CN105809488B - Information processing method and electronic equipment - Google Patents
Information processing method and electronic equipment Download PDFInfo
- Publication number
- CN105809488B CN105809488B CN201610189058.4A CN201610189058A CN105809488B CN 105809488 B CN105809488 B CN 105809488B CN 201610189058 A CN201610189058 A CN 201610189058A CN 105809488 B CN105809488 B CN 105809488B
- Authority
- CN
- China
- Prior art keywords
- information
- user
- user data
- evaluation information
- emotional
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/205—Parsing
Abstract
The invention provides an information processing method and electronic equipment, comprising the following steps: acquiring evaluation information related to the entity store through a network platform; collecting user data related to the brick and mortar store; and generating an evaluation result of the entity store based on the evaluation information and the user data. The method is used for solving the technical problem that the evaluation of the entity store is not objective in the prior art, and achieves the technical effect of obtaining more objective evaluation of the entity store. The method provided by the application is used for solving the technical problem that the evaluation of the entity store is not objective enough in the prior art.
Description
Technical Field
The present invention relates to the field of information processing, and in particular, to an information processing method and an electronic device.
Background
With the development of electronic commerce, online stores increasingly occupy the market of offline physical stores, but some commodity transactions, especially products needing to experience consumption in time, still need to be performed by the offline physical stores.
In order to increase the user experience effect of the brick-and-mortar store, the off-line brick-and-mortar stores in various fields need to create comfortable hardware environment and software environment so as to improve the user experience effect of the storefront. The evaluation of the user on the entity store is the most direct embodiment of the user experience effect. However, in the prior art, the evaluation of the brick and mortar stores is mainly performed by manpower; on one hand, a large amount of manpower and material resources can be wasted, on the other hand, the evaluations are relatively subjective and have hysteresis, and the experience effect of the user in the physical store cannot be objectively and timely reflected.
Therefore, the problems to be solved by the scheme are as follows: if a more objective mode is adopted, the user experience is evaluated in time, and necessary data support is provided for improving the competitiveness of the physical store.
Disclosure of Invention
The embodiment of the invention provides an information processing method and electronic equipment, and aims to solve the technical problem that in the prior art, evaluation of a physical store is not objective enough.
A first aspect of the present application provides an information processing method, including:
acquiring evaluation information related to the entity store through a network platform;
collecting user data related to the brick and mortar store;
and generating an evaluation result of the entity store based on the evaluation information and the user data.
Optionally, the collecting user data related to the brick and mortar store includes:
acquiring video information of a user through an image acquisition module of the physical store; and/or
And acquiring input operation of a user and input information corresponding to the input operation through the terminal equipment of the physical store.
Optionally, generating an evaluation result of the brick-and-mortar store based on the evaluation information and the user data, includes:
acquiring first credibility of the evaluation information; generating an evaluation information score value based on the evaluation information and the first reliability;
acquiring a second credibility of the user data; generating a user data score value based on the user data and the second credibility;
and generating the physical store score value as an evaluation result of the physical store according to a first predetermined algorithm based on the evaluation information score value and the user data score value.
Optionally, generating an evaluation information score value based on the evaluation information and the first reliability includes:
generating user emotional tendency information used for representing the emotional tendency of the user to the entity store and a user emotional intensity value used for representing the intensity of the emotional tendency of the user to the entity store according to the evaluation information; wherein the emotional tendency comprises a positive tendency and a negative tendency;
and generating the evaluation information score value according to a second preset algorithm based on the user emotional tendency information, the user emotional intensity value and the first credibility.
Optionally, obtaining the first reliability of the evaluation information includes:
generating first user emotional tendency information and first emotional tendency strength corresponding to the user data; generating second user emotional tendency information and second emotional tendency intensity corresponding to the evaluation information;
performing reliability analysis on the evaluation information based on the first user emotional tendency information, the first emotional tendency strength, the second user emotional tendency information and the second emotional tendency strength to obtain the first reliability;
wherein the first confidence level is negatively correlated with an emotional intensity difference, the emotional intensity difference being a difference between the first emotional tendency intensity and the second emotional tendency intensity.
A second aspect of the embodiments of the present application provides an electronic device, including:
a housing;
the processor is arranged in the shell and used for acquiring evaluation information related to the physical store through a network platform; collecting user data related to the brick and mortar store; and generating an evaluation result of the entity store based on the evaluation information and the user data.
Optionally, the processor is configured to:
acquiring video information of a user through an image acquisition module of the physical store; and/or
And acquiring input operation of a user and input information corresponding to the input operation through the terminal equipment of the physical store.
Optionally, the processor is configured to:
acquiring first credibility of the evaluation information; generating an evaluation information score value based on the evaluation information and the first reliability;
acquiring a second credibility of the user data; generating a user data score value based on the user data and the second credibility;
and generating the physical store score value as an evaluation result of the physical store according to a first predetermined algorithm based on the evaluation information score value and the user data score value.
