CN108595498B

CN108595498B - Question feedback method and device

Info

Publication number: CN108595498B
Application number: CN201810220466.0A
Authority: CN
Inventors: 朱文; 陈小桃
Original assignee: Advanced New Technologies Co Ltd
Current assignee: Advanced New Technologies Co Ltd; Advantageous New Technologies Co Ltd
Priority date: 2018-03-16
Filing date: 2018-03-16
Publication date: 2021-09-03
Anticipated expiration: 2038-03-16
Also published as: CN108595498A

Abstract

The embodiment of the application provides a problem feedback method and a problem feedback device, wherein the method comprises the following steps: the method comprises the steps that a mobile terminal sends scene data of a user feedback problem to a server, wherein the scene data comprises user data, product data and page data; the server receives scene data, and determines suspected feedback problems of the user and problem data corresponding to the suspected feedback problems according to the scene data; the server returns the question data to the mobile terminal according to the sequence of the question data, wherein the sequence of the question data is related to the question preference degree of the user; and the mobile terminal recommends the received problem data to the user according to the sequence of the problem data.

Description

Question feedback method and device

Technical Field

The present application relates to the field of computer technologies, and in particular, to a problem feedback method and apparatus.

Background

During the operation of the mobile terminal, the user can feed back the encountered question to the background server to obtain the answer to the question. For example, the user may feed back navigation-related questions to the background server to obtain answers to the navigation-related questions during the process of using the mobile terminal to perform navigation, where the navigation-related questions include, but are not limited to, how to set a common navigation route, the function of accumulated navigation mileage of registered members, and the like, and for example, the user may feed back financing-related questions to the background server to obtain answers to the financing-related questions during the process of using the mobile terminal to perform financing, where the financing-related questions include, but are not limited to, how to calculate annual profitability, whether to guarantee principal, and the like.

Based on the requirement that the user feeds back the problem to the background server, a technical scheme is needed to be provided so as to accurately push the problem data required by the user to the user, so that the user can conveniently and quickly obtain the required problem data, and the problem feedback experience of the user is improved.

Disclosure of Invention

The embodiment of the application aims to provide a problem feedback method and device, so that problem data required by a user can be accurately pushed to the user, the user can conveniently and quickly obtain the required problem data, and the problem feedback experience of the user is improved.

In order to solve the above technical problem, the embodiment of the present application is implemented as follows:

the embodiment of the application provides a problem feedback method, which comprises the following steps:

acquiring scene data of a user feedback problem; the scene data comprises user data, product data and page data;

according to the scene data, determining suspected feedback problems of the user and problem data corresponding to the suspected feedback problems;

returning the question data to the user according to the sequence of the question data; wherein the ranking of the issue data is related to the degree of issue preference of the user.

The embodiment of the present application provides a problem feedback method, including:

acquiring scene data of a user feedback problem; the scene data is triggered and sent by the mobile terminal according to the problem feedback operation of the user for the first page; the scene data comprises user data corresponding to the user, product data corresponding to the first page and page data corresponding to the first page;

sending scene data of a user feedback problem to a server; the scene data comprises user data, product data and page data;

receiving question data corresponding to suspected feedback questions of the user, which are returned by the server according to the scene data;

recommending the question data to the user according to the sequence of the question data; wherein the ranking of the issue data is related to the degree of issue preference of the user.

sending scene data of the user feedback question to a server according to the question feedback operation of the user aiming at the first page; the scene data comprises user data corresponding to the user, product data corresponding to the first page and page data corresponding to the first page;

receiving question data corresponding to the suspected feedback question of the user, which is returned by the server according to the scene data;

The embodiment of the present application provides a problem feedback apparatus, including:

the first acquisition module is used for acquiring scene data of a user feedback problem; the scene data comprises user data, product data and page data;

the first determining module is used for determining suspected feedback problems of the user and problem data corresponding to the suspected feedback problems according to the scene data;

the first returning module is used for returning the question data to the user according to the sequence of the question data; wherein the ranking of the issue data is related to the degree of issue preference of the user.

the second acquisition module is used for acquiring scene data of the user feedback problem; the scene data is triggered and sent by the mobile terminal according to the problem feedback operation of the user for the first page; the scene data comprises user data corresponding to the user, product data corresponding to the first page and page data corresponding to the first page;

a second determining module, configured to determine, according to the scene data, a suspected feedback problem of the user and problem data corresponding to the suspected feedback problem;

the second returning module is used for returning the question data to the user according to the sequence of the question data; wherein the ranking of the issue data is related to the degree of issue preference of the user.

the first sending module is used for sending scene data of the user feedback question to the server; the scene data comprises user data, product data and page data;

the first receiving module is used for receiving question data corresponding to suspected feedback questions of the user, returned by the server according to the scene data;

the first recommending module is used for recommending the question data to the user according to the sequence of the question data; wherein the ranking of the issue data is related to the degree of issue preference of the user.

the second sending module is used for sending scene data of the user feedback question to the server according to the question feedback operation of the user aiming at the first page; the scene data comprises user data corresponding to the user, product data corresponding to the first page and page data corresponding to the first page;

the second receiving module is used for receiving question data corresponding to the suspected feedback question of the user, which is returned by the server according to the scene data;

the second recommending module is used for recommending the question data to the user according to the sequence of the question data; wherein the ranking of the issue data is related to the degree of issue preference of the user.

The embodiment of the present application provides a problem feedback device, including:

a processor; and

a memory arranged to store computer executable instructions that, when executed, cause the processor to:

a processor; and

A further embodiment of the present application provides a storage medium for storing computer-executable instructions, which when executed implement the following process:

Through the technical scheme in the embodiment, the problem data can be returned to the user according to the scene of the user feedback problem, and the sequencing of the problem data is related to the problem preference degree of the user, so that the problem data required by the user is accurately pushed to the user, the user can conveniently and quickly acquire the required problem data, and the problem feedback experience of the user is improved.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort.

