartphone, a Personal Digital Assistant (PDA), a gaming device, a navigation device, or a point of sale (POS) device, or the like, or any combination thereof. In some embodiments, the virtual reality device and/or the augmented reality device may include a virtual reality helmet, virtual reality glass, a virtual reality patch, an augmented reality helmet, augmented reality glass, an augmented reality patch, or the like, or any combination thereof. For example, the virtual reality device and/or augmented reality device may include various virtual reality products and the like. In some embodiments, the built-in devices in the motor vehicle may include an on-board computer, an on-board television, and the like.

Database 150 may store data and/or instructions. In some embodiments, the database 150 may store data obtained from the service requester terminal 130 and/or the service provider terminal 140. In some embodiments, database 150 may store data and/or instructions for the exemplary methods described herein. In some embodiments, database 150 may include mass storage, removable storage, volatile Read-write Memory, or Read-Only Memory (ROM), among others, or any combination thereof. By way of example, mass storage may include magnetic disks, optical disks, solid state drives, and the like; removable memory may include flash drives, floppy disks, optical disks, memory cards, zip disks, tapes, and the like; volatile read-write Memory may include Random Access Memory (RAM); the RAM may include Dynamic RAM (DRAM), Double data Rate Synchronous Dynamic RAM (DDR SDRAM); static RAM (SRAM), Thyristor-Based Random Access Memory (T-RAM), Zero-capacitor RAM (Zero-RAM), and the like. By way of example, ROMs may include Mask Read-Only memories (MROMs), Programmable ROMs (PROMs), Erasable Programmable ROMs (PERROMs), Electrically Erasable Programmable ROMs (EEPROMs), compact disk ROMs (CD-ROMs), digital versatile disks (ROMs), and the like. In some embodiments, database 150 may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, across clouds, multiple clouds, or the like, or any combination thereof.

In some embodiments, a database 150 may be connected to the network 120 to communicate with one or more components of the issue recommendation system 100 (e.g., the server 110, the service requester terminal 130, the service provider terminal 140, etc.). One or more components in the issue recommendation system 100 may access data or instructions stored in the database 150 via the network 120. In some embodiments, the database 150 may be directly connected to one or more components in the issue recommendation system 100 (e.g., the server 110, the service requester terminal 130, the service provider terminal 140, etc.); alternatively, in some embodiments, database 150 may also be part of server 110.

In some embodiments, one or more components (e.g., server 110, service requestor terminal 130, service provider terminal 140, etc.) in issue recommendation system 100 may have access to database 150. In some embodiments, one or more components in the issue recommendation system 100 may read and/or modify information related to a service requestor, a service provider, or the public, or any combination thereof, when certain conditions are met. For example, server 110 may read and/or modify information for one or more users after receiving a service request.

In some embodiments, the exchange of information by one or more components in the issue recommendation system 100 may be accomplished by a request service. The object of the service request may be any product. In some embodiments, the product may be a tangible product or a non-physical product. Tangible products may include food, pharmaceuticals, commodities, chemical products, appliances, clothing, automobiles, homes, or luxury goods, and the like, or any combination thereof. The non-material product may include a service product, a financial product, a knowledge product, an internet product, or the like, or any combination thereof. The internet product may include a stand-alone host product, a network product, a mobile internet product, a commercial host product, an embedded product, or the like, or any combination thereof. The internet product may be used in software, programs, or systems of the mobile terminal, etc., or any combination thereof. The mobile terminal may include a tablet, a laptop, a mobile phone, a Personal Digital Assistant (PDA), a smart watch, a Point of sale (POS) device, a vehicle-mounted computer, a vehicle-mounted television, a wearable device, or the like, or any combination thereof. The internet product may be, for example, any software and/or application used in a computer or mobile phone. The software and/or applications may relate to social interaction, shopping, transportation, entertainment time, learning, or investment, or the like, or any combination thereof. In some embodiments, the transportation-related software and/or applications may include travel software and/or applications, vehicle dispatch software and/or applications, mapping software and/or applications, and the like. In the vehicle scheduling software and/or application, the vehicle may include a horse, a carriage, a human powered vehicle (e.g., unicycle, bicycle, tricycle, etc.), an automobile (e.g., taxi, bus, privatege, etc.), a train, a subway, a ship, an airplane (e.g., airplane, helicopter, space shuttle, rocket, hot air balloon, etc.), etc., or any combination thereof.