Optionally, the processor is configured to:
generating user emotional tendency information used for representing the emotional tendency of the user to the entity store and a user emotional intensity value used for representing the intensity of the emotional tendency of the user to the entity store according to the evaluation information; wherein the emotional tendency comprises a positive tendency and a negative tendency;
and generating the evaluation information score value according to a second preset algorithm based on the user emotional tendency information, the user emotional intensity value and the first credibility.
Optionally, the processor is configured to:
generating first user emotional tendency information and first emotional tendency strength corresponding to the user data; generating second user emotional tendency information and second emotional tendency intensity corresponding to the evaluation information;
performing reliability analysis on the evaluation information based on the first user emotional tendency information, the first emotional tendency strength, the second user emotional tendency information and the second emotional tendency strength to obtain the first reliability;
wherein the first confidence level is negatively correlated with an emotional intensity difference, the emotional intensity difference being a difference between the first emotional tendency intensity and the second emotional tendency intensity.
A third aspect of the embodiments of the present application provides an electronic device, including:
the system comprises an acquisition unit, a storage unit and a management unit, wherein the acquisition unit is used for acquiring evaluation information related to a physical store through a network platform;
the acquisition unit is used for acquiring user data related to the physical store;
a generating unit configured to generate an evaluation result of the brick-and-mortar store based on the evaluation information and the user data.
One or more technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages:
1. according to the scheme of the embodiment of the application, the evaluation information related to the entity store is obtained through the network platform; collecting user data related to the brick and mortar store; then, an evaluation result of the brick-and-mortar store is generated based on the evaluation information and the user data. The evaluation information of the entity store and the user data collected by the entity store, which are acquired by the network platform, are used for comprehensively generating the evaluation result of the entity store, so that the condition that the evaluation of the user is collected by manpower is avoided, the technical problem that the evaluation of the entity store is not objective in the prior art is solved, and the technical effect of acquiring more objective evaluation of the entity store is realized.
2. In the scheme of the embodiment of the application, the collecting user data related to the physical store comprises: acquiring video information of a user through an image acquisition module of the physical store; and/or acquiring input operation of a user and input information corresponding to the input operation through terminal equipment of the physical store. Video information is directly acquired through an image acquisition module, input operation and input information corresponding to the input operation are directly acquired through terminal equipment, and therefore in the scheme of the embodiment of the application, subjective factors in an evaluation process are avoided when user data related to the entity store is acquired, and evaluation of the entity store is more objective.
3. In the scheme of the embodiment of the application, for evaluation information acquired through a network platform, when the reliability of the evaluation information is analyzed, reliability analysis is performed on the evaluation information through user data acquired by a physical store, the first reliability of the evaluation information is negatively correlated with the emotional intensity difference, and the emotional intensity difference is the difference between the emotional tendency intensity of the user data and the emotional tendency intensity of the evaluation information, namely, the closer the emotional tendency intensity of the user data and the emotional tendency intensity of the evaluation information is, the higher the first reliability of the evaluation information is; otherwise, the opposite is true. Because the user data collected by the entity store is real data, the influence of false network evaluation information on an evaluation result can be reduced by analyzing the credibility of the evaluation information through the user data, and the evaluation of the entity store is more objective.
4. In the scheme of the embodiment of the application, the data for evaluating the entity store is from the website and the user data collected in the entity store, the data volume is large, the reliability of the evaluation information is evaluated through the user data collected in the entity store, a closed-loop mechanism is realized, and the effectiveness of the evaluation system is improved.
Drawings
FIG. 1 is a flow chart of an information processing method in an embodiment of the present application;
fig. 2 is a flowchart of a method for performing reliability analysis on evaluation information in an embodiment of the present application;
FIG. 3 is a flowchart illustrating a method for implementing step 103 in this embodiment of the present application;
fig. 4 is a schematic hardware structure diagram of an electronic device in an embodiment of the present application;
fig. 5 is a schematic diagram of functional modules of an electronic device in an embodiment of the present application.
Detailed Description
The embodiment of the invention provides an information processing method and electronic equipment, and aims to solve the technical problem that in the prior art, evaluation of a physical store is not objective enough.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. 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 invention.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
Please refer to fig. 1, which is a flowchart illustrating an information processing method according to an embodiment of the present disclosure. The method comprises the following steps:
step 101: and acquiring evaluation information related to the physical store through the network platform.
Step 102: collecting user data related to the brick and mortar store.
Step 103: and generating an evaluation result of the entity store based on the evaluation information and the user data.
In step 101, the evaluation information related to the physical store is obtained through the network platform and is the evaluation information published by the user on the network platform, and the evaluation information includes text information, picture information, audio information, video information, and the like. Specifically, the information related to the brick-and-mortar store can be crawled from the public internet through a web crawler and stored in the external information set c (external) of the brick-and-mortar store.