Fig. 1 is a schematic view of an application scenario of question feedback according to an embodiment of the present application;

FIG. 2 is a schematic flow chart illustrating a problem feedback method according to an embodiment of the present application;

fig. 3 is a schematic structural diagram of a user question bank according to an embodiment of the present application;

FIG. 4 is a diagram illustrating a process for identifying suspected feedback questions from a user question bank according to an embodiment of the present application;

FIG. 5 is a schematic flow chart illustrating a problem feedback method according to another embodiment of the present application;

FIG. 6 is a schematic flow chart illustrating a problem feedback method according to another embodiment of the present application;

FIG. 7 is a flowchart illustrating a problem feedback method according to another embodiment of the present application;

fig. 8a is a schematic diagram of a first scenario for recommending question data to a user according to an embodiment of the present application;

FIG. 8b is a diagram illustrating a second scenario in which question data is recommended to a user according to an embodiment of the present application;

fig. 8c is a schematic diagram of a third scenario for recommending question data to a user according to an embodiment of the present application;

fig. 8d is a schematic diagram of a fourth scenario for recommending question data to a user according to an embodiment of the present application;

FIG. 9 is a flowchart illustrating a problem feedback method according to another embodiment of the present application;

fig. 10 is a schematic block diagram of a user feedback device according to an embodiment of the present application;

fig. 11 is a schematic diagram illustrating a module configuration of a user feedback device according to an embodiment of the present application;

fig. 12 is a schematic block diagram of a user feedback device according to an embodiment of the present application;

fig. 13 is a schematic diagram illustrating a module configuration of a user feedback device according to an embodiment of the present application;

fig. 14 is a schematic structural diagram of a problem feedback device according to an embodiment of the present application.

Detailed Description

The embodiment of the application provides a problem feedback method and device, which are used for accurately pushing problem data required by a user to the user, facilitating the user to quickly obtain the required problem data and improving the problem feedback experience of the user.

In order to make those skilled in the art better understand the technical solutions in the present application, 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 is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

Fig. 1 is a schematic view of an application scenario of problem feedback according to an embodiment of the present application, as shown in fig. 1, in the scenario, a mobile terminal 100 and a server 200 are in communication connection through a network 300, the mobile terminal 100 may be an intelligent terminal such as a mobile phone, a tablet computer, a notebook computer, a desktop computer, and a vehicle-mounted computer, and the server 200 may be a server cluster or a cloud server. The mobile terminal 100 sends scene data of a user feedback question to the server 200 through the network 300, and the server 200 receives the scene data and returns question data corresponding to a suspected feedback question of the user to the mobile terminal 100, so as to recommend question data that the user may pay attention to in a scene corresponding to the scene data to the user, wherein the ranking of the question data is related to the question preference degree of the user, for example, the ranking of the question data is closer to the front when the question preference degree of the user is higher. The mobile terminal 100 receives the problem data and displays the problem data according to the sequence of the problem data, so that the problem data required by the user is accurately pushed to the user, the user can conveniently and quickly acquire the required problem data, and the problem feedback experience of the user is improved.

Fig. 2 is a schematic flowchart of a problem feedback method according to an embodiment of the present application, where the method can be executed by the server in fig. 1, and as shown in fig. 2, the method includes:

step S202, scene data of a user feedback problem is obtained; the scene data comprises user data, product data and page data;

step S204, according to the scene data, determining suspected feedback problems of the user and problem data corresponding to the suspected feedback problems;

step S206, returning the question data to the user according to the sequence of the question data; wherein the ranking of the issue data is related to the user's issue preference level.

For example, the question data is sent to the mobile terminal according to the sequence of the question data, so that the mobile terminal returns the question data to the user.

The question data corresponding to the suspected feedback question includes, but is not limited to, a question stem, an answer writer, answer writing time, answer praise number and the like of the suspected feedback question.

In the embodiment of the application, scene data of the user feedback problem is obtained, suspected feedback problems of the user and problem data corresponding to the suspected feedback problems are determined according to the scene data, and the problem data are returned to the user according to the sequence of the problem data, wherein the sequence of the problem data is related to the problem preference degree of the user. Therefore, according to the problem feedback method and device, the problem data can be returned to the user according to the scene of the user feedback problem, the problem data are sorted and related to the problem preference degree of the user, the problem data required by the user are accurately pushed to the user, the user can conveniently and quickly obtain the required problem data, and the problem feedback experience of the user is improved.

In step S202, the server obtains scene data of the user feedback question, where the scene data includes user data, product data, and page data. In one embodiment, the scene data is triggered and acquired by the mobile terminal according to the problem feedback operation of the user for the first page and is sent to the server, and the scene data comprises user data corresponding to the user, product data corresponding to the first page and page data corresponding to the first page.

The user data includes, but is not limited to, a user identification including, but not limited to, at least one of a login name, a user identification, and a user ID (identity) of the user. The product data is corresponding to the first page and includes, but is not limited to, a product category, a product ID, and the like. The page data is page data corresponding to the first page, and includes, but is not limited to, a page identifier and the like. In one particular embodiment, the products are financial products, the product categories include, but are not limited to, funds, stocks, etc., and the pages are identified as "detail pages," "purchase pages," etc.

In one embodiment, a mobile terminal displays a first page, a user executes a problem feedback operation on the first page, the mobile terminal acquires user data corresponding to the user, product data corresponding to the first page and page data corresponding to the first page under the trigger of the problem feedback operation on the first page, the user data, the product data and the page data are used as scene data of a user feedback problem and are sent to a server, and the server acquires the scene data of the user feedback problem. The product corresponding to the first page may be one of a plurality of products provided by the server to the user, for example, one of a plurality of financial products provided by the server to the user.

The problem feedback operation for the first page includes at least one of: the method comprises the steps of carrying out screenshot operation aiming at a first page, executing specified touch operation on the first page, shaking the mobile terminal when the mobile terminal displays the first page, activating the first page at a specified place, and starting a camera when the first page is displayed.

The designated touch operation comprises clicking, sliding and other operations aiming at the first page. Activating the first page may be opening the first page or entering the first page. In one embodiment, after a user performs a screenshot operation on a first page, a mobile terminal acquires scene data and sends the scene data to a server, in another embodiment, after the user performs a designated touch operation on the first page, the mobile terminal acquires the scene data and sends the scene data to the server, and in yet another embodiment, after the user performs the screenshot operation and the designated touch operation on the first page in sequence, the mobile terminal acquires the scene data and sends the scene data to the server. Of course, the mobile terminal may also acquire the scene data and send the scene data to the server when monitoring at least one of the situations that the user shakes the mobile terminal when displaying the first page, activates the first page at a specified location, starts a camera when displaying the first page, and the like.