Fig. 2 illustrates a schematic diagram of exemplary hardware and software components of an electronic device 200 of a server 110, a service requester terminal 130, and a service provider terminal 140, which may implement the concepts of the present application, provided by some embodiments of the present application. For example, the processor 220 may be used on the electronic device 200 and to perform the functions herein.

The electronic device 200 may be a general purpose computer or a special purpose computer, both of which may be used to implement the issue recommendation method of the present application. Although only a single computer is shown, for convenience, the functions described herein may be implemented in a distributed fashion across multiple similar platforms to balance processing loads.

For example, the electronic device 200 may include a network port 210 connected to a network, one or more processors 220 for executing program instructions, a communication bus 230, and a different form of storage medium 240, such as a disk, ROM, or RAM, or any combination thereof. Illustratively, the computer platform may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The method of the present application may be implemented in accordance with these program instructions. The electronic device 200 also includes an Input/Output (I/O) interface 250 between the computer and other Input/Output devices (e.g., keyboard, display screen).

For ease of illustration, only one processor is depicted in the electronic device 200. However, it should be noted that the electronic device 200 in the present application may also comprise a plurality of processors, and thus the steps performed by one processor described in the present application may also be performed by a plurality of processors in combination or individually. For example, if the processor of the electronic device 200 executes steps a and B, it should be understood that steps a and B may also be executed by two different processors together or separately in one processor. For example, a first processor performs step a and a second processor performs step B, or the first processor and the second processor perform steps a and B together.

FIG. 3 illustrates a flow diagram of a problem recommendation method provided by some embodiments of the present application, which may be performed by the server 110 shown in FIG. 1. It should be understood that, in other embodiments, the order of some steps in the problem recommendation method of the present embodiment may be interchanged according to actual needs, or some steps may be omitted or deleted. The detailed steps of the problem recommendation method are described below.

Step S110, according to the question acquisition request sent by the service requester terminal 130, acquires the historical service record data of the service requester.

The service requester (e.g., passenger, driver, etc.) can establish a session communication with the server 110 through the service requester terminal 130 in using various services (e.g., travel service, take-out service) to consult various problems to be solved through a single turn of conversation. For example, customer service calls for these services may be dialed by the service requester terminal 130 to send corresponding incoming call requests to the server 110. Taking a travel service as an example, a corresponding incoming call request may be sent to the server 110 of the travel APP through a customer service phone provided on the travel APP installed on the service requester terminal 130. For another example, the question acquisition request may also be generated by operating some indication controls (e.g., contact customer service, complaint feedback, online inquiry, etc.) in a service interface (e.g., a service interface provided by an application, a wechat applet, a WEB page, a wechat public number, etc.) presented by the service requester terminal 130, and sent to the server 110.

Prior to this, the server 110 may collect historical service record data for each service requester during use of the services by each service requester and build a historical service record database, which may include one or more combinations of service requester profile characteristics, service order data, service provider profile characteristics, and service spatio-temporal information.

In detail, the service requester portrait characteristics at least include basic information of the service requester, service usage frequency, service type distribution and complaint problem distribution, the service provider portrait characteristics at least include basic information of the service provider, average service statistical data and complaint problem distribution, the service order data includes order statistical data of the latest preset number of service orders, the service spatio-temporal information includes time information, location information and thermodynamic diagram information when a problem acquisition request is sent, wherein the basic information of the service requester includes age, gender, service level and occupation, the basic information of the service provider includes age, gender and service level, and the average service statistical data includes average service duration and average daily income.

Taking a travel service as an example, the service requester profile characteristics may include age, gender, service level and occupation of passengers, travel service usage frequency, car pool/good/non-car pool distribution, and complaint problem distribution. Service requestor profile characteristics may include age, gender, and service level of the driver, average service duration per day, average revenue per day, and complained problem distribution. The service order data may include order statistics of the latest two travel orders, and the order statistics may include order estimated cost, actual cost, estimated time, actual time, bridge fee, high speed fee, order estimated price/actual cost price value, estimated time/actual time ratio, interval from start to end to payment, and other statistics. The thermodynamic diagram information may include location information when the problem acquisition request is transmitted and an amount of travel orders for a section in which the time information is located.

