WO2020010954A1

WO2020010954A1 - Offline immediate demand processing method, information recommendation method and apparatus, and device

Info

Publication number: WO2020010954A1
Application number: PCT/CN2019/089064
Authority: WO
Inventors: 陈力; 杨磊; 官砚楚; 曾晓东; 周乐
Original assignee: 阿里巴巴集团控股有限公司
Priority date: 2018-07-09
Filing date: 2019-05-29
Publication date: 2020-01-16
Also published as: CN109063002A; TW202006640A

Abstract

The embodiments of the present description provide an offline immediate demand processing method, an information recommendation method and apparatus, and a device. In the embodiments of the present description, semantic analysis is performed on demand content of a demand request sent by a demand proposer, to obtain the intention of the demand proposer. Primary screening is performed on demand responders at least according to the intention and location information concerning the demand proposer, to obtain at least one potential demand responder, and the demand of the demand proposer is pushed to the potential demand responders on the basis of the demand content. On the basis of response information fed back by the potential demand responders, a target demand responder satisfying the demand of the demand proposer is selected, by means of screening, from the obtained potential demand responders, and related information about the target demand responder is recommended to the demand proposer.

Description

Offline instant demand processing method, information recommendation method, device and equipment

Technical field

This specification relates to the field of data processing technology, and in particular, relates to offline instant demand processing methods, information recommendation methods, devices, and equipment.

Background technique

In an offline environment, when users have certain specific needs, they often search for nearby merchants through the network to guess the merchants that may meet the specific needs, and to investigate whether the merchant can provide goods or services corresponding to the specific needs. It can be seen that users may go to multiple merchants to find the corresponding goods or services, which is inefficient.

Summary of the invention

In order to overcome the problems in the related technologies, this specification provides offline instant demand processing methods, information recommendation methods, devices, and equipment.

According to a first aspect of the embodiments of the present specification, an information recommendation method is provided. The method includes:

Perform semantic analysis on the content of the demand in the demand request sent by the demander to obtain the intention of the demander, the demand request carrying at least the content of the demand and the position of the demander;

Preliminary screening of the demand responder based on at least the intent and the position information of the demand side, obtaining at least one potential demand responder, and pushing the demand demander's demand to the potential demand responder based on the content of the demand;

Based on the response information fed back by the potential demand responder, a target demand responder that meets the requirements of the demand proposer is selected from the potential demand responders obtained by the screening, and the relevant information of the target demand responder is recommended to the demand proposer.

Optionally, both the demand responder and the demand content are classified according to a preset category, and the intent is a preset category corresponding to the demand content.

Optionally, the method further includes:

Extracting feature data that can characterize the preset category to which the demand responder belongs from the information of the demand responder of the known preset category;

Build a category prediction model based on the preset category and feature data of the demand responder with a known preset category;

A category prediction model is used to predict the preset category to which the demand responder of the unknown preset category belongs.

Optionally, the characteristic data includes at least one or more of a collection frequency within a preset time period, a distribution of a payment amount within a preset time period, a distribution of the payment time within a preset time period, and a geographic location. feature.

Optionally, the requirements of the demander include at least audio data obtained based on the content of the demand, and the audio data is played in real time on the potential demand responder.

Optionally, the related information of the target demand responder includes one or more of the following information:

The identification information of the target demand responder, the response content of the target demand responder feedback, the distance information between the target demand responder and the demand proposer, and navigation guidance data where the position of the demand proposer reaches the position of the target demand responder.

Optionally, recommending the target demand responder to the demand proposer includes:

The relevant information of the target demand responder is marked on the map at a position corresponding to the position information of the target demand responder, and the marked map is sent to the demand proposer.

According to a second aspect of the embodiments of the present specification, an offline instant demand processing method is provided. The method includes:

The user sends to the server an offline demand request that carries at least the content of the demand and the location information of the demander;

The server performs semantic analysis on the demand content to obtain the intention of the client, at least preliminary screening of merchants according to the intent and the position information of the demand side, to obtain at least one potential merchant, and to the terminal of the potential merchant based on the demand content Push audio data used to represent the needs of the client;

The terminal of the potential merchant plays the audio data;

Based on the response information fed back by the terminal of the potential merchant after playing the audio data, the server screens out the target merchants that meet the needs of the client from the potential merchants obtained by the screening, and recommends the relevant information of the target merchant to the client.

According to a third aspect of the embodiments of the present specification, an information recommendation device is provided, where the device includes:

An intent recognition module, configured to: perform a semantic analysis on the content of a demand in a demand request sent by a demander to obtain the intention of the demander, the demand request carrying at least the content of the demand and the position of the demander;

A preliminary screening module, configured to: perform preliminary screening on demand responders according to at least the intent and the position information of the demand parties to obtain at least one potential demand responder;

An information transmission module, configured to: push a demander's demand to a potential demand responder based on the demand content;

The target screening module is configured to: based on the response information fed back by the potential demand responders, select a target demand responder that meets the demands of the demand proposers from the potential demand responders obtained by the screening;

The information transmission module is further configured to recommend relevant information of the target demand responder to a demand proposer.

Optionally, the device further includes:

The model training module is configured to extract feature data that can represent the preset category to which the demand responder belongs from the information of the known responder of the preset category; according to the preset category and Feature data to construct a category prediction model;

A category prediction module is used to predict a preset category to which a demand responder of an unknown preset category belongs through a category prediction model.

Optionally, the device further includes:

An information marking module, configured to mark the relevant information of the target demand responder on a map corresponding to the position information of the target demand responder;

The information transmission module is configured to send a marked map to a demander.

According to a fourth aspect of the embodiments of the present specification, there is provided a computer device including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the program as follows method:

The embodiment of this specification obtains the intention of the demander by performing a semantic analysis on the content of the demand in the demand request sent by the demander. Since the demand request also carries the positional information of the demander, at least according to the intention and the positional information of the demander Preliminary screening of the demand responders, obtaining at least one potential demand responder, and pushing the demand proposer's demand to the potential demand responders based on the content of the demand. Based on the response information fed back by the potential demand responders, the target demand responders that meet the needs of the demand proposers are selected from the potential demand responders obtained by the screening, and the relevant information of the target demand responders is recommended to the demand proposers, so as to achieve Provide an interaction channel between demand proposers and demand responders to solve the problem that the two parties cannot effectively match and communicate, so that users can quickly find the demand responders, thereby improving the efficiency of users in obtaining services or goods.

It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and should not limit the present specification.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings herein are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the specification, and together with the description serve to explain the principles of the specification.

Fig. 1 is a schematic diagram of an information recommendation system architecture according to an exemplary embodiment of the present specification.

Fig. 2 is a flowchart illustrating an information recommendation method according to an exemplary embodiment of the present specification.

Fig. 3A is a flowchart illustrating a method for processing offline instant demand according to an exemplary embodiment of the present specification.

Fig. 3B is a system frame diagram of offline instant demand processing according to an exemplary embodiment of the present specification.

Fig. 4 is a hardware structural diagram of a computer device in which an information recommendation apparatus is located according to an exemplary embodiment of the present specification.

Fig. 5 is a block diagram of an information recommendation device according to an exemplary embodiment of the present specification.

detailed description

Exemplary embodiments will be described in detail here, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numerals in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with this specification. Rather, they are merely examples of devices and methods consistent with some aspects of the specification, as detailed in the appended claims.

The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the specification. As used in this specification and the appended claims, the singular forms "a," "the," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

It should be understood that although the terms first, second, third, etc. may be used in this specification to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this specification, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein can be interpreted as "at" or "when" or "in response to determination".

In practical applications, when a user puts forward a certain demand, it is often difficult to determine which demand responders can respond to the demand. It is often necessary to conduct an on-site inspection to confirm whether the demand responder can provide the service or goods corresponding to the demand. It can be seen that users obtain services Or the efficiency of the goods is low.

In view of this, the embodiments of the present specification provide an interaction channel between the requester and the responder to solve the problem that the two parties cannot effectively match and communicate, thereby improving the efficiency of users in obtaining services or goods.

The technical solutions in the embodiments of the present specification will be described below by way of example with reference to the drawings in the embodiments of the present specification.

As shown in FIG. 1, it is a schematic diagram of an information recommendation system architecture according to an exemplary embodiment of the present specification. In this schematic diagram, a demand requesting end, a server end, and a demand response end may be included. The requirement presenting end may be an end presenting a requirement, for example, it may be a user end. The demand presenting end may be an application program that can provide a demand presenting service, such as an application such as Alipay. The demand-providing end may also be an electronic device with a demand-providing function. The electronic device can be a mobile phone or other handheld portable device, or a slightly smaller portable device such as a wristwatch device, a pendant device, or a miniaturized device, tablet computer, notebook computer, desktop computer, integrated in a computer display Computer or other electronic equipment. The server can be a collective name for multiple server devices, or it can be a collective name for software installed on the server device. The demand response end may be an end capable of responding to a corresponding demand, for example, it may be a merchant end. The demand response end may be an application program capable of providing a demand response service, or an electronic device having a demand response function.

Next, from the perspective of the server, the embodiments of this specification will be described by way of example. As shown in FIG. 2, it is a flowchart of an information recommendation method according to an exemplary embodiment of the present specification. The method includes:

In step 202, semantic analysis is performed on the content of the requirements in the demand request sent by the demander to obtain the intention of the demander, the demand request carrying at least the content of the demand and the position of the demander;

In step 204, preliminary screening is performed on the demand responders according to at least the intent and the position information of the demand parties, to obtain at least one potential demand responder, and based on the content of the demand, the demand proposer's requirements are pushed to the potential demand responders. ;

In step 206, based on the response information fed back by the potential demand responder, a target demand responder that meets the demand proposer's needs is selected from the potential demand responders obtained by the screening, and the target demand responder is recommended to the demand proposer. Related Information.

Among them, the requester can be a user or a client. The demand responder can be a merchant or a merchant. Requirement content is used to describe the requirements of the requester. It can be voice content or text content issued by the requester. The demand-side location information may be the geographic position of the demand-side, or information for determining the geographic position of the demand-side, such as WiFi information.

When performing semantic analysis on the demand content sent by the demand proposer, if the demand content is voice content, the demand content may be firstly speech-recognized to be parsed into text content, and the text content is subjected to semantic analysis. Semantic analysis of the content of the request is to identify the intention of the requester. In one embodiment, the preset category corresponding to the demand content may be used as the intention of the demand proposer. For example, a preset category can be constructed in advance, and a preset category can also be called a category system or a preset type. Demand responders and demand content are classified according to preset categories. The preset category can be obtained by dividing based on the type of the merchant, or can be obtained by dividing based on the user's intention. For example, the preset categories may include: department stores, foods, and fruits.

In this embodiment, the preset category can be used as the intention of the demander, that is, the purpose of performing semantic analysis on the demand content is to obtain the preset category corresponding to the demand content, so that the intention of the demander can be quickly identified. Associate the demand content with the demand responder through the preset category, and the demand responder can be preliminarily filtered according to the preset category corresponding to the demand content.

There are many methods for semantic analysis. The following two examples are used for illustration.

In one example, keyword matching can be used to obtain the intention of the demander. For example, performing semantic analysis on the content of the requirements in the demand request sent by the demand proposer to obtain the intention of the demand proposer may include:

Extracting keywords matching the keywords in a preset correspondence relationship from the required content, the preset correspondence relationship being a correspondence relationship between a pre-built keyword and a preset category;

According to the preset correspondence, the preset category corresponding to the matched keywords is used as the intention of the demander.

In this embodiment, a correspondence relationship between keywords and a preset category may be constructed in advance. The corresponding relationship can be obtained based on big data analysis. For example, it may be obtained according to a correspondence between a search term and a preset category to which a final product belongs in a history of a user searching for a product on an e-commerce platform. In this embodiment, a keyword matching method is used, and the preset category corresponding to the keywords matched in the requirement content is used as the intention of the demand proposer, so that the intention of the demand proposer can be obtained quickly.

In another embodiment, the requirement content may be input into a pre-built intent recognition model to obtain a preset category corresponding to the requirement content. The intent recognition model may be a pre-built model for identifying a user's intent. For example, a training sample can be constructed using demand content of a known preset category, and a deep learning algorithm can be trained using the training sample to obtain an intent recognition model. This embodiment uses the intent recognition model to predict the preset categories corresponding to the demand content, so as to recognize more preset categories of demand content.

Demand responders are classified according to preset categories. In actual applications, only some of the demand responders can obtain their preset categories, while some demand responders do not know their preset categories. For example, a preset category to which some demand responders belong can be obtained based on a preset category uploaded by the demand responder, or can be obtained based on attribute information of the demand responder in an e-commerce platform. For other demand responders who do not know which preset category they belong to, they can use demand responders who already know the preset category to predict the category that the demand responder whose unknown preset category belongs to. For example, in one embodiment, the method further includes:

Among them, the information of the demand responder may include information related to the demand responder such as historical payment records and static attribute information. The characteristic data may be characteristic data capable of characterizing a preset category to which the demand responder belongs. In one example, the characteristic data includes at least one or more of a collection frequency within a preset time period, a distribution of a payment amount within a preset time period, a distribution of the payment time within a preset time period, and a geographic location. Kind of characteristics. It can be seen that the preset categories to which the demand responder belongs can be reflected by the information such as the frequency of collection, the amount of the receipt, the time of receipt, and the location, etc., so as to accurately predict the preset categories to which other demand responders belong. It should be understood that the characteristic data may also include other characteristic data, as long as it can represent a preset category to which the demand responder belongs, which is not described in detail here.

According to the preset category and the characteristic data of the demand responder of the known preset category, a category prediction model can be constructed. For example, according to a preset category and feature data of a demand responder of a known preset category, the supervising algorithm is trained to obtain a category prediction model for predicting the preset category. Supervised algorithms can be linear algorithms, logistic regression, random forest, etc.

In this embodiment, more preset categories to which the responders belong can be obtained through the above method, thereby expanding the recommendable responders.

Further, in order to improve security, the demand responder referred to in the embodiments of the present specification may be a responder that satisfies a reliability condition. For example, the responders are filtered based on historical information such as historical payment records to ensure that the demand responders obtained are reliable responders. In particular, demand responders are reliable merchants.

In the application phase, after obtaining the intention of the demand proposer, a preliminary screening can be performed on the demand responders according to preset screening conditions to obtain at least one potential demand responder. The screening factors of the preset screening conditions include at least the intention. In order to recommend information associated with the position of the demander, the filtering factor of the preset screening condition further includes the position of the demander. The preliminary screening of demand responders by intent can be to screen out demand responders that are the same as the preset category to which the intent belongs. The preliminary screening of demand responders using the position information of the demand side can be to screen out demand responders that are related in distance to the demand side. For example, filtering out demand responders within a preset range from the demand proposer, or selecting demand responders belonging to the same area as the demand proposer. It can be understood that other filtering conditions can also be set to screen potential demand responders suitable for recommending to the demand proposer. For example, the preset screening condition may further include: the potential demand responder is a demand responder whose terminal has an audio playing function, so that the terminal of the potential demand responder immediately plays the demand of the demand proposer. For example, the terminal of the potential demand responder may be an Alipay box or other similar type of device with a speaker function.

In this embodiment, in order to distinguish different demand responders, the demand responders obtained through preliminary screening are referred to as potential demand responders. Through preliminary screening, the number of notifications of demand responders by the server can be reduced, and at the same time, interruptions to unrelated demand responders can be reduced.

After the potential demand responder is obtained, the demand proposer's demand can be pushed to the potential demand responder based on the content of the demand. In one example, the demand content can be pushed directly to the demand responder, so as to establish an interaction channel between the demand proposer and the demand responder, so that the two parties can communicate. In another example, in order to increase the reminder of the demander ’s needs, the demander ’s needs pushed to the demander include at least the audio data obtained based on the content of the demand, so that the potential demander can play the audio data in real time. Facilitate immediate demand response. Specifically, if the demand content is voice data, the voice data is directly pushed to potential demand responders; if the demand content is text data, the demand content is generated by speech synthesis to generate audio data, and the audio data is pushed to each potential demand. Responder.

It can be seen that by playing the demand responder's demand in real time on the potential demand responder in real time, the potential responder can be reminded in time to facilitate the potential demand responder to respond in time to achieve the immediate demand performance of the demand proposer Be timely feedback.

After the potential demand responder's equipment reminds the demander of the demand, if the merchant meets the demand, the potential demand responder can respond to the demand. There are many types of responses, for example, whether physical keys or key combinations can be used to feedback whether the requirements can be met by the demander; or whether the content of the response through text or voice can satisfy the requirements of the demander.

After the server receives the response information from the potential demand responder, it can filter out the target demand responder that meets the needs of the demand proposer from the potential demand responders and recommend the relevant information of the target demand responder to the demand proposer. .

Regarding the screening of target demand responders based on response information, in one embodiment, a demand responder that can meet the demand requester's requirements will initiate response information. Therefore, whether the potential demand responder can be determined according to whether the response information is received Meet the needs of the demander. Specifically, a potential demand responder that initiates response information may be used as a target demand responder. If no response information is received from the potential demand responder, it can be assumed that the potential demand responder does not meet the demand proposer's requirements. In another embodiment, the response information may include response content, such as voice content or text content, provided by a potential demand responder. On the one hand, it is possible to analyze whether the potential demand responder can meet the demand proposer's needs based on the content of the response, so that the potential demand responder that meets the needs is the target demand responder. On the other hand, it can be assumed that the potential demand responder who initiated the response information can meet the needs of the demand proposer. Therefore, the potential demand responder who initiated the response information is directly regarded as the target demand responder, and the response content is regarded as the target demand responder. One of the relevant information is pushed to the requester.

Recommending the relevant information of the target demand responder to the demand proposer may be for the users of the demand proposer to understand the target demand responder. In view of this, in one example, the related information of the target demand responder includes one or more of the following information:

The identification information of the target demand responder, the response content of the target demand responder's feedback, the distance information between the target demand responder and the demand proposer, and navigation guidance data where the position of the demand proposer reaches the position of the target demand responder.

The identification information of the target demand responder may be an identifier that uniquely identifies the merchant, such as a merchant name. The location information of the target demand responder can be determined according to the GPS information, WiFi information, etc. in the response, or obtained by searching for the location information registered by the target demand responder based on the identification information.

It can be seen that by displaying the relevant information of the target demand responder, the user can be prevented from querying other information again and the user operation steps can be reduced. In particular, when the relevant information includes the response content of the target demand responder feedback, communication between the demand proposer and the demand responder can be achieved.

Further, recommending the target demand responder to the demand proposer includes: marking the relevant information of the target demand responder on a map corresponding to the position information of the target demand responder, and sending the information to the demand proposer. Map marked.

It can be seen that by sending the marked map to the requester, the relevant information of the target demand responder can be displayed on the demander using the map. If the feedback from the merchant is voice data, the controls that trigger voice playback can also be displayed at the corresponding position on the map to realize the interaction between the demander and the responder.

The various technical features in the above embodiments can be arbitrarily combined, as long as there is no conflict or contradiction in the combination of features, but it is limited in space and has not been described one by one, so the various technical features in the above embodiments can be arbitrarily combined It also belongs to the scope disclosed by this specification.

The following describes the embodiments of this specification by taking a specific application scenario as an example.

As shown in FIG. 3A, it is a flowchart of an offline instant demand processing method according to an exemplary embodiment of the present specification. The method includes:

The client sends an offline demand request that carries at least the content of the demand and the location information of the demander to the server (step 302). The offline demand request may refer to a request that is an offline demand and needs to be processed immediately.

The server performs semantic analysis on the content of the demand to obtain the intention of the user. At least a preliminary screening of merchants is performed based on the intent and the position of the demander to obtain at least one potential merchant (step 304), and based on the content of the demand, The terminal of the potential merchant pushes audio data used to represent the needs of the user terminal (step 306);

The terminal of the potential merchant plays the audio data (step 308);

Based on the response information fed back by the potential merchant's terminal after playing the audio data, the server screens out target merchants that meet the needs of the client from the potential merchants obtained through the screening (step 310), and recommends the relevant information of the target merchant to the client. Information (step 312). The client can display information about the target merchant.

Among them, FIG. 3A is the same as the related technology in FIG. 2, and details are not described herein.

As shown in FIG. 3B, it is a system frame diagram of offline instant demand processing according to an exemplary embodiment of the present specification. In this diagram, the user sends the required content in text / voice mode at the user terminal. The server receives the demand request carrying the demand content. If the demand content is in the form of speech, it is converted into text through speech recognition, and the user's intention is identified. According to the user's intention and the preset category to which the merchant belongs, the merchant is screened to obtain potential merchants. Send user needs to potential merchants. If the user sends text content, the text content is generated by speech synthesis. The merchant's offline device receives the request and plays it. The merchant can respond to the request according to its own situation. The server receives the response information from the merchant, organizes the feedback information and sends it to the user. If it is a response in the form of a voice, it can also be determined whether the voice data is fed back. After receiving the feedback information, the client can view the feedback information and purchase products or services offline.

It can be seen from the foregoing embodiments that the embodiments of the present specification propose a mobile function for instant transmission requirements. On the server side, the user's intention is accurately obtained through speech recognition and intention analysis. An algorithm for matching the immediate needs with the surrounding merchants is also proposed. The algorithm can accurately find nearby potential merchants that can meet the immediate needs. Especially for long-tailed small merchants who do not have effective means to solicit and operate nearby customers, build an even-to-response channel from client to merchant to solve the problem that the two parties cannot effectively match and communicate.

Corresponding to the foregoing embodiments of the information recommendation method, this specification also provides embodiments of the information recommendation device and the electronic equipment to which it is applied.

The embodiment of the information recommendation device in this specification may be applied to a computer device, and the computer device may be a server device. The device embodiments can be implemented by software, or by hardware or a combination of software and hardware. Taking software implementation as an example, as a device in a logical sense, it is formed by reading the corresponding computer program instructions in the non-volatile memory into the memory and running it through the processor of the computer equipment in which it is located. In terms of hardware, as shown in FIG. 4, it is a hardware structure diagram of the computer equipment where the information recommendation device in this specification is located, in addition to the processor 410, the network interface 420, the memory 430, and the nonvolatile memory shown in FIG. 4. In addition to the memory 440, the computer device in which the information recommendation device 431 is located in the embodiment may generally include other hardware according to the actual function of the device, and details are not described herein again.

As shown in FIG. 5, it is a block diagram of an information recommendation device according to an exemplary embodiment of the present specification. The device includes:

An intent recognition module 52 is configured to: perform a semantic analysis on the content of a demand in a demand request sent by a demander to obtain the intention of the demander, the demand request carrying at least the content of the demand and the position of the demander;

A preliminary screening module 54 is configured to perform preliminary screening on demand responders according to at least the intent and the position information of the demand parties to obtain at least one potential demand responder;

An information transmission module 56 is configured to: push a demand requester's demand to a potential demand responder based on the demand content;

The target screening module 58 is configured to: based on the response information fed back by the potential demand responders, select a target demand responder that satisfies the requirements of the demand proposers from the potential demand responders obtained by the screening;

The information transmission module 56 is further configured to recommend relevant information of the target demand responder to the demand proposer.

Optionally, the device further includes (not shown in FIG. 5):

The information transmission module 56 is configured to send the marked map to the demander.

As for the device embodiment, since it basically corresponds to the method embodiment, the relevant part may refer to the description of the method embodiment. The device embodiments described above are only schematic, and the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, may be located in One place, or can be distributed to multiple network modules. Some or all of these modules can be selected according to actual needs to achieve the purpose of the solution in this specification. Those of ordinary skill in the art can understand and implement without creative efforts.

Accordingly, an embodiment of the present specification further provides a computer device including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the following method when executing the program:

Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. In particular, as for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant part may refer to the description of the method embodiment.

A computer storage medium stores program instructions in the storage medium, and the program instructions include:

Based on the response information fed back by the potential demand responder, a target demand responder that meets the requirements of the demand proposer is selected from the potential demand responders obtained by the screening, and related information of the target demand responder is recommended to the demand proposer.

The embodiments of the present specification may take the form of a computer program product implemented on one or more storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing program code. Computer-usable storage media includes permanent and non-permanent, removable and non-removable media, and information can be stored by any method or technology. Information may be computer-readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to: phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, read-only disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, Magnetic tape cartridges, magnetic tape storage or other magnetic storage devices or any other non-transmitting medium may be used to store information that can be accessed by a computing device.

Those skilled in the art will readily think of other embodiments of the present specification after considering the specification and practicing the inventions filed herein. This description is intended to cover any variations, uses, or adaptations of this specification. These modifications, uses, or adaptations follow the general principles of this specification and include the common general knowledge or conventional technical means in the technical field to which this specification has not been applied. . It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the specification being indicated by the following claims.

It should be understood that this description is not limited to the precise structure that has been described above and illustrated in the accompanying drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of this description is limited only by the following claims.

The above are only the preferred embodiments of this specification and are not intended to limit this specification. Any modification, equivalent replacement, or improvement made within the spirit and principles of this specification shall be included in this specification Within the scope of protection.

Claims

An information recommendation method, the method includes:

Perform semantic analysis on the content of the demand in the demand request sent by the demander to obtain the intention of the demander, the demand request carrying at least the content of the demand and the position of the demander;

Preliminary screening of the demand responder based on at least the intent and the position information of the demand side, obtaining at least one potential demand responder, and pushing the demand demander's demand to the potential demand responder based on the content of the demand;

Based on the response information fed back by the potential demand responder, a target demand responder that meets the requirements of the demand proposer is selected from the potential demand responders obtained by the screening, and the relevant information of the target demand responder is recommended to the demand proposer.
The method according to claim 1, wherein the demand responder and the demand content are classified according to a preset category, and the intention is a preset category corresponding to the demand content.
The method according to claim 2, further comprising:

Extracting feature data that can characterize the preset category to which the demand responder belongs from the information of the demand responder of the known preset category;

Build a category prediction model based on the preset category and feature data of the demand responder with a known preset category;

A category prediction model is used to predict the preset category to which the demand responder of the unknown preset category belongs.
The method according to claim 3, wherein the characteristic data includes at least a frequency of receipts within a preset period of time, a distribution of the amount of receipts within a preset period of time, a distribution of receipt time within a preset period of time, and a geographic location. One or more characteristics.
According to the method of any one of claims 1 to 4, the demand of the demander includes at least audio data obtained based on the content of the demand, and the audio data is played in real time on a potential demand responder.
The method according to any one of claims 1 to 4, wherein the related information of the target demand responder includes one or more of the following information:

The identification information of the target demand responder, the response content of the target demand responder feedback, the distance information between the target demand responder and the demand proposer, and navigation guidance data where the position of the demand proposer reaches the position of the target demand responder.
The method according to any one of claims 1 to 4, recommending the target demand responder to a demand proposer comprises:

The relevant information of the target demand responder is marked on the map at a position corresponding to the position information of the target demand responder, and the marked map is sent to the demand proposer.
An offline instant demand processing method, the method includes:

The user sends to the server an offline demand request that carries at least the content of the demand and the location information of the demander;

The server performs semantic analysis on the demand content to obtain the intention of the client, at least preliminary screening of merchants according to the intent and the position information of the demand side, to obtain at least one potential merchant, and to the terminal of the potential merchant based on the demand content Push audio data used to represent the needs of the client;

The terminal of the potential merchant plays the audio data;

Based on the response information fed back by the terminal of the potential merchant after playing the audio data, the server screens out the target merchants that meet the needs of the client from the potential merchants obtained by the screening, and recommends the relevant information of the target merchant to the client.
An information recommendation device, the device includes:

An intent recognition module, configured to: perform a semantic analysis on the content of a demand in a demand request sent by a demander to obtain the intention of the demander, the demand request carrying at least the content of the demand and the position of the demander;

A preliminary screening module, configured to: perform preliminary screening on demand responders according to at least the intent and the position information of the demand parties to obtain at least one potential demand responder;

An information transmission module, configured to: push a demander's demand to a potential demand responder based on the demand content;

The target screening module is configured to: based on the response information fed back by the potential demand responders, select a target demand responder that meets the demands of the demand proposers from the potential demand responders obtained by the screening;

The information transmission module is further configured to recommend relevant information of the target demand responder to a demand proposer.
A computer device includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the program, the following method is implemented:

Perform semantic analysis on the content of the demand in the demand request sent by the demander to obtain the intention of the demander, the demand request carrying at least the content of the demand and the position of the demander;

Preliminary screening of the demand responder based on at least the intent and the position information of the demand side, obtaining at least one potential demand responder, and pushing the demand demander's demand to the potential demand responder based on the content of the demand;

Based on the response information fed back by the potential demand responder, a target demand responder that meets the requirements of the demand proposer is selected from the potential demand responders obtained by the screening, and the relevant information of the target demand responder is recommended to the demand proposer.