In this embodiment, the problem feedback operation for the first page is set to include multiple operation forms, so that the operation forms for the user to feed back the problem can be enriched, and the requirement of the user for feeding back the problem through different operation forms can be met.

In the step S204, the server determines the suspected feedback question of the user and the question data corresponding to the suspected feedback question according to the acquired scene data, where the ranking of the question data is related to the question preference degree of the user, for example, the higher the question preference degree of the user, the higher the ranking of the question data.

In one embodiment, the server determines the suspected feedback problem of the user and problem data corresponding to the suspected feedback problem according to the acquired scene data, specifically:

(11) screening target problems in a user problem library according to the product data and the page data;

(12) determining the question preference of a user according to user data, and performing first sequencing on target questions according to the question preference;

(13) and taking the sorted target problems as suspected feedback problems, and taking the problem data corresponding to the sorted target problems as the problem data corresponding to the suspected feedback problems.

Specifically, the user question bank includes a plurality of questions, and the user questions included in the user question bank are obtained according to manual collection and/or user feedback. For example, the staff member actively collects questions about the provided product and puts the collected questions into the user question bank, and for example, the server obtains questions about the provided product fed back by the user and puts the obtained questions into the user question bank. The user question bank also contains question data corresponding to each question, and the question data includes but is not limited to question stem, answer writer, answer writing time, answer praise number and the like of the question.

Fig. 3 is a schematic structural diagram of a user question bank provided in an embodiment of the present application, and as shown in fig. 3, the user question bank includes a plurality of user question sub-banks, the user question sub-banks have a corresponding relationship with product categories, each user question sub-bank corresponds to one product category, for example, the user question sub-bank 1 corresponds to a fund, the user question sub-bank 2 corresponds to a stock, and the user question sub-bank n corresponds to a security. Each user question sub-library comprises a plurality of user questions, and the user questions in each user question sub-library have corresponding relations with the page identifications. As shown in fig. 3, user questions 1, 2, 3 in user question sub-repository 1 correspond to page identification "detail pages", and user questions 2, 3, 5 in user question sub-repository 1 correspond to page identification "purchase pages". In the user question bank, different user question sub-banks correspond to different products and contain different user questions, and in the same user question sub-bank, the questions corresponding to different page identifications may overlap, and the same question corresponds to a detail page and a purchase page.

In this embodiment, first, target questions are screened from a user question bank according to product data and page data, specifically:

(111) determining a product category according to the product data, and determining a page identifier according to the page data;

(112) screening a target user question sub-library corresponding to the determined product category from a plurality of user question sub-libraries;

(113) screening the user problems corresponding to the determined page identifications in the target user problem sub-library;

(114) and taking the user problems obtained by screening as target problems.

Firstly, extracting product types from the product data, or determining the product types according to product IDs in the product data, and extracting page identifications from the page data. Each user question sub-library corresponds to one product category, so that a target user question sub-library corresponding to the determined product category can be screened; because the user question in each user question sub-library has a corresponding relation with the page identifier, the user question corresponding to the determined page identifier can be screened in the target user question sub-library based on the corresponding relation, and finally, the user question obtained by screening is taken as the target question. The target problem represents a problem that a user may present in a scene composed of a product corresponding to the product data and a page corresponding to the page data.

After determining the target problem, the server determines the problem preference of the user according to the user data, and performs a first ordering on the target problem according to the problem preference, specifically:

(121) determining hierarchical dimension information of a user relative to a target product according to the user data; wherein, the target product is a product corresponding to the product data; the hierarchical dimension information includes at least one of the following information;

a user rating of the user relative to the target product, a purchase preference of the user relative to the target product, a transaction frequency of the user relative to the target product, a risk-fighting rating of the user relative to the target product, an asset condition of the user relative to the target product;

(122) determining problem preference of a user according to the determined hierarchical dimension information;

(123) determining a main concern problem and a secondary concern problem of a user in the target problem according to the determined problem preference;

(124) the ordering of the primary concerns is set before the secondary concerns, so far the first ordering is completed.

The server side is provided with hierarchical dimension information of users relative to each product, table 1 is a schematic table of the hierarchical dimension information provided in an embodiment of the present application, and as shown in table 1, the hierarchical dimension information includes at least one of user level, purchase preference, transaction frequency, risk-fighting-resistant level and asset condition of a user relative to each product. The user level of the user relative to the product includes, but is not limited to, determining according to the purchase duration or purchase times of the user relative to the product, if the user has purchased the product for three years, the user is an old-level user, and if the user has purchased the product once, the user is a new-level user. The user's purchasing preference with respect to a product indicates which type of sub-product the user is inclined to purchase under the product, or indicates what parameters the user is inclined to purchase for that type of product, such as the user's purchasing inclination is to purchase a short term (within three months) fund, or the user is inclined to purchase an exponential fund. The trading frequency of the user with respect to each product may be exemplified by that the trading frequency of the user for stocks is on average once a month. The risk-resistant strike rating of the user relative to the product represents the risk rating that the user can bear during the purchase of the product, such as the highest risk rating represents that the user can bear the full loss of funds for purchasing the product, and the lowest risk rating represents that the user cannot accept the loss of funds for purchasing the product. The user's asset condition with respect to a product includes, but is not limited to, the amount of funds the user can use to purchase the product.

TABLE 1

	User level	Purchasing preferences	Frequency of transactions	Risk-resistant impact rating	Status of assets
						Fund A	New level	Short term fund	Once a month	Highest level	$ 10 ten thousand
Stock B	Old grade	Class A stock ticket	Once a month	Lowest level of	5 million dollars

In this embodiment, a product corresponding to the product data acquired by the server is a target product, the server extracts a user identifier from the acquired user data, searches in the database according to the user identifier, and obtains hierarchical dimension information of the user relative to the target product, that is, obtains at least one of a user level, a purchase preference, a transaction frequency, an anti-risk strike level, and an asset condition of the user relative to the target product.