Specifically, the web crawler may retrieve web page information from specific websites, or may search for related web page information by using a search engine with a brick and mortar store or products in the brick and mortar store as keywords. Then, the page of the web page and the evaluation (including text information, image information, audio information, video information, or the like) are downloaded and stored in c (external). In practical application, UGC (User generated content) and User information (including account number, mailbox address, etc.) corresponding to the webpage information can be recorded.
Step 102: collecting user data related to the brick and mortar store.
The user data related to the physical store may be audio information, video information, image information, input information, sensing information, and the like. Specifically, the monitoring module in the brick and mortar store can be used, such as: the system comprises camera equipment, audio equipment, sensor equipment and data obtained by software which is installed in other terminal equipment and can automatically collect interaction events between a user and the terminal equipment, and the data are stored in an internal information set C (internal) of the physical store.
Specifically, the video information comprises security records of the physical store, records of the physical store doorway for counting passenger flow and records of a POS (point of sale) machine for collecting user face features; the audio information may be audio information of a user's conversation extracted through the recording apparatus; the sensing information may be environmental information related to temperature, humidity, and noise; the input operation may be an operation performed by a user on the terminal device; the input information may be log information collected by the terminal device and generated when the user performs an interaction event with the terminal device when the user performs an input operation on the terminal device.
In the embodiment of the application, for the evaluation information acquired through the network platform, the computer can automatically analyze and extract the evaluation attribute and the evaluation content based on the semantic information extraction technology, extract the part related to the experience of the entity store, and then automatically analyze the emotional tendency and the tendency strength of the evaluation according to the emotional analysis; the computer may also record time information for each piece of rating information, as well as the trustworthiness of the source of the rating information (the trustworthiness of the website where the rating information is located).
In the computer, the evaluation attributes corresponding to different evaluation contents may be stored in advance, for example: the emotional tendency corresponding to the disappointment content is evaluated to be negative, and the tendency intensity is high; the emotional tendency corresponding to the evaluation content of 'good still' is 'forward', and the tendency intensity is moderate; the emotional tendency corresponding to the evaluation content "very satisfactory" is positive, and the tendency intensity is high. In practical application, the corresponding evaluation content, and the emotional tendency and the tendency strength corresponding to the evaluation content can be set according to the characteristics of the physical store and the product.
For example, if the brick-and-mortar store in the web page is "convenience store", the evaluation target is "shopping experience", and the specific content of the evaluation is "disappointing," the emotional tendency of the evaluation content is negative, and the tendency strength is high.
For the user data, the emotional tendency of the user can be analyzed from the aspects of vision, hearing, behavior and the like by means of computer machine learning. For example, video information is analyzed, and the video analysis result is: the user has relaxed posture and a smile face, so that the user can be considered to have better experience in a physical store, the emotional tendency of the user is positive, and the tendency strength is high; another example is: analyzing the audio information, wherein the audio analysis result shows that the speech speed of the user is normal and the voice is soft, and then determining that the emotional tendency of the user is in the positive direction and the tendency intensity; for another example: analyzing the log information on the terminal equipment, wherein the behavior analysis result is as follows: and the user continuously tries to input new content, so that the emotional tendency of the user can be determined to be a forward direction, and the tendency strength is high.
Next, the reliability of the evaluation information and the user data will be described. Since the user data collected by the brick-and-mortar store is real data, the credibility of the user data can be set to be higher credibility.
For the evaluation information, the reliability of the evaluation information may be the reliability of the website; the reliability of the evaluation information may also be a reliability value obtained by performing reliability analysis on the evaluation information through user data collected by a brick and mortar store, as shown in fig. 2, a specific analysis method is as follows:
step 201: and generating first user emotional tendency information and first emotional tendency strength corresponding to the user data.
Step 202: and generating second user emotional tendency information and second emotional tendency strength corresponding to the evaluation information.
The method for generating the emotional tendency information and the emotional tendency strength of the user is already described in the above embodiments, and is not described herein again.
Step 203: and carrying out reliability analysis on the evaluation information based on the first user emotional tendency information, the first emotional tendency strength, the second user emotional tendency information and the second emotional tendency strength to obtain the first reliability.
Wherein the first confidence level is negatively correlated with an emotional intensity difference, the emotional intensity difference being a difference between the first emotional tendency intensity and the second emotional tendency intensity. That is, the closer the emotional tendency intensity of the user data is to the emotional tendency intensity of the evaluation information, the smaller the emotional intensity difference is, and the higher the first reliability of the evaluation information is; conversely, the greater the difference between the emotional tendency intensity of the user data and the emotional tendency intensity of the evaluation information, the lower the first reliability of the evaluation information.
In the embodiment of the application, before evaluating the evaluation information through the user data, the computer needs to establish a data model u of the user.
Specifically, the computer extracts visual features based on video information or image information according to the internal information set C (internal) of the physical store, judges the gender and age of the user, and records the visual features (including residence time, posture, actions and the like) of the user in the physical store into a user data model corresponding to the user. For audio information, audio features belonging to different people in the audio are extracted by means of audio splitting, the gender and the age of a user are identified, and meanwhile, the audio features (including emotion, emotion and speech speed) of the user in a physical store are recorded into a user data model corresponding to the user. Judging behavior characteristics (interval time between two inputs, input content, speed mode of a keyboard and a mouse) of different users in interactive event information detected by software installed in terminal equipment, and recording time information for triggering related interactive events;
for the environment information detected by the sensing device, recording the time information of the user existence, such as: the binary switch sensor records the time information of the user; and summarizing and mapping the user data through the time continuity to establish a user model u. The user model u is { feature, profile, relationship, presence }, wherein the feature refers to feature fragments of the user and includes text features, video features or audio features; profile records the representation of the user, such as: age, gender, hobbies, etc.; the relationship is the business relationship between the user and the physical store, such as: the user is a frequent visitor (actually, the user comes to the physical store more frequently), or the user comes to the physical store for the first time, and the like; the sentiment is a list of user experience sentiment records, including experience objects, sentiment tendency, sentiment strength and time interval.
Analyzing and extracting the internal information set C (internal) of the entity store, and finally establishing a plurality of independent users u ═ feature, profile, relationship, and presence.
Next, reliability analysis of the evaluation information will be described.
Specifically, by means of the association between time and place, the computer can perform credibility analysis on the evaluation information published on the network platform by the user. For example: the computer discovers that the user A publishes and evaluates that the user A goes to the Shanghai Changning district at 3 pm on a certain website through the network platform, experiences a new product B and feels disappointed. "the emotional tendency and the tendency intensity of the time information, the position information, and the evaluation information of the user a going to the brick-and-mortar store can be obtained from the evaluation information issued by the user a. Then, extracting user data related to the user A from the user model u, if the data related to the user A exists in the user model, including visual features, audio features or behavior features, and generating emotional tendency and tendency strength corresponding to the data of the user A. Furthermore, reliability analysis is carried out on the evaluation information published on the website by the user A according to the emotional tendency and the tendency strength of the evaluation information of the user A and the emotional tendency and the tendency strength of the user data of the user A. For example: the emotional tendency of the evaluation information published on the website by the user A is negative, the tendency intensity is strong, the emotional tendency of the user data is positive, and the tendency intensity is strong, so that the reliability of the evaluation information published on the network by the user A is determined to be low when reliability analysis is performed on the evaluation information published on the website by the user A due to the fact that the emotional tendency of the evaluation information is different from the emotional tendency of the user data.
In the embodiment of the application, when the user data related to the user a is extracted from the user model u, the association can be performed through the time information and the location information, and further, the association can be performed through the user information (including a user name, a mailbox address and the like), so that the accuracy of the user association is improved. In practical applications, the association may be performed through other features, which are not limited in this application.
Next, step 103 will be explained, and as shown in fig. 3, step 103 includes the following:
step 1031: acquiring first credibility of the evaluation information; and generating an evaluation information score value based on the evaluation information and the first reliability.
Step 1032: acquiring a second credibility of the user data; and generating a user data score value based on the user data and the second confidence level.
Specifically, the calculation formula is as follows:
Score(ex)＝1-Sum(Strength*confidential|Neg)/Sum(Strength*confidential(Pos+Neg))
Score(in)＝1-Sum(Strength*confidential|Neg)/Sum(Strength*Confidential|(Pos+Neg))
score (ex) is a score value corresponding to an external information set c (external) of the physical store, Strength is tendency Strength, confidential is reliability of evaluation information, Neg is negative evaluation information, Pos is positive evaluation information, numerator Sum (Strength confidential | Neg) is a product of Strength and reliability of all negative evaluation information in c (external) (reliability can be reliability of corresponding website, or reliability obtained by analyzing evaluation information through user data) and sums up, and denominator Sum (Strength confidential (Pos + Neg)) is a product of Strength and reliability of all positive and negative evaluation information and sums up.
Score (in) is a score value corresponding to the internal information set c (external) of the physical store, Strength is tendency intensity, Confidential is reliability of user data, Neg is negative evaluation information, Pos is positive evaluation information, numerator Sum (Strength Confidential | Neg) is obtained by multiplying and summing the intensity and reliability (preset reliability) of all negative evaluation information in c (external), and denominator Sum (Strength Confidential) is obtained by multiplying and summing the intensity and reliability scores of all positive and negative evaluation information.
In practical applications, the credibility confidence of the evaluation information in the corresponding formula score (ex) may be replaced by Authority of the information source (website), and the corresponding formula for calculating the score (ex) is score (ex) 1-Sum (strenggth automation | Neg)/Sum (strenggth automation | (Pos + Neg)); wherein Authority is Authority of the website.
Step 1033: and generating the physical store score value as an evaluation result of the physical store according to a first predetermined algorithm based on the evaluation information score value and the user data score value.
Specifically, the first algorithm may be a weighted average algorithm, and the formula is as follows:
Score＝w1*score(ex)+w2*score(in)
wherein Score is the evaluation result, w1 is the weight occupied by Score (ex), w2 is the weight occupied by Score (in), for example: it is possible to set w 1-0.3 and w 2-0.7. In practical applications, the occupied weight of score (ex) and score (in) can be set according to the condition of the physical store. Of course, other algorithms may be used besides the weighted average algorithm, and the present application is not limited thereto.
In step 103, the computer may set an interval time for generating the evaluation result. For example: the interval time can be one week or one month.
After the evaluation result Score is obtained through the calculation in step 103, the Score may be written into a predefined physical store experience evaluation model m ═ { store, usexp }, where the store corresponds to the physical store and includes physical store information such as the name of the physical store and the location of the physical store; usrexp records the evaluation results Score of the storefront experience and the time scale of the corresponding Score.
Based on the same inventive concept, an embodiment of the present application further provides an electronic device, as shown in fig. 4, including:
a housing 41;
a processor 42 disposed in the housing 41, wherein the processor 42 is configured to obtain evaluation information related to the brick-and-mortar store through a network platform; collecting user data related to the brick and mortar store; and generating an evaluation result of the entity store based on the evaluation information and the user data.
Optionally, the processor 42 is configured to:
acquiring video information of a user through an image acquisition module of the physical store; and/or
And acquiring input operation of a user and input information corresponding to the input operation through the terminal equipment of the physical store.
Optionally, the processor 42 is configured to:
acquiring first credibility of the evaluation information; generating an evaluation information score value based on the evaluation information and the first reliability;
acquiring a second credibility of the user data; generating a user data score value based on the user data and the second credibility;
and generating the physical store score value as an evaluation result of the physical store according to a first predetermined algorithm based on the evaluation information score value and the user data score value.
Optionally, the processor 42 is configured to:
generating user emotional tendency information used for representing the emotional tendency of the user to the entity store and a user emotional intensity value used for representing the intensity of the emotional tendency of the user to the entity store according to the evaluation information; wherein the emotional tendency comprises a positive tendency and a negative tendency;
and generating the evaluation information score value according to a second preset algorithm based on the user emotional tendency information, the user emotional intensity value and the first credibility.
Optionally, the processor 42 is configured to:
generating first user emotional tendency information and first emotional tendency strength corresponding to the user data; generating second user emotional tendency information and second emotional tendency intensity corresponding to the evaluation information;
performing reliability analysis on the evaluation information based on the first user emotional tendency information, the first emotional tendency strength, the second user emotional tendency information and the second emotional tendency strength to obtain the first reliability;
wherein the first confidence level is negatively correlated with an emotional intensity difference, the emotional intensity difference being a difference between the first emotional tendency intensity and the second emotional tendency intensity.
Based on the same inventive concept, an embodiment of the present application further provides an electronic device, as shown in fig. 5, including:
an acquisition unit 50 configured to acquire evaluation information related to the physical store via the network platform;
an acquisition unit 51, configured to acquire user data related to the brick-and-mortar store;
a generating unit 52, configured to generate an evaluation result of the brick-and-mortar store based on the evaluation information and the user data.
Optionally, the collecting unit 51 is configured to:
acquiring video information of a user through an image acquisition module of the physical store; and/or
And acquiring input operation of a user and input information corresponding to the input operation through the terminal equipment of the physical store.
Optionally, the generating unit 52 is configured to:
acquiring first credibility of the evaluation information; generating an evaluation information score value based on the evaluation information and the first reliability;
acquiring a second credibility of the user data; generating a user data score value based on the user data and the second credibility;
and generating the physical store score value as an evaluation result of the physical store according to a first predetermined algorithm based on the evaluation information score value and the user data score value.
Optionally, the generating unit 52 is configured to:
generating user emotional tendency information used for representing the emotional tendency of the user to the entity store and a user emotional intensity value used for representing the intensity of the emotional tendency of the user to the entity store according to the evaluation information; wherein the emotional tendency comprises a positive tendency and a negative tendency;
and generating the evaluation information score value according to a second preset algorithm based on the user emotional tendency information, the user emotional intensity value and the first credibility.
Optionally, the generating unit 52 is configured to:
generating first user emotional tendency information and first emotional tendency strength corresponding to the user data; generating second user emotional tendency information and second emotional tendency intensity corresponding to the evaluation information;
performing reliability analysis on the evaluation information based on the first user emotional tendency information, the first emotional tendency strength, the second user emotional tendency information and the second emotional tendency strength to obtain the first reliability;
wherein the first confidence level is negatively correlated with an emotional intensity difference, the emotional intensity difference being a difference between the first emotional tendency intensity and the second emotional tendency intensity.
One or more technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages:
1. according to the scheme of the embodiment of the application, the evaluation information related to the entity store is obtained through the network platform; collecting user data related to the brick and mortar store; then, an evaluation result of the brick-and-mortar store is generated based on the evaluation information and the user data. The evaluation information of the entity store and the user data collected by the entity store, which are acquired by the network platform, are used for comprehensively generating the evaluation result of the entity store, so that the condition that the evaluation of the user is collected by manpower is avoided, the technical problem that the evaluation of the entity store is not objective in the prior art is solved, and the technical effect of acquiring more objective evaluation of the entity store is realized.
2. In the scheme of the embodiment of the application, the collecting user data related to the physical store comprises: acquiring video information of a user through an image acquisition module of the physical store; and/or acquiring input operation of a user and input information corresponding to the input operation through terminal equipment of the physical store. Video information is directly acquired through an image acquisition module, input operation and input information corresponding to the input operation are directly acquired through terminal equipment, and therefore in the scheme of the embodiment of the application, subjective factors in an evaluation process are avoided when user data related to the entity store is acquired, and evaluation of the entity store is more objective.
3. In the scheme of the embodiment of the application, for evaluation information acquired through a network platform, when the reliability of the evaluation information is analyzed, reliability analysis is performed on the evaluation information through user data acquired by a physical store, the first reliability of the evaluation information is negatively correlated with the emotional intensity difference, and the emotional intensity difference is the difference between the emotional tendency intensity of the user data and the emotional tendency intensity of the evaluation information, namely, the closer the emotional tendency intensity of the user data and the emotional tendency intensity of the evaluation information is, the higher the first reliability of the evaluation information is; otherwise, the opposite is true. Because the user data collected by the entity store is real data, the influence of false network evaluation information on an evaluation result can be reduced by analyzing the credibility of the evaluation information through the user data, and the evaluation of the entity store is more objective.
4. In the scheme of the embodiment of the application, the data for evaluating the entity store is from the website and the user data collected in the entity store, the data volume is large, the reliability of the evaluation information is evaluated through the user data collected in the entity store, a closed-loop mechanism is realized, and the effectiveness of the evaluation system is improved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention 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 invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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.
Specifically, the computer program instructions corresponding to the information processing method in the embodiment of the present application may be stored on a storage medium such as an optical disc, a hard disc, a usb disk, or the like, and when the computer program instructions corresponding to the information processing method in the storage medium are read or executed by an electronic device, the method includes the following steps:
acquiring evaluation information related to the entity store through a network platform;
collecting user data related to the brick and mortar store;
and generating an evaluation result of the entity store based on the evaluation information and the user data.
Optionally, the step of storing in the storage medium: collecting user data related to the physical store, wherein the corresponding computer instructions specifically comprise the following steps in the specific executed process:
acquiring video information of a user through an image acquisition module of the physical store; and/or
And acquiring input operation of a user and input information corresponding to the input operation through the terminal equipment of the physical store.
Optionally, the step of storing in the storage medium: generating an evaluation result of the brick-and-mortar store based on the evaluation information and the user data, wherein the corresponding computer instructions specifically comprise the following steps in the specific executed process:
acquiring first credibility of the evaluation information; generating an evaluation information score value based on the evaluation information and the first reliability;
acquiring a second credibility of the user data; generating a user data score value based on the user data and the second credibility;
and generating the physical store score value as an evaluation result of the physical store according to a first predetermined algorithm based on the evaluation information score value and the user data score value.
Optionally, the step of storing in the storage medium: generating an evaluation information score value based on the evaluation information and the first reliability, wherein the corresponding computer instructions specifically comprise the following steps in the specific executed process:
generating user emotional tendency information used for representing the emotional tendency of the user to the entity store and a user emotional intensity value used for representing the intensity of the emotional tendency of the user to the entity store according to the evaluation information; wherein the emotional tendency comprises a positive tendency and a negative tendency;
and generating the evaluation information score value according to a second preset algorithm based on the user emotional tendency information, the user emotional intensity value and the first credibility.
Optionally, the step of storing in the storage medium: acquiring a first reliability of the evaluation information, wherein the corresponding computer instruction specifically comprises the following steps in the specific executed process:
generating first user emotional tendency information and first emotional tendency strength corresponding to the user data; generating second user emotional tendency information and second emotional tendency intensity corresponding to the evaluation information;
performing reliability analysis on the evaluation information based on the first user emotional tendency information, the first emotional tendency strength, the second user emotional tendency information and the second emotional tendency strength to obtain the first reliability;
wherein the first confidence level is negatively correlated with an emotional intensity difference, the emotional intensity difference being a difference between the first emotional tendency intensity and the second emotional tendency intensity.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (7)
1. An information processing method comprising:
acquiring evaluation information related to the entity store through a network platform;
collecting user data related to the brick and mortar store; the user data is used for carrying out reliability analysis on the evaluation information; before reliability analysis is carried out on the evaluation information through the user data, a user model is established through analyzing and extracting an internal information set of the physical store; the user data is the user data related to the user publishing the evaluation information on the network platform, which is extracted from the user model based on the association of the time information and the place information;
generating an evaluation result of the brick-and-mortar store based on the evaluation information and the user data;
wherein the generating an evaluation result of the brick-and-mortar store based on the evaluation information and the user data comprises:
acquiring first credibility of the evaluation information; generating an evaluation information score value based on the evaluation information and the first reliability; the first reliability of the evaluation information is negatively correlated with the emotional intensity difference, and the emotional intensity difference is the difference between the emotional tendency intensity of the user data and the emotional tendency intensity of the evaluation information;
acquiring a second credibility of the user data; generating a user data score value based on the user data and the second credibility;
generating the physical store score value as an evaluation result of the physical store according to a first predetermined algorithm based on the evaluation information score value and the user data score value;
wherein collecting user data related to the brick and mortar store comprises at least one of:
acquiring video information or image information of a user through an image acquisition module of the physical store;
acquiring and obtaining audio information of a user through audio equipment in the physical store;
acquiring sensing information of a user through sensor equipment in the physical store;
and acquiring input operation of a user and input information corresponding to the input operation through the terminal equipment of the physical store.
2. The method of claim 1, wherein generating a rating information score value based on the rating information and the first confidence level comprises:
generating user emotional tendency information used for representing the emotional tendency of the user to the entity store and a user emotional intensity value used for representing the intensity of the emotional tendency of the user to the entity store according to the evaluation information; wherein the emotional tendency comprises a positive tendency and a negative tendency;
and generating the evaluation information score value according to a second preset algorithm based on the user emotional tendency information, the user emotional intensity value and the first credibility.
3. The method of claim 1, wherein obtaining a first degree of confidence in the evaluation information comprises:
generating first user emotional tendency information and first emotional tendency strength corresponding to the user data; generating second user emotional tendency information and second emotional tendency intensity corresponding to the evaluation information;
performing reliability analysis on the evaluation information based on the first user emotional tendency information, the first emotional tendency strength, the second user emotional tendency information and the second emotional tendency strength to obtain the first reliability;
wherein the first confidence level is negatively correlated with an emotional intensity difference, the emotional intensity difference being a difference between the first emotional tendency intensity and the second emotional tendency intensity.
4. An electronic device, comprising:
a housing;
the processor is arranged in the shell and used for acquiring evaluation information related to the physical store through a network platform; collecting user data related to the brick and mortar store; generating an evaluation result of the brick-and-mortar store based on the evaluation information and the user data; the user data is used for carrying out reliability analysis on the evaluation information; before reliability analysis is carried out on the evaluation information through the user data, a user model is established through analyzing and extracting an internal information set of the physical store; the user data is the user data related to the user publishing the evaluation information on the network platform, which is extracted from the user model based on the association of the time information and the place information;
the processor is configured to:
acquiring first credibility of the evaluation information; generating an evaluation information score value based on the evaluation information and the first reliability; the first reliability of the evaluation information is negatively correlated with the emotional intensity difference, and the emotional intensity difference is the difference between the emotional tendency intensity of the user data and the emotional tendency intensity of the evaluation information;
acquiring a second credibility of the user data; generating a user data score value based on the user data and the second credibility;
generating the physical store score value as an evaluation result of the physical store according to a first predetermined algorithm based on the evaluation information score value and the user data score value;
wherein the processor is configured to:
acquiring video information or image information of a user through an image acquisition module of the physical store; and/or
Acquiring input operation of a user and input information corresponding to the input operation through terminal equipment of the physical store; and/or
Acquiring and obtaining audio information of a user through audio equipment in the physical store; and/or
And acquiring the sensing information of the user through the sensor equipment in the physical store.
5. The electronic device of claim 4, wherein the processor is to:
generating user emotional tendency information used for representing the emotional tendency of the user to the entity store and a user emotional intensity value used for representing the intensity of the emotional tendency of the user to the entity store according to the evaluation information; wherein the emotional tendency comprises a positive tendency and a negative tendency;
and generating the evaluation information score value according to a second preset algorithm based on the user emotional tendency information, the user emotional intensity value and the first credibility.
6. The electronic device of claim 4, wherein the processor is to:
generating first user emotional tendency information and first emotional tendency strength corresponding to the user data; generating second user emotional tendency information and second emotional tendency intensity corresponding to the evaluation information;
performing reliability analysis on the evaluation information based on the first user emotional tendency information, the first emotional tendency strength, the second user emotional tendency information and the second emotional tendency strength to obtain the first reliability;
wherein the first confidence level is negatively correlated with an emotional intensity difference, the emotional intensity difference being a difference between the first emotional tendency intensity and the second emotional tendency intensity.
7. An electronic device, comprising:
the system comprises an acquisition unit, a storage unit and a management unit, wherein the acquisition unit is used for acquiring evaluation information related to a physical store through a network platform;
the acquisition unit is used for acquiring user data related to the physical store; the user data is used for carrying out reliability analysis on the evaluation information; before reliability analysis is carried out on the evaluation information through the user data, a user model is established through analyzing and extracting an internal information set of the physical store; the user data is the user data related to the user publishing the evaluation information on the network platform, which is extracted from the user model based on the association of the time information and the place information;
a generation unit configured to generate an evaluation result of the brick-and-mortar store based on the evaluation information and the user data;
wherein the generating an evaluation result of the brick-and-mortar store based on the evaluation information and the user data comprises:
acquiring first credibility of the evaluation information; generating an evaluation information score value based on the evaluation information and the first reliability; the first reliability of the evaluation information is negatively correlated with the emotional intensity difference, and the emotional intensity difference is the difference between the emotional tendency intensity of the user data and the emotional tendency intensity of the evaluation information;
acquiring a second credibility of the user data; generating a user data score value based on the user data and the second credibility;
generating the physical store score value as an evaluation result of the physical store according to a first predetermined algorithm based on the evaluation information score value and the user data score value;
wherein collecting user data related to the brick and mortar store comprises at least one of:
acquiring video information or image information of a user through an image acquisition module of the physical store;
acquiring and obtaining audio information of a user through audio equipment in the physical store;
acquiring sensing information of a user through sensor equipment in the physical store;
and acquiring input operation of a user and input information corresponding to the input operation through the terminal equipment of the physical store.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610189058.4A CN105809488B (en) | 2016-03-29 | 2016-03-29 | Information processing method and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610189058.4A CN105809488B (en) | 2016-03-29 | 2016-03-29 | Information processing method and electronic equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105809488A CN105809488A (en) | 2016-07-27 |
| CN105809488B true CN105809488B (en) | 2020-10-27 |
Family
ID=56455048
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610189058.4A Active CN105809488B (en) | 2016-03-29 | 2016-03-29 | Information processing method and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105809488B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106408402A (en) * | 2016-11-22 | 2017-02-15 | 中青冠岳科技(北京)有限公司 | Method and system for merchant evaluation based on electronic transaction |
| CN108549641B (en) * | 2018-04-26 | 2022-09-20 | 中国联合网络通信集团有限公司 | Song evaluation method, device, equipment and storage medium |
| CN110413778A (en) * | 2019-07-10 | 2019-11-05 | 深圳传世智慧科技有限公司 | Generation method, expert recommendation method and the electronic equipment of expert power |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7523051B2 (en) * | 2001-05-31 | 2009-04-21 | Sony Corporation | Information processing apparatus, information processing method, and program |
| CN101110110A (en) * | 2006-07-18 | 2008-01-23 | 吴鑫 | Restaurant chain store management system |
| US8370818B2 (en) * | 2006-12-02 | 2013-02-05 | Time Warner Cable Inc. | Methods and apparatus for analyzing software interface usage |
| CN101894347A (en) * | 2010-07-15 | 2010-11-24 | 俞波 | Shopping guide scoring system and method |
-
2016
- 2016-03-29 CN CN201610189058.4A patent/CN105809488B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN105809488A (en) | 2016-07-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10950060B2 (en) | Identifying augmented reality visuals influencing user behavior in virtual-commerce environments | |
| WO2019091177A1 (en) | Risk identification model building method, apparatus and device and risk identification method, apparatus and device | |
| JP6681342B2 (en) | Behavioral event measurement system and related method | |
| CN110020411B (en) | Image-text content generation method and equipment | |
| JP6662119B2 (en) | Method, program, and server device for generating caption of social media post | |
| US11275748B2 (en) | Influence score of a social media domain | |
| US11620474B2 (en) | Model reselection for accommodating unsatisfactory training data | |
| US11915282B2 (en) | Preemptive transaction analysis | |
| CN105809488B (en) | Information processing method and electronic equipment | |
| US10547582B1 (en) | Methods and systems for enhancing viewer engagement with content portions | |
| CN110019837B (en) | User portrait generation method and device, computer equipment and readable medium | |
| CN109711849B (en) | Ether house address portrait generation method and device, electronic equipment and storage medium | |
| CN110674404A (en) | Link information generation method, device, system, storage medium and electronic equipment | |
| US20220189472A1 (en) | Recognition and restructuring of previously presented materials | |
| CN107291686B (en) | Method and system for identifying emotion identification | |
| CN111127057B (en) | Multi-dimensional user portrait recovery method | |
| US10237226B2 (en) | Detection of manipulation of social media content | |
| WO2019242453A1 (en) | Information processing method and device, storage medium, and electronic device | |
| US11853948B2 (en) | Methods and systems for managing risk with respect to potential customers | |
| US11159467B2 (en) | Methods and systems for managing content based on proxy understanding | |
| CN113821596A (en) | Information recommendation method and device, computer equipment and storage medium | |
| US20220261856A1 (en) | Method for generating search results in an advertising widget | |
| Hwang et al. | Usability Problem Identification Based on Explainable Neural Network in Asynchronous Testing Environment | |
| US11928153B2 (en) | Multimedia linked timestamp validation detection | |
| CN116821475B (en) | Video recommendation method and device based on client data and computer equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |