CN117932167A - Intelligent recommendation method and device for merchants - Google Patents
Intelligent recommendation method and device for merchants Download PDFInfo
- Publication number
- CN117932167A CN117932167A CN202311620254.9A CN202311620254A CN117932167A CN 117932167 A CN117932167 A CN 117932167A CN 202311620254 A CN202311620254 A CN 202311620254A CN 117932167 A CN117932167 A CN 117932167A
- Authority
- CN
- China
- Prior art keywords
- user
- merchant
- recommendation
- information
- feedback
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 48
- 230000006399 behavior Effects 0.000 claims abstract description 37
- 230000008451 emotion Effects 0.000 claims abstract description 20
- 238000004458 analytical method Methods 0.000 claims abstract description 19
- 238000010219 correlation analysis Methods 0.000 claims abstract description 13
- 238000013145 classification model Methods 0.000 claims abstract description 8
- 238000012545 processing Methods 0.000 claims description 18
- 238000004590 computer program Methods 0.000 claims description 16
- 238000003860 storage Methods 0.000 claims description 13
- 230000003542 behavioural effect Effects 0.000 claims description 7
- 238000012216 screening Methods 0.000 claims description 6
- 238000005457 optimization Methods 0.000 claims description 4
- 238000005516 engineering process Methods 0.000 abstract description 8
- 230000000694 effects Effects 0.000 abstract description 3
- 230000006870 function Effects 0.000 description 12
- 238000010586 diagram Methods 0.000 description 8
- 230000006872 improvement Effects 0.000 description 8
- 230000008569 process Effects 0.000 description 6
- 238000012098 association analyses Methods 0.000 description 5
- 238000001914 filtration Methods 0.000 description 5
- 230000003993 interaction Effects 0.000 description 4
- 238000011156 evaluation Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000013135 deep learning Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 238000003058 natural language processing Methods 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 230000001413 cellular effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000002996 emotional effect Effects 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 238000011068 loading method Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 230000005055 memory storage Effects 0.000 description 1
- 230000007935 neutral effect Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 229920001296 polysiloxane Polymers 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 239000010979 ruby Substances 0.000 description 1
- 229910001750 ruby Inorganic materials 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses a merchant intelligent recommendation method and device, wherein the method comprises the steps of determining recommended merchants from a stored merchant information base by using a recommendation algorithm based on the position of a user side and sending the recommended merchants to the user side; acquiring feedback information of a user based on recommended merchants, which is sent by a user terminal, and determining emotion polarities corresponding to the feedback information; inputting the feedback information into a topic classification model to determine a corresponding topic in the feedback information; acquiring behavior data of the feedback user, and performing relevance analysis on the theme and the behavior data; generating a feedback suggestion based on the emotion polarity and the correlation analysis result to adjust the recommendation algorithm based on the feedback suggestion. The problem that in the related technology, the existing system is mostly recommended based on historical behaviors of the user or information uploaded by merchants, lacks accurate understanding and application of personalized preferences of the user, and is unsatisfactory in recommendation effect is solved.
Description
Technical Field
The application relates to the technical field of information processing, in particular to a merchant recommendation method and device.
Background
With the popularity of mobile devices and the maturity of location technology, people increasingly rely on mobile phones to obtain information from surrounding merchants to make consumption decisions. However, the existing merchant recommendation system only provides a static merchant list, lacks individuation and real-time performance, and cannot meet the requirements of users. Therefore, there is a need for an intelligent merchant recommendation system based on mobile phone positioning, which can provide accurate, timely and personalized merchant recommendation according to real-time location information and personalized preferences of users.
Disclosure of Invention
The application provides a merchant intelligent recommendation method and device, which are used for solving the problems in the related art.
In a first aspect, the present invention provides a method for intelligent recommendation of a merchant, including determining a recommended merchant from a stored merchant information base by using a recommendation algorithm based on a user location and transmitting the recommended merchant to a user; acquiring feedback information of a user based on recommended merchants, which is sent by a user terminal, and determining emotion polarities corresponding to the feedback information; inputting the feedback information into a topic classification model to determine a corresponding topic in the feedback information; acquiring behavior data of the feedback user, and performing relevance analysis on the theme and the behavior data; generating a feedback suggestion based on the emotion polarity and the correlation analysis result to adjust the recommendation algorithm based on the feedback suggestion.
Optionally, determining the recommended merchant from the stored merchant information base by using the recommendation algorithm based on the user side location and transmitting the recommended merchant to the user side comprises: after receiving a short message sent by a user side, acquiring real-time position information of the user side; acquiring historical data of a corresponding user based on the user side; generating merchant recommendation information by using a recommendation model based on the historical data of the user and the real-time position information; and sending the merchant recommendation information to a user terminal in a short message form.
Optionally, the method further comprises obtaining updated merchant information and updating merchant information in the merchant information base to make a recommendation based on the updated merchant information base.
Optionally, after sending the recommended merchant to the user side, the method further includes: and receiving a screening request sent by a user terminal and providing data of merchants indicated by the screening request.
Optionally, performing the correlation analysis on the topic and the behavior data includes: encoding the topic and the behavioral data; processing the codes based on a correlation analysis algorithm to determine a correlation rule, wherein the correlation rule indicates correlation between the behavior data of the subject.
In a second aspect, the present invention provides a merchant intelligent recommendation device, including a recommendation unit configured to determine a recommended merchant from a stored merchant information base by using a recommendation algorithm based on a user side location and send the recommended merchant to a user side; the first processing unit is configured to acquire feedback information of a user based on recommended merchants, sent by a user side, and determine emotion polarities corresponding to the feedback information; the second processing unit is configured to input the feedback information into a topic classification model to determine a corresponding topic in the feedback information; acquiring behavior data of the feedback user, and performing relevance analysis on the theme and the behavior data; an optimization unit configured to generate a feedback suggestion based on the emotion polarity, the result of the relevance analysis, to adjust the recommendation algorithm based on the feedback suggestion.
Optionally, performing the correlation analysis on the topic and the behavior data includes: encoding the topic and the behavioral data; processing the codes based on a correlation analysis algorithm to determine a correlation rule, wherein the correlation rule indicates correlation between the behavior data of the subject.
Optionally, the recommending unit is further configured to obtain real-time position information of the user terminal after receiving the short message sent by the user terminal; acquiring historical data of a corresponding user based on the user side; generating merchant recommendation information by using a recommendation model based on the historical data of the user and the real-time position information; and sending the merchant recommendation information to a user terminal in a short message form.
In a third aspect, the present invention provides a computer readable storage medium, where the storage medium stores a computer program, where the computer program, when executed by a processor, implements the intelligent recommendation method for a merchant according to any one of the above-mentioned implementations.
In a fourth aspect, the present invention provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing a merchant recommendation method provided in the first aspect when the program is executed.
The invention discloses a merchant intelligent recommendation method and device, wherein the method comprises the steps of determining recommended merchants from a stored merchant information base by using a recommendation algorithm based on the position of a user side and sending the recommended merchants to the user side; acquiring feedback information of a user based on recommended merchants, which is sent by a user terminal, and determining emotion polarities corresponding to the feedback information;
Inputting the feedback information into a topic classification model to determine a corresponding topic in the feedback information; acquiring behavior data of the feedback user, and performing relevance analysis on the theme and the behavior data; generating a feedback suggestion based on the emotion polarity and the correlation analysis result to adjust the recommendation algorithm based on the feedback suggestion. Through the user feedback and interaction functions, the recommendation result is continuously optimized, more accurate personalized recommendation is realized, and the problems that in the related technology, the existing system is mostly recommended based on user historical behaviors or information uploaded by merchants, accurate understanding and application of personalized preferences of users are lacking, and the recommendation effect is not ideal are solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is a flow chart of intelligent recommendation of merchants according to the present application;
Fig. 2 is a schematic diagram of an electronic device corresponding to fig. 1 according to the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present application and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
An exemplary method of intelligent recommendation for merchants is described below with reference to fig. 1. The method comprises the following steps:
Step 101: based on the user side position, a recommendation algorithm is utilized to determine a recommendation merchant from a stored merchant information base and send the recommendation merchant to the user side.
In this embodiment, a merchant information database is pre-established and includes merchant information such as merchant name, address, contact information, longitude and latitude coordinates, and the like. Merchant information and user data may be cleaned and processed prior to warehousing, including deduplication, normalization, and the like.
The recommendation algorithm can construct a personalized recommendation model through collaborative filtering, content filtering, deep learning, real-time recommendation, mixed recommendation and other algorithms. Such as: and (3) performing deep learning to pre-construct a recommendation model based on a collaborative filtering algorithm of the user and the object and then a content filtering algorithm according to merchant attributes and geographic position information.
As an optional implementation manner of this embodiment, determining, based on the location of the user terminal, a recommended merchant from the stored merchant information base by using a recommendation algorithm and sending the recommended merchant to the user terminal includes: after receiving a short message sent by a user side, acquiring real-time position information of the user side after consulting a request; acquiring historical data of a corresponding user based on the user side; generating merchant recommendation information by using a recommendation model based on the historical data of the user and the real-time position information; and sending the merchant recommendation information to a user terminal in a short message form.
In this optional implementation manner, the user side may be a device including a SIM card, and the user side may send an intelligent short message consultation through the SIM card, and then may start a mobile phone positioning service, and obtain real-time location information of the user based on the mobile phone positioning service. And then determining recommended merchants based on the real-time location information in combination with the historical data of the user. The historical data of the user can be information of historical position, preference, evaluation and the like of the user. The user's preference and preference can be accurately understood by acquiring the user's position information and analyzing the historical behavior in real time, and the merchant which meets the requirements is recommended for the user, so that personalized recommendation is realized.
Step 102: and acquiring feedback information of a user based on recommended merchants, which is sent by a user terminal, and determining emotion polarities corresponding to the feedback information.
In this embodiment, a user-friendly interface may be created in the application prior to determining the emotional polarity, allowing the user to provide feedback, comments, and ratings to the merchant or recommendation. Different options or tabs are provided so that the user can select or describe their feedback, for example: merchant service, food quality, price, etc. Users provide text comments to describe their experience and advice in detail. The user feedback data is saved in a database for subsequent analysis and processing, with each feedback being assigned a unique identifier so that the source and user of each feedback, including a time stamp, can be tracked to determine the time of submission of the feedback. The feedback data is then purged, including deduplication, spell checking, and normalized text. The text data is converted into structured data for analysis. For example, the evaluation paper is classified into sentences or keywords.
In emotion analysis, natural Language Processing (NLP) techniques are used to perform emotion analysis on text data to determine emotion polarity (positive, negative, neutral) in user feedback, by which the system can be assisted in identifying user satisfaction or dissatisfaction