Then, the server determines the problem preference of the user according to the hierarchical dimension information of the user relative to the target product. Since there is usually a difference in questions that users of different user levels, different purchasing preferences, different transaction frequencies, different anti-risk strike grades, and different asset conditions need to ask questions with respect to the target product, such as a new-level user for the target product who has a greater probability of asking "how to calculate the profitability" but not "how to purchase the product using the old user identity", or a user whose purchasing preference for the target product is a short-term fund, who has a greater probability of asking "how to select the short-term fund" but not "how to select the exponential fund", the server can determine the question preferences of the users according to the hierarchical dimension information of the users with respect to the target product.

The server determines the problem preference of the user according to the hierarchical dimension information of the user relative to the target product, and specifically comprises the following steps: determining the label of the problem concerned by the user under each item of hierarchical dimension information, taking the set of the labels of the problems concerned by the user under each item of hierarchical dimension information as the problem preference of the user, wherein, the label of the question can be extracted from the hierarchical dimension information of the user, for example, the hierarchical dimension information includes the user level 'old user', the question may be labeled "old user", the hierarchical dimension information includes a purchase preference "short term fund", the question may be labeled "short term fund", the hierarchical dimensional information includes the transaction frequency "once a week", the label of the problem may be "frequent transaction", the hierarchical dimensional information includes the highest level of risk-fighting ranking, the label for the problem may be "high risk", the hierarchical dimensional information includes the status of the asset "over $ 10", and the label for the problem may be "big buy". Taking a set of tags of the problem concerned by the user under each item of hierarchical dimension information as the problem preference of the user, in the above example, the problem preference of the user includes: "old user", "short term fund", "frequent transaction", "high risk", "large buy".

Then, a primary concern and a secondary concern of the user are determined among the target concerns according to the question preferences of the user. The problem in the target problem matched with the problem preference of the user is the main concern problem of the user, and the problem not matched with the problem preference of the user is the secondary concern problem of the user. The question stem of the question comprises at least keywords corresponding to the question preferences of the preset number of users, the keywords corresponding to the question preferences of the users can be extracted from the question preferences of the users, for example, nonsense words in the question preferences of the users are removed, then keyword extraction is carried out on the question preferences of the users, and the preset number can be more than or equal to 1. As in the above example, the question that matches the user's question preferences may be "how the old user picks a large fund for a short-term transaction". It can be understood that the primary concern question of the user is a question that the user may pay attention to, which is determined according to the hierarchical dimension information of the user relative to the target product, and the secondary concern question of the user is a question that the user may not pay attention to, which is determined according to the hierarchical dimension information of the user relative to the target product.

Finally, in the target questions, the ranking of the main concern questions of the user is set before the secondary concern questions, the ranked target questions are used as suspected feedback questions, and the question data corresponding to the ranked target questions are used as the question data corresponding to the suspected feedback questions, wherein the question data corresponding to the suspected feedback questions can be obtained from a user question bank and include but not limited to data such as question stems, answers, answer writers, answer compiling time, answer praise numbers and the like of the suspected feedback questions.

In this embodiment, the suspected feedback problem is determined according to the product data and the page data corresponding to the user when feeding back the problem, and the sequence of the suspected feedback problem is determined according to the user data of the user, so that when the problem data corresponding to the suspected feedback problem is sent to the user, the user can obtain the problem data matched with the scene of the feedback problem, the problem data required by the user is accurately pushed to the user, the user can conveniently and quickly obtain the required problem data, and the problem feedback experience of the user is improved.

In the suspected feedback problem, the main concern problem is located before the secondary concern problem, so that in the problem data corresponding to the suspected feedback problem, the problem data of the main concern problem is located before the problem data of the secondary concern problem, the sequencing of the problem data corresponding to the suspected feedback problem is related to the problem preference degree of the user, and after the user obtains the problem data corresponding to the suspected concern problem, the problem data corresponding to the main concern problem can be browsed at first, so that the problem data required by the user can be further accurately pushed to the user, the user can conveniently and quickly obtain the required problem data, and the problem feedback experience of the user is improved.

In another embodiment, the question preferences of the user may also be determined from the user data, the target questions being first ordered according to the question preferences by: inputting the hierarchical dimension information and the problem data of the user into a pre-trained sequencing model, determining and sequencing the main concern problem and the secondary concern problem of the user in the input problem data according to the hierarchical dimension information of the user by the sequencing model, and outputting a sequencing result, wherein the main concern problem of the user is positioned before the secondary concern problem of the user in the sequencing result.

The ranking model can be obtained by adopting an AI (Artificial Intelligence) algorithm, specifically, the hierarchical dimension information of a plurality of users and the click data of each user to the problem are obtained, including which problem is clicked and which problem is not clicked, the ranking model is obtained by adopting the AI algorithm and training based on the hierarchical dimension information of each user and the click data of each user to the problem. Wherein, the question refers to a question browsed by a user in a question feedback scene.

In view of the problem of ranking among the main concerns in the suspected feedback problem, the method in this embodiment further includes, before taking the sorted target problem as the suspected feedback problem and taking the problem data corresponding to the sorted target problem as the problem data corresponding to the suspected feedback problem:

(21) after the first ordering is completed, performing second ordering on the main concern questions in the target questions according to the question heat of the main concern questions in the target questions;

(22) and taking the second sorted target problem as a sorted target problem.

In this embodiment, each question in the user question bank has a corresponding question heat, and the question heat may be determined according to at least one of the following indicators: the amount of clicks made by the user on the question, the time of release of the question, and the degree of association between the question and the current news hotspot. The association degree between the question and the current news hotspot can be represented by an association level, for example, the highest association level represents the highest association degree.

In this embodiment, in the target problems after the first ranking, the main concern problems are subjected to second ranking according to the problem heat of the main concern problems, for example, the main concern problems are ranked from front to back according to the order of the problem heat from high to low, and the target problems after the second ranking are regarded as the ranked target problems, that is, the target problems after the second ranking are suspected feedback problems.

Fig. 4 is a schematic diagram of a process of determining a suspected feedback problem by a user question bank according to an embodiment of the present application, and as shown in fig. 4, a target problem is first determined from the user question bank, then the target problem is first sorted to obtain a target problem with a primary concern before and a secondary concern after, and then the primary concern is second sorted, in the second sorting, the primary concern is sorted from front to back according to a sequence of a low degree of heat of the problem, and the target problem after the second sorting is the suspected feedback problem. It should be noted that, in the target problem after the second ranking, the state that the main concern problem is before and the secondary concern problem is after is still maintained.