It is worth noting that in order to improve the referential property of the historical service record data and enable the historical service record data to indicate the recent service use condition of each user, the service order data of each service requester should be historical data within a certain time period (for example, 20 days) before the current time node or order statistical data of two recent orders.

Thus, when the server 110 receives the question acquisition request, it first acquires the user information of the service requester from the question acquisition request, and acquires the historical service record data of the service requester from the historical service record database stored in the server 110 according to the user information.

Optionally, the user information may include at least one of an incoming call number, a user account number, and a user biometric feature. For example, if the question acquisition request is an incoming call request generated by the service requester terminal 130 dialing a customer service telephone, the incoming call number of the target user may be acquired from the incoming call request. For another example, if the question acquisition request is a question acquisition request generated when some indication control in the application is triggered, the user account or the user biometric feature of the target user may be acquired from the question acquisition request. The user biometric features may be any recognizable biometric features such as a fingerprint feature, a face feature, and an iris feature, which is not limited in this embodiment.

Step S120, inputting historical service record data into a pre-trained collaborative filtering model to obtain confidence degrees of all candidate main-class problems, and selecting a target main-class problem from all candidate main-class problems according to the confidence degrees of all candidate main-class problems.

Before step S120, the collaborative filtering model may be trained by:

first, a main class training sample set is obtained. The main class training sample set can comprise main class problem service record data of each service requester and candidate main class problems associated with the main class problem service record data, wherein the problem service record data comprises a service requester portrait, service order data, a service provider portrait and service spatio-temporal information.

Still taking travel service as an example, the above-mentioned main candidate problems may include "cost problem", "time problem", "driver side problem", "passenger side problem", and "safety problem". The main-class problem service record data may include historical service record data of the passenger acquired when the passenger consults some candidate main-class problems, such as "cost problem" and "time problem", respectively, where the candidate main-class problems associated with the main-class problem service record data are "cost problem" and "time problem".

Wherein each candidate main-class question may be determined based on previously recorded questions consulted by each service requester terminal 130 during a single round of the session. For example, in one possible implementation, the total number of times of each question that the different service requester terminals 130 request consultation in a preset time period may be counted, and then the counted questions whose total number of times meets a preset condition may be taken as candidate main-class questions. For example, the questions whose counted total number exceeds the preset number may be regarded as candidate main-class questions, or the total number corresponding to each question may be arranged in descending order, and the questions whose total number is M, where M is a positive integer, may be arranged in the order of the largest number and the smallest number.

And then, training by adopting a collaborative filtering algorithm according to the training sample set to obtain a collaborative filtering model.

The candidate main problem has prominent characteristics, high prediction accuracy, definite problem, quick positioning problem and strong regularity, for example:

the cost problem is as follows: the cost is abnormal, and the actual price is obviously higher than the estimated price, etc.

The time problem is as follows: the time length is abnormal, and the time running time is larger than the average time of the route.

Driver side issues: bad service attitude, bad vehicle sanitation, etc.

Passenger side issues: lost articles, etc.

Safety problems are as follows: driver income, identity, age, complaints, etc.

Therefore, the confidence of each candidate main-class problem can be accurately predicted by inputting the historical service record data into the trained collaborative filtering model. And then, according to the confidence degrees of the candidate main problems, generating a sequencing result of the candidate main problems according to the descending order of the confidence degrees, and selecting at least one candidate main problem which is sequenced most at the front as a target main problem according to the sequencing result. Alternatively, a candidate main-class problem with a confidence level greater than a preset confidence level may be used as the target main-class problem.

For example, the ranking results generated for each candidate main class question in order of decreasing confidence may be: TOP1 (cost issue, confidence 9), TOP2 (time issue, confidence 8), TOP3 (driver side issue, confidence 7), TOP4 (passenger side issue, confidence 6), and TOP5 (security issue, confidence 5), the first candidate main category issue ranked, i.e., "cost issue", may be selected as the target main category issue. Alternatively, a candidate major problem with a confidence level greater than 9, i.e., a "cost problem," may be selected as the target major problem.

Step S130, inputting historical service record data into a deep neural network DNN model corresponding to the target main-class problem to obtain the confidence of each candidate sub-class problem under the target main-class problem.

Before step S130, the collaborative filtering model may be trained by:

first, a subclass training sample set for each candidate main class problem is obtained. The subclass training sample set can comprise subclass problem service record data of each service requester and candidate subclass problems associated with the subclass problem service record data, wherein the problem service record data comprises a service requester portrait, service order data, a service provider portrait and service spatio-temporal information.

Still taking the travel service as an example, the sub-category problem service record data under the "expense problem", "time problem", "driver side problem", "passenger side problem" and "safety problem" and the candidate sub-category problem associated with the sub-category problem service record data are acquired, respectively. For example, when a passenger consults the candidate subclass problems of 'detour multiple-generation cost', 'no-ride-in-the-car' and 'multiple-charge-in-the-extra' the acquired historical service record data of the passenger is the subclass training sample set of the 'cost problem', and the candidate subclass problems associated with the subclass problem service record data are 'detour multiple-generation cost', 'no-ride-in-the-car' and 'multiple-charge-in-the-extra'.

On the basis, according to each subclass training sample set, a DNN (Deep Neural Network) algorithm can be adopted to train and obtain a DNN model of each candidate main class problem.

The DNN model may include an input layer (inputlayer), a hidden layer (hiddenlayer), and an output layer (outputlayer), where: an input layer, i.e., the first layer of the DNN model, which may include a plurality of input nodes, for example, when the extracted feature vector includes 200-dimensional features, the number of input nodes 15 may be 200; the output layer, i.e. the last layer of the DNN model, includes output nodes whose number depends on the category of the candidate sub-class problems included in each candidate main class problem, e.g. when 10 candidate sub-class problems are included in the candidate main class problem, then the output layer may include 10 output nodes; the hidden layers are located between the input layer and the output layer, the hidden layers can be multi-layered, the more hidden layers are, the more nodes each hidden layer contains, and the stronger the expression capability of the DNN model is.

Wherein each candidate sub-category question may be determined based on previously recorded questions consulted by each service requester terminal 130 during a single round of the session. For example, in one possible implementation, the total number of times of each question that the different service requester terminal 130 requests consultation within a preset time period (for example, within the past three months) may be counted, and then the counted questions whose total number of times meets a preset condition may be regarded as candidate sub-class questions. For example, each question with a counted total number exceeding a preset number may be regarded as a candidate sub-category question, or the total number of times corresponding to each question may be arranged in descending order, and each question with the top M total numbers may be regarded as a candidate sub-category question, where M is a positive integer.

In this way, the confidence of each candidate subclass problem under the target main class problem can be obtained by inputting the historical service record data into the deep neural network DNN model corresponding to the target main class problem. For example, the historical service record data is input into the deep neural network DNN model corresponding to the "expense problem", so that the confidence levels of the candidate sub-class problems of "detour multi-generation expense", "multi-charging additional expense", "non-timely ending charging multi-generation expense", "getting expense", "non-vehicle riding multi-generation expense" under the "expense problem" can be obtained.

Step S140, sending a question recommendation menu to the service requester terminal 130 according to the confidence level of each candidate main-class question and the confidence level of each candidate sub-class question under the target main-class question, where the question recommendation menu includes the main-class recommendation question and the sub-class recommendation question under the target main-class question.

As a possible implementation manner, a first preset number of candidate main-class problems may be selected as the main-class recommendation problems according to the confidence degrees of the respective candidate main-class problems in descending order of the confidence degrees. And meanwhile, selecting a second preset number of candidate subclasses as subclass recommendation problems according to the confidence degrees of the candidate subclasses under the target main class problem and the descending order of the confidence degrees. Finally, a question recommendation menu is generated according to the main class recommendation question and the sub class recommendation question under the target main class question, and the question recommendation menu is sent to the service requester terminal 130.

For example, suppose that the ranking results of the candidate main-class questions generated in the order of descending confidence are: TOP1 (cost question, confidence 9), TOP2 (time question, confidence 8), TOP3 (driver side question, confidence 7), TOP4 (passenger side question, confidence 6) and TOP5 (security question, confidence 5), taking the "cost question" as the target main class question for example, the ranking results of the candidate sub-class questions generated in descending order of confidence are: TOP1 (cost due to detour, confidence 9), TOP2 (additional cost due to multiple charges, confidence 8), TOP3 (cost due to multiple charges due to untimely ending, confidence 7), TOP4 (consumption, confidence 6), TOP5 (cost due to multiple charges due to no vehicle sitting, confidence 5).