In conclusion, by the method in the embodiment of the application, the problem data can be returned to the user according to the scene of the problem fed back by the user, and the sequence of the problem data is related to the problem preference degree of the user, so that the problem data required by the user is accurately pushed to the user, the user can conveniently and quickly obtain the required problem data, and the problem feedback experience of the user is improved.

Fig. 5 is a flowchart illustrating a problem feedback method according to another embodiment of the present application, where the method can be executed by the server in fig. 1, and as shown in fig. 5, the method includes:

step S502, scene data of a user feedback question is obtained; the method comprises the steps that scene data are triggered and sent by a mobile terminal according to problem feedback operation of a user for a first page; the scene data comprises user data corresponding to a user, product data corresponding to a first page and page data corresponding to the first page;

step S504, according to the scene data, a suspected feedback problem of the user and problem data corresponding to the suspected feedback problem are determined;

step S506, returning the question data to the user according to the sequence of the question data; wherein the ranking of the issue data is related to the user's issue preference level.

In this embodiment, the question data corresponding to the suspected feedback question includes, but is not limited to, data of a stem, an answer writer, an answer writing time, an answer approval number, and the like of the suspected feedback question.

In this embodiment, the problem feedback operation for the first page includes at least one of the following operations: the method comprises the steps of carrying out screenshot operation aiming at a first page, executing specified touch operation on the first page, shaking the mobile terminal when the mobile terminal displays the first page, activating the first page at a specified place, and starting a camera when the first page is displayed.

Specific description about the present embodiment may refer to the foregoing parts, which are not repeated here.

Fig. 6 is a flowchart illustrating a problem feedback method according to another embodiment of the present application, where the method corresponds to the method in fig. 2 and can be executed by the mobile terminal in fig. 1, as shown in fig. 6, where the method includes:

step S602, sending scene data of the user feedback question to a server; the scene data comprises user data, product data and page data;

step S604, receiving question data corresponding to suspected feedback questions of the user, which are returned by the server according to the scene data;

step S606, recommending the question data to the user according to the sequence of the question data; wherein the ranking of the issue data is related to the user's issue preference level.

In the embodiment of the application, the scene data of the user feedback questions are sent to the server, the question data corresponding to the suspected feedback questions of the user returned by the server according to the scene data are received, the ranking of the question data is related to the question preference degree of the user, and the question data are recommended to the user according to the ranking of the question data. Therefore, the problem data determined based on the scene of the user feedback problem can be recommended to the user through the method and the device, the sequencing of the problem data is related to the problem preference degree of the user, the problem data required by the user is accurately pushed to the user, the user can conveniently and quickly obtain the required problem data, and the problem feedback experience of the user is improved.

In the step S602, the scene data of the user feedback question is sent to the server, in an embodiment, the scene data is triggered by the mobile terminal according to the question feedback operation of the user for the first page and is obtained and sent to the server, that is, the mobile terminal obtains the scene data of the user feedback question according to the question feedback operation of the user for the first page and sends the scene data to the server, where the scene data includes user data corresponding to the user, product data corresponding to the first page, and page data corresponding to the first page.

Wherein the question feedback operation for the first page comprises at least one of the following operations: the method comprises the steps of carrying out screenshot operation aiming at a first page, executing specified touch operation on the first page, shaking the mobile terminal when the mobile terminal displays the first page, activating the first page at a specified place, and starting a camera when the first page is displayed.

In step S606, recommending the question data to the user according to the sequence of the question data, specifically:

(31) displaying the problem data on a screen according to the sequence of the problem data, and displaying a first page corresponding to the page data on the screen in a split screen mode;

(32) and adjusting the proportion of the screen areas respectively occupied by the first page and the question data according to the checking operation of the user on the question data.

Specifically, when the mobile terminal recommends the problem data to the user according to the sorting of the problem data, a first page corresponding to the page data and the problem data are displayed on a screen of the mobile terminal in a split-screen mode, wherein the problem data are displayed according to the sorting of the problem data, then, when the checking operation of the user for the problem data is received, if the user clicks a certain problem data, the proportion of the screen area occupied by the first page and the problem data is adjusted, for example, the screen area occupied by the checked problem data is increased, the screen area occupied by the first page is reduced, and therefore the problem data can be better browsed by the user.

After detecting that the user performs the checking operation aiming at the question data, the mobile terminal also at least displays the question stem and answer data contained in the checked question data so as to facilitate the browsing of the user.

In this embodiment, the first page is a page scene corresponding to the problem data, and the problem data and the page scene corresponding to the problem data can be associated by displaying the problem data and the first page in a split-screen manner, so that the experience of a user in browsing the problem data is improved. By adjusting the proportion of the screen area occupied by the first page and the problem data respectively, a user can browse the problem data conveniently, and the experience of browsing the problem data by the user is further improved.

Fig. 7 is a flowchart illustrating a problem feedback method according to another embodiment of the present application, where the method can be executed by the mobile terminal in fig. 1, and as shown in fig. 7, the method includes:

step S702, sending scene data of the user feedback question to a server according to the question feedback operation of the user aiming at the first page; the scene data comprises user data corresponding to a user, product data corresponding to a first page and page data corresponding to the first page;

step S704, receiving question data corresponding to suspected feedback questions of the user returned by the server according to the scene data;

step S706, recommending the question data to the user according to the sequence of the question data; wherein the ranking of the issue data is related to the user's issue preference level.

In step S706, recommending the question data to the user according to the sequence of the question data, including:

(41) displaying the question data on a screen according to the sequence of the question data, and displaying a first page on the screen in a split screen mode;

(42) and adjusting the proportion of the screen areas respectively occupied by the first page and the question data according to the checking operation of the user on the question data.

Reference may be made to the preceding description with respect to processes (41) (42), which is not heavily loaded here.

Fig. 8a is a first scenario diagram of recommending problem data to a user according to an embodiment of the present application, fig. 8b is a second scenario diagram of recommending problem data to a user according to an embodiment of the present application, fig. 8c is a third scenario diagram of recommending problem data to a user according to an embodiment of the present application, fig. 8d is a fourth scenario diagram of recommending problem data to a user according to an embodiment of the present application, as shown in fig. 8a to 8d, the mobile terminal displays a detail page of a product a, after the mobile terminal detects that a user performs a screenshot operation on the detail page of the product a, the mobile terminal displays a "problem feedback" button, the problem feedback button is a countdown button, if the user does not click the button within the countdown end, the mobile terminal abandons the response, and if the user clicks the button within the countdown end, the mobile terminal acquires user data of the user, And sending the product data corresponding to the product A and the page data corresponding to the detail page to a server by taking the obtained user data, the product data and the page data as scene data of the user feedback problem.

The server determines suspected feedback problems of the user and problem data corresponding to the suspected feedback problems according to the scene data, and sends the problem data to the mobile terminal, wherein the ranking of the problem data is related to the problem preference degree of the user, and the higher the problem preference degree of the user is, the higher the ranking of the problem data is. After receiving the question data, the mobile terminal displays the detail page of the product a and the received question data in a split screen manner on the screen as shown in fig. 8c, and specifically displays the detail page of the product a and the question stem data in the received question data, and after detecting the checking operation of the user on the question data, the mobile terminal reduces the screen area occupied by the detail page of the product a, enlarges the screen area occupied by the question data, and displays answer data included in the clicked question data as shown in fig. 8 d. The question data includes, but is not limited to, a stem, an answer writer, an answer writing time, an answer approval number, and the like.

Therefore, through the embodiment, the problem data which is possibly required by the user can be recommended to the user according to the screenshot operation of the user, so that the problem data required by the user can be accurately pushed to the user by combining the problem feedback scene of the user, the user can conveniently and quickly obtain the required problem data, and the problem feedback experience of the user is improved.

In view of the fact that the method executed by the server in fig. 2 or fig. 5 can be separately executed by a plurality of servers, fig. 9 is a schematic flow chart of a problem feedback method provided by another embodiment of the present application, and as shown in fig. 9, the flow chart includes:

step S902, the mobile terminal sends the scene data of the user feedback question to the first server.

Specifically, the mobile terminal sends scene data of a user feedback question to the first server according to a question feedback operation of the user for the first page; the scene data comprises user data corresponding to a user, product data corresponding to a first page and page data corresponding to the first page;

step S904, the first server receives the scene data, and determines the category of the target product, the page identifier, and the hierarchical dimension information of the user relative to the target product according to the scene data.

The first server determines the category of a target product according to product data contained in the scene data, determines a page identifier according to page data contained in the scene data, and determines hierarchical dimension information of a user relative to the target product according to user data contained in the scene data. And the product corresponding to the product data is the target product.

Step S906, the first server sends the target product category, the page identification and the hierarchical dimension information of the user relative to the target product to the second server.

Step S908, the second server determines the suspected feedback problem of the user and determines the problem data corresponding to the suspected feedback problem according to the category of the target product, the page identifier, and the hierarchical dimension information of the user relative to the target product.

Wherein, the second server is provided with the user question bank.

In step S910, the second server sends the question data corresponding to the suspected feedback question to the first server, where the ranking of the question data is related to the question preference degree of the user.

In step S912, the first server receives the question data, and sends the question data to the mobile terminal according to the sequence of the question data.

In step S914, the mobile terminal receives the question data, and recommends the question data to the user according to the ranking of the question data.

Therefore, according to the problem feedback method and device, the problem feedback scene of the user can be combined, the problem data required by the user can be accurately pushed to the user, the user can conveniently and quickly obtain the required problem data, and the problem feedback experience of the user is improved.

Fig. 10 is a schematic diagram illustrating a module composition of a user feedback device according to an embodiment of the present application, and as shown in fig. 10, the device includes:

a first obtaining module 1001, configured to obtain scene data of a user feedback question; the scene data comprises user data, product data and page data;

a first determining module 1002, configured to determine, according to the scene data, a suspected feedback problem of the user and problem data corresponding to the suspected feedback problem;

a first returning module 1003, configured to return the question data to the user according to the sorting of the question data; wherein the ranking of the issue data is related to the degree of issue preference of the user.

Optionally, the first determining module 1002 is specifically configured to:

screening target problems in a user problem library according to the product data and the page data;

determining question preferences of a user according to the user data, and performing first sequencing on the target questions according to the question preferences;

and taking the sorted target problem as the suspected feedback problem, and taking the problem data corresponding to the sorted target problem as the problem data corresponding to the suspected feedback problem.

Optionally, the user question bank includes a plurality of user question sub-banks, the user question sub-banks have a corresponding relationship with the product category, and the user questions in the user question sub-banks have a corresponding relationship with the page identifier;

the first determining module 1002 is further specifically configured to:

determining a product category according to the product data, and determining a page identifier according to the page data;

screening the target user question sub-base corresponding to the determined product category from the plurality of user question sub-bases;

screening the user problems corresponding to the determined page identification in the target user problem sub-library;

and taking the user problem obtained by screening as the target problem.

Optionally, the first determining module 1002 is further specifically configured to:

determining the hierarchical dimension information of the user relative to the target product according to the user data; wherein the target product is a product corresponding to the product data; the hierarchical dimension information comprises at least one of the following information;

according to the determined hierarchical dimension information, determining problem preference of a user;

determining a primary concern problem and a secondary concern problem of the user in the target problem according to the problem preference;

and setting the sequence of the primary concern problem before the secondary concern problem.

Optionally, the apparatus further comprises a sorting module configured to:

before the target problem which is sorted is taken as the suspected feedback problem, after the first sorting is finished, performing second sorting on the main concern problem in the target problem according to the problem heat degree of the main concern problem in the target problem;

and taking the target problem after the second sorting as the target problem after the sorting is finished.

Optionally, the user questions included in the user question bank are obtained according to a manual collection and/or user feedback.

Therefore, according to the problem feedback method and device, the problem data can be returned to the user according to the scene of the user feedback problem, the problem data are sorted and related to the problem preference degree of the user, the problem data required by the user are accurately pushed to the user, the user can conveniently and quickly obtain the required problem data, and the problem feedback experience of the user is improved.