On the basis of the above, the candidate main-class problem of TOP2 can be selected as the main-class recommendation problem, i.e., "cost problem" and "time problem". Meanwhile, with the "cost problem" as the target main-class problem, the candidate sub-class problem of TOP3 under the "cost problem" may be selected as the sub-class recommendation problem, that is, "detour multi-generation cost", "multi-charge additional cost", and "non-timely end of charging multi-generation cost". Finally, the generated problem recommendation menu is 'detour multi-generation cost', 'extra additional cost', 'untimely charging and multi-generation cost' and 'time problem'.

As another possible implementation manner, according to the confidence level of each preset main-class problem, the preset main-class problem with the confidence level greater than the first confidence level may be used as the main-class recommendation problem. Meanwhile, according to the confidence of each preset subclass problem under the target main class problem, the preset subclass problem with the confidence higher than the second confidence is used as the subclass recommendation problem. Finally, a question recommendation menu is generated according to the main class recommendation question and the sub class recommendation question under the target main class question, and the question recommendation menu is sent to the service requester terminal 130.

Still taking the above example as an example, candidate major questions with a confidence level greater than 8 may be selected as major recommendation questions, i.e., "cost questions" and "time questions". Meanwhile, with the 'cost problem' as a target main class problem, a candidate sub-class problem with a confidence degree of more than 7 under the 'cost problem' can be selected as a sub-class recommendation problem, namely 'detour multi-generation cost', 'multi-charge additional cost' and 'non-timely ending charging multi-generation cost'. Finally, the generated problem recommendation menu is 'detour multi-generation cost', 'extra additional cost', 'untimely charging and multi-generation cost' and 'time problem'.

After the question recommendation menu is generated, the user may be prompted to select a question that meets the condition in a voice playing manner with respect to the incoming call request sent by the service requester terminal 130. For example, the voice data of the main category recommendation problem and the sub category recommendation problem under the target main category problem included in the problem recommendation menu may be transmitted to the service requester terminal 130, so that the service requester terminal 130 plays the voice data of the main category recommendation problem and the sub category recommendation problem under the target main category problem to the service requester. For example, the voice data may prompt the passenger to: the "detour more-generated charge" is pressed according to 1, the "more-received additional charge" is pressed according to 2, the "not-timely-ended charging more-generated charge" is pressed according to 3, and the "time problem" is pressed according to 4.

Or the target user can be prompted to select the qualified service recommendation item in a page display mode. For example, the main-class recommendation question included in the question recommendation menu and the sub-class recommendation question under the target main-class question may be sent to the service requester terminal 130, so that the service requester terminal 130 presents the main-class recommendation question and the sub-class recommendation question under the target main-class question to the user. For example, passengers are respectively presented with "detour and multi-generation charge", "multi-charge additional charge", "non-timely charging and multi-generation charge", and "time problem".

Based on the design, in the embodiment, the main-class recommendation problem is predicted, then the target main-class problem is selected, the historical service record data is continuously input into the deep neural network DNN model corresponding to the target main-class problem, the specific sub-class recommendation problem under the target main-class problem is predicted, and the finally output problem recommendation menu can simultaneously include the main-class recommendation problem and the specific sub-class recommendation problem. Therefore, by using the characteristics of outstanding problem characteristics and high prediction accuracy of the main recommendation problem, the main recommendation problem is predicted first, and then the subclass recommendation problem is predicted, so that the prediction accuracy can be effectively improved.

Further, the user may select a qualified target recommendation problem from the main recommendation problems and the sub recommendation problems under the target main recommendation problem according to actual requirements, and send the selected target recommendation problem to the server 110 through the service requester terminal 130. For example, if the passenger encounters a problem of "detour multi-occurrence cost", the "detour multi-occurrence cost" may be selected as the target recommendation problem. As another example, if the passenger encounters a "time issue," the "time issue" may be selected as the target recommendation issue.

Then, after obtaining the target recommendation problem, the server 110 determines whether the target recommendation problem is a major recommendation problem. If the target recommendation problem is a main recommendation problem, inputting historical service record data into a DNN model corresponding to the main recommendation problem to obtain confidence levels of all preset sub-problems under the main recommendation problem, and sending the sub-recommendation problems under the main recommendation problem to the service requester terminal 130 according to the confidence levels of all the preset sub-problems. And if the target recommendation question is a subclass recommendation question, processing the subclass recommendation question according to the question processing strategy of the subclass recommendation question.

For example, if the target recommendation question is a main-class recommendation question of "time question", the history service record data is input to the DNN model corresponding to "time question", the confidence of each preset sub-class question under "time question" is obtained, and the sub-class recommendation question under "time question" is sent to the service requester terminal 130 according to the confidence of each preset sub-class question.

For example, if the target recommendation question is a sub-category recommendation question of "detour occurrence rate", the "detour occurrence rate" is processed according to a question processing policy of "detour occurrence rate". For example, a passenger in the service requester may be prompted to enter a refund process by voice or online interaction.

Therefore, the problems of the user in the service using process can be quickly positioned, and the user experience is improved.

Fig. 4 is a functional block diagram of an issue recommending apparatus 300 according to some embodiments of the present application, where the functions implemented by the issue recommending apparatus 300 may correspond to the steps executed by the method described above. The problem recommending apparatus 300 may be understood as the server 110 or a processor of the server 110, or may be understood as a component that is independent from the server 110 or the processor and implements the functions of the present application under the control of the server 110, as shown in fig. 4, the problem recommending apparatus 300 may include an obtaining module 310, a first input module 320, a second input module 330, and a menu sending module 340, and the functions of the function modules of the problem recommending apparatus 300 are described in detail below.

The obtaining module 310 may be configured to obtain historical service record data of the service requester according to the question obtaining request sent by the service requester terminal 130, where the historical service record data includes one or more combinations of service requester profile characteristics, service order data, service provider profile characteristics, and service spatio-temporal information. It is understood that the obtaining module 310 may be configured to perform the step S110, and for a detailed implementation of the obtaining module 310, reference may be made to the content related to the step S110.

The first input module 320 may be configured to input historical service record data into a pre-trained collaborative filtering model, obtain confidence levels of the candidate major problems, and select a target major problem from the candidate major problems according to the confidence levels of the candidate major problems. It is understood that the first input module 320 can be used to perform the step S120, and for the detailed implementation of the first input module 320, reference can be made to the above description of the step S120.

The second input module 330 may be configured to input historical service record data into a deep neural network DNN model corresponding to the target main-class problem, so as to obtain a confidence of each candidate sub-class problem under the target main-class problem. It is understood that the second input module 330 can be used to perform the step S130, and for the detailed implementation of the second input module 330, reference can be made to the above description regarding the step S130.

The menu sending module 340 may be configured to send a question recommendation menu to the service requester terminal 130 according to the confidence level of each candidate main-class question and the confidence level of each candidate sub-class question under the target main-class question, where the question recommendation menu includes the main-class recommendation question and the sub-class recommendation question under the target main-class question. It is understood that the menu sending module 340 can be used to execute the above step S140, and for the detailed implementation of the menu sending module 340, reference can be made to the above contents related to step S140.

In a possible implementation manner, the obtaining module 310 may specifically obtain the historical service record data of the service requester through the following manners:

acquiring user information of the service requester from the question acquisition request, wherein the user information comprises at least one of an incoming call number, a user account and user biological characteristics;

and acquiring historical service record data of the service requester from a historical service record database stored in the server 110 according to the user information.

In one possible implementation, the service requester profile feature at least includes basic information of the service requester, service usage frequency, service type distribution and complaint problem distribution, the service provider profile feature at least includes basic information of the service provider, average service statistics data and complaint problem distribution, the service order data includes order statistics data of the latest preset number of service orders, and the service spatio-temporal information includes time information, location information and thermodynamic diagram information when the problem acquisition request is sent, wherein the basic information of the service requester includes age, gender, service level and occupation, the basic information of the service provider includes age, gender and service level, and the average service statistics data includes average service duration per day and average income per day.

In a possible implementation manner, please further refer to fig. 5, the problem recommendation apparatus may further include a first training module 301 for pre-training the collaborative filtering model, where the first training module 301 may pre-train the collaborative filtering model specifically by:

and training by adopting a collaborative filtering algorithm according to the training sample set to obtain a collaborative filtering model.

In a possible implementation manner, please further refer to fig. 6, the problem recommendation apparatus may further include a second training module 302 for pre-training the DNN model corresponding to each candidate main-class problem, where the second training module 302 may specifically pre-train the DNN model corresponding to each candidate main-class problem by:

acquiring a subclass training sample set of each candidate main class problem, wherein the subclass training sample set comprises subclass problem service record data of each service requester and candidate subclasses problems associated with the subclass problem service record data, and the problem service record data comprises service requester portraits, service order data, service provider portraits and service spatio-temporal information;

In one possible implementation, the first input module 320 may specifically select the target major problem from the candidate major problems by:

generating a sequencing result of each candidate main-class problem according to the confidence coefficient of each candidate main-class problem and the descending order of the confidence coefficient;

selecting at least one candidate main class problem which is ranked most front as a target main class problem according to the ranking result; or

And taking the candidate main class problem with the confidence coefficient larger than the preset confidence coefficient as a target main class problem.

In a possible implementation manner, the menu sending module 340 may specifically send the question recommendation menu to the service requester terminal 130 by:

selecting a first preset number of candidate main-class problems as main-class recommendation problems according to the confidence degrees of the candidate main-class problems and the descending order of the confidence degrees;

selecting a second preset number of candidate subclass problems as subclass recommendation problems according to the confidence degrees of the candidate subclass problems under the target main class problem and the descending order of the confidence degrees;

and generating a question recommendation menu according to the main class recommendation question and the sub class recommendation question under the target main class question, and sending the question recommendation menu to the service requester terminal 130.

the voice data of the main-class recommendation problem and the sub-class recommendation problem under the target main-class problem included in the problem recommendation menu is sent to the service requester terminal 130, so that the service requester terminal 130 plays the voice data of the main-class recommendation problem and the sub-class recommendation problem under the target main-class problem to the service requester.

In a possible implementation manner, the menu sending module 340 may specifically send the question recommendation menu by:

the main-class recommendation problem and the sub-class recommendation problem under the target main-class problem included in the problem recommendation menu are sent to the service requester terminal 130, so that the service requester terminal 130 displays the main-class recommendation problem and the sub-class recommendation problem under the target main-class problem to the user.

In a possible implementation, the menu sending module 340 is further configured to:

acquiring a target recommendation problem selected by the service requester terminal 130 from the main class recommendation problem and the sub class recommendation problem under the target main class problem;

if the target recommendation question is a main recommendation question, inputting historical service record data into a DNN model corresponding to the main recommendation question to obtain confidence of each candidate subclass question under the main recommendation question;

sending the subclass recommendation problem under the main class recommendation problem to the service requester terminal 130 according to the confidence of each candidate subclass problem;

and if the target recommendation question is a subclass recommendation question, processing the subclass recommendation question according to the question processing strategy of the subclass recommendation question.

The modules may be connected or in communication with each other via a wired or wireless connection. The wired connection may include a metal cable, an optical cable, a hybrid cable, etc., or any combination thereof. The wireless connection may comprise a connection over a LAN, WAN, bluetooth, ZigBee, NFC, or the like, or any combination thereof. Two or more modules may be combined into a single module, and any one module may be divided into two or more units.

It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the apparatus described above may refer to corresponding processes in the method embodiments, and are not described in detail in this application. In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and there may be other divisions in actual implementation, and for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or modules through some communication interfaces, and may be in an electrical, mechanical or other form.

The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A question recommendation method is applied to a server and comprises the following steps:

2. The question recommendation method according to claim 1, wherein said step of obtaining historical service record data of the service requester according to the question obtaining request sent by the service requester terminal comprises:

3. The question recommendation method according to claim 1, characterized in that said service requester profile characteristics comprise at least basic information of the service requester, service usage frequency, service type distribution and complaint question distribution, the service provider portrait characteristics at least comprise basic information of the service provider, average service statistical data and complained question distribution, the service order data includes order statistics of a most recent preset number of service orders, the service spatio-temporal information includes time information, location information, and thermodynamic diagram information when the problem acquisition request is transmitted, wherein the basic information of the service requester includes age, gender, service level and occupation, the basic information of the service provider includes age, gender and service level, and the average service statistics includes average service duration per day and average income per day.

4. The question recommendation method according to claim 1, characterized in that said method further comprises a step of pre-training a collaborative filtering model, specifically comprising:

5. The question recommendation method according to claim 1, characterized in that said method further comprises a step of training a DNN model corresponding to each of said candidate main class questions, specifically comprising:

6. The question recommendation method of claim 1, wherein said step of selecting a target major question from said respective candidate major questions based on a confidence of said respective candidate major question comprises:

7. The question recommendation method according to any one of claims 1 to 6, wherein said step of sending a question recommendation menu to said service requester terminal according to the confidence level of each candidate main-class question and the confidence level of each candidate sub-class question under said target main-class question comprises:

8. The question recommendation method according to any one of claims 1 to 6, wherein said step of sending a question recommendation menu to said service requester terminal according to the confidence level of each candidate main-class question and the confidence level of each candidate sub-class question under said target main-class question comprises:

9. The question recommendation method according to claim 1, wherein said step of sending a question recommendation menu to said service requester terminal comprises:

10. The question recommendation method according to claim 1, wherein said step of sending a question recommendation menu to said service requester terminal comprises:

11. The question recommendation method according to claim 1, wherein after the step of sending a question recommendation menu to the service requester terminal according to the confidence level of each candidate main-class question and the confidence level of each candidate sub-class question under the target main-class question, the method further comprises:

12. An issue recommendation apparatus, applied to a server, the apparatus comprising:

the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring historical service record data of a service requester according to a problem acquisition request sent by a service requester terminal, and the historical service record data comprises one or more combinations of service requester portrait characteristics, service order data, service provider portrait characteristics and service spatio-temporal information;

the first input module is used for inputting the historical service record data into a pre-trained collaborative filtering model to obtain the confidence coefficient of each candidate main-class problem, and selecting a target main-class problem from each candidate main-class problem according to the confidence coefficient of each candidate main-class problem;

the second input module is used for inputting the historical service record data into a deep neural network DNN model corresponding to the target main class problem to obtain the confidence of each candidate subclass problem under the target main class problem;

and the menu sending module is used for sending a question recommendation menu to the service requester terminal according to the confidence degrees of the candidate main-class problems and the confidence degrees of the candidate sub-class problems under the target main-class problem, wherein the question recommendation menu comprises main-class recommendation problems and sub-class recommendation problems under the target main-class problem.

13. The question recommendation device according to claim 12, wherein the obtaining module obtains the historical service record data of the service requester by:

14. The question recommendation device of claim 12, wherein the service requester profile characteristics comprise at least basic information of the service requester, service usage frequency, service type distribution and complaint question distribution, the service provider portrait characteristics at least comprise basic information of the service provider, average service statistical data and complained question distribution, the service order data includes order statistics of a most recent preset number of service orders, the service spatio-temporal information includes time information, location information, and thermodynamic diagram information when the problem acquisition request is transmitted, wherein the basic information of the service requester includes age, gender, service level and occupation, the basic information of the service provider includes age, gender and service level, and the average service statistics includes average service duration per day and average income per day.

15. The question recommendation device according to claim 12, characterized in that it further comprises a first training module for pre-training a collaborative filtering model;

the first training module trains the collaborative filtering model in advance specifically by the following method:

16. The question recommendation device of claim 12, characterized in that the device further comprises a second training module for pre-training a DNN model corresponding to each of the candidate main class questions;

the second training module is specifically configured to pre-train the DNN model corresponding to each candidate main-class problem in the following manner:

17. The question recommendation device of claim 12, wherein the first input module selects a target major question from the candidate major questions by:

18. The question recommendation device according to any one of claims 12-17, wherein the menu sending module sends the question recommendation menu to the service requester terminal by:

19. The question recommendation device according to any one of claims 12-17, wherein the menu sending module sends the question recommendation menu to the service requester terminal by:

20. The question recommendation device according to claim 12, wherein the menu sending module sends the question recommendation menu to the service requester terminal by:

21. The question recommendation device according to claim 12, wherein the menu sending module sends the question recommendation menu by:

22. The question recommendation device of claim 12, wherein the menu sending module is further configured to:

23. A server, comprising: a processor, a storage medium and a bus, the storage medium storing machine-readable instructions executable by the processor, the processor and the storage medium communicating via the bus when the server is running, the processor executing the machine-readable instructions to perform the steps of the problem recommendation method of any one of claims 1-11 when executed.

24. A readable storage medium, having stored thereon a computer program which, when being executed by a processor, is adapted to carry out the steps of the question recommendation method according to any one of claims 1-11.