Fig. 11 is a schematic diagram illustrating a module composition of a user feedback device according to an embodiment of the present application, and as shown in fig. 11, the device includes:

a second obtaining module 1101, configured to obtain scene data of a user feedback question; the scene data is triggered and sent by the mobile terminal according to the problem feedback operation of the user for the first page; the scene data comprises user data corresponding to the user, product data corresponding to the first page and page data corresponding to the first page;

a second determining module 1102, configured to determine, according to the scene data, a suspected feedback problem of the user and problem data corresponding to the suspected feedback problem;

a second returning module 1103, configured to return the question data to the user according to the ranking of the question data; wherein the ranking of the issue data is related to the degree of issue preference of the user.

Optionally, the problem feedback operation for the first page includes at least one of the following operations:

the method comprises the steps of aiming at screenshot operation of a first page, executing specified touch operation on the first page, shaking the mobile terminal when the mobile terminal displays the first page, activating the first page at a specified place, and starting a camera when the first page is displayed.

Fig. 12 is a schematic diagram illustrating a module composition of a user feedback device according to an embodiment of the present application, and as shown in fig. 12, the device includes:

a first sending module 1201, configured to send scene data of a user feedback question to a server; the scene data comprises user data, product data and page data;

a first receiving module 1202, configured to receive question data corresponding to a suspected feedback question of a user, which is returned by the server according to the scene data;

a first recommending module 1203, configured to recommend the question data to a user according to the ranking of the question data; wherein the ranking of the issue data is related to the degree of issue preference of the user.

Optionally, the first recommending module 1203 is specifically configured to:

displaying the question data on a screen according to the sequence of the question data, and displaying a first page corresponding to the page data on the screen in a split screen mode;

and adjusting the proportion of the screen areas respectively occupied by the first page and the question data according to the checking operation of the user on the question data.

Therefore, the problem data determined based on the scene of the user feedback problem can be recommended to the user through the method and the device, the sequencing of the problem data is related to the problem preference degree of the user, the problem data required by the user is accurately pushed to the user, the user can conveniently and quickly obtain the required problem data, and the problem feedback experience of the user is improved.

Fig. 13 is a schematic diagram illustrating a module composition of a user feedback device according to an embodiment of the present application, and as shown in fig. 13, the device includes:

a second sending module 1301, configured to send scene data of the user feedback question to a server according to a question feedback operation of the user for the first page; the scene data comprises user data corresponding to the user, product data corresponding to the first page and page data corresponding to the first page;

a second receiving module 1302, configured to receive question data corresponding to the suspected feedback question of the user, which is returned by the server according to the scene data;

a second recommending module 1303, configured to recommend the question data to the user according to the ranking of the question data; wherein the ranking of the issue data is related to the degree of issue preference of the user.

the method comprises the steps of aiming at screenshot operation of a first page, executing appointed touch operation on the first page, shaking the mobile terminal when the first page is displayed on the mobile terminal, activating the first page at an appointed place, and starting a camera when the first page is displayed.

Optionally, the second recommending module 1303 is specifically configured to:

displaying the question data on a screen according to the sequence of the question data, and displaying the first page on the screen in a split screen mode;

Fig. 14 is a schematic structural diagram of the problem feedback device provided in an embodiment of the present application, and as shown in fig. 14, the problem feedback device may have a relatively large difference due to different configurations or performances, and may include one or more processors 1401 and a memory 1402, where the memory 1402 may store one or more stored applications or data. Memory 1402 may be, among other things, transient storage or persistent storage. The application program stored in memory 1402 may include one or more modules (not shown), each of which may include a series of computer-executable instructions in a device for feeding back questions. Still further, the processor 1401 may be configured to communicate with the memory 1402, and execute a series of computer-executable instructions in the memory 1402 on the problem feedback device. The problem feedback apparatus may also include one or more power sources 1403, one or more wired or wireless network interfaces 1404, one or more input-output interfaces 1405, one or more keyboards 1406, and the like.

In one particular embodiment, the problem feedback apparatus includes a memory, and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for the problem feedback apparatus, and the one or more programs configured to be executed by the one or more processors include computer-executable instructions for:

Optionally, when executed, the determining, according to the scene data, a suspected feedback problem of a user and problem data corresponding to the suspected feedback problem includes:

Optionally, when executed by computer executable instructions, the user question bank comprises a plurality of user question sub-banks, the user question sub-banks have a corresponding relationship with the product category, and user questions in the user question sub-banks have a corresponding relationship with the page identifier;

the step of screening target questions in a user question bank according to the product data and the page data comprises the following steps:

and taking the user problem obtained by screening as the target problem.

Optionally, the computer executable instructions, when executed, determine question preferences of the user from the user data, perform a first ordering of the target questions according to the question preferences, comprising:

Optionally, when executed, the computer-executable instructions further comprise, before the target problem with the sorted completion is the suspected feedback problem:

after the first ordering is completed, performing second ordering on the main concern questions in the target questions according to the question heat of the main concern questions in the target questions;

Optionally, the computer executable instructions, when executed, cause the user questions contained in the user question bank to be derived from a manual collection and/or user feedback.

In another particular embodiment, the problem feedback device includes a memory, and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for the problem feedback device, and the one or more programs configured to be executed by the one or more processors include computer-executable instructions for:

Optionally, the problem feedback operations for the first page, when executed, comprise at least one of:

Optionally, the computer-executable instructions, when executed, recommend the issue data to the user according to the ranking of the issue data, comprising:

Optionally, the computer-executable instructions, when executed, recommend the issue data to the user in accordance with the ranking of the issue data, comprising:

In a specific embodiment, the storage medium may be a usb disk, an optical disk, a hard disk, or the like, and when the storage medium stores the computer-executable instructions, the following process can be implemented:

Optionally, when executed by a processor, the determining, according to the scene data, a suspected feedback problem of the user and problem data corresponding to the suspected feedback problem includes:

Optionally, the storage medium stores computer-executable instructions that, when executed by the processor, the user question bank includes a plurality of user question sub-banks, the user question sub-banks have a correspondence with the product category, and the user questions in the user question sub-banks have a correspondence with the page identifier;

and taking the user problem obtained by screening as the target problem.

Optionally, the storage medium stores computer executable instructions that, when executed by the processor, determine question preferences of the user based on the user data, perform a first ordering of the target questions based on the question preferences, comprising:

Optionally, the storage medium stores computer-executable instructions that, when executed by the processor, further comprise, before the target problem that is sorted to completion is taken as the suspected feedback problem:

Optionally, the storage medium stores computer-executable instructions that, when executed by the processor, cause the user questions contained in the user question bank to be derived from a manual search and/or user feedback.

In another specific embodiment, the storage medium may be a usb disk, an optical disk, a hard disk, or the like, and when executed by the processor, the storage medium stores computer-executable instructions for implementing the following processes:

Optionally, the storage medium stores computer-executable instructions that, when executed by the processor, recommend the question data to the user in accordance with the ranking of the question data, including:

Optionally, the storage medium stores computer-executable instructions that, when executed by the processor, cause the problem feedback operation for the first page to include at least one of:

Optionally, the storage medium stores computer-executable instructions that, when executed by the processor, recommend the question data to the user in the order of the question data, including:

In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The 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 technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A problem feedback method, comprising:

returning the question data to the user according to the sequence of the question data; the ordering of the question data is related to the question preference degree of a user, the question preference is determined according to the hierarchical dimension information of the user relative to a target product, and the target product is a product corresponding to the product data; determining the hierarchical dimension information of the user relative to the target product according to the user data; the hierarchical dimension information comprises at least one of the following information;

a user rating of the user relative to the target product, a purchase preference of the user relative to the target product, a transaction frequency of the user relative to the target product, a risk-fighting rating of the user relative to the target product, an asset condition of the user relative to the target product.

2. The method according to claim 1, wherein the determining the suspected feedback question of the user and the question data corresponding to the suspected feedback question according to the scene data comprises:

performing a first ranking on the target questions according to the question preferences;

3. The method of claim 2, wherein the user question bank comprises a plurality of user question sub-banks, the user question sub-banks having a correspondence with product categories, and user questions in the user question sub-banks having a correspondence with page identifications;

and taking the user problem obtained by screening as the target problem.

4. The method of claim 2, wherein first ranking the target questions according to the question preferences comprises:

5. The method of claim 4, wherein before the target question that is sorted to completion is taken as the suspected feedback question, the method further comprises:

6. The method according to any of claims 2 to 5, wherein the user questions contained in the user question bank are obtained from a manual search and/or user feedback.

7. A problem feedback method, comprising:

returning the question data to the user according to the sequence of the question data; the ordering of the question data is related to the question preference degree of the user, the question preference is determined according to the hierarchical dimension information of the user relative to a target product, and the target product is a product corresponding to the product data; determining the hierarchical dimension information of the user relative to the target product according to the user data; the hierarchical dimension information comprises at least one of the following information;

8. The method of claim 7, wherein the question feedback operation for the first page comprises at least one of:

9. A problem feedback method, comprising:

recommending the question data to the user according to the sequence of the question data; the ordering of the question data is related to the question preference degree of a user, the question preference is determined according to the hierarchical dimension information of the user relative to a target product, and the target product is a product corresponding to the product data; determining the hierarchical dimension information of the user relative to the target product according to the user data; the hierarchical dimension information comprises at least one of the following information;

10. The method of claim 9, wherein recommending the issue data to the user in the order of the issue data comprises:

11. A problem feedback method, comprising:

recommending the question data to the user according to the sequence of the question data; the ordering of the question data is related to the question preference degree of the user, the question preference is determined according to the hierarchical dimension information of the user relative to a target product, and the target product is a product corresponding to the product data; determining the hierarchical dimension information of the user relative to the target product according to the user data; the hierarchical dimension information comprises at least one of the following information;

12. The method of claim 11, wherein the question feedback operation for the first page comprises at least one of:

13. The method of claim 11 or 12, wherein recommending the issue data to the user in the order of the issue data comprises:

14. A problem feedback device, comprising:

the first returning module is used for returning the question data to the user according to the sequence of the question data; the ordering of the question data is related to the question preference degree of a user, the question preference is determined according to the hierarchical dimension information of the user relative to a target product, and the target product is a product corresponding to the product data; determining the hierarchical dimension information of the user relative to the target product according to the user data; the hierarchical dimension information comprises at least one of the following information;

15. The apparatus of claim 14, wherein the first determining module is specifically configured to:

16. A problem feedback device, comprising:

the second returning module is used for returning the question data to the user according to the sequence of the question data; the ordering of the question data is related to the question preference degree of the user, the question preference is determined according to the hierarchical dimension information of the user relative to a target product, and the target product is a product corresponding to the product data; determining the hierarchical dimension information of the user relative to the target product according to the user data; the hierarchical dimension information comprises at least one of the following information;

17. The apparatus of claim 16, wherein the question feedback operation for the first page comprises at least one of:

18. A problem feedback device, comprising:

the first recommending module is used for recommending the question data to the user according to the sequence of the question data; the ordering of the question data is related to the question preference degree of a user, the question preference is determined according to the hierarchical dimension information of the user relative to a target product, and the target product is a product corresponding to the product data; determining the hierarchical dimension information of the user relative to the target product according to the user data; the hierarchical dimension information comprises at least one of the following information;

19. The apparatus of claim 18, wherein the first recommendation module is specifically configured to:

20. A problem feedback device, comprising:

the second recommending module is used for recommending the question data to the user according to the sequence of the question data; the ordering of the question data is related to the question preference degree of the user, the question preference is determined according to the hierarchical dimension information of the user relative to a target product, and the target product is a product corresponding to the product data; determining the hierarchical dimension information of the user relative to the target product according to the user data; the hierarchical dimension information comprises at least one of the following information;

21. The apparatus of claim 20, wherein the question feedback operation for the first page comprises at least one of:

22. The apparatus according to claim 20 or 21, wherein the second recommendation module is specifically configured to:

23. A problem feedback device, comprising:

a processor; and

24. A problem feedback device, comprising:

a processor; and

25. A problem feedback device, comprising:

a processor; and

26. A problem feedback device, comprising:

a processor; and

27. A storage medium storing computer-executable instructions that, when executed, implement the following:

28. A storage medium storing computer-executable instructions that, when executed, implement the following:

29. A storage medium storing computer-executable instructions that, when executed, implement the following:

30. A storage medium storing computer-executable instructions that, when executed, implement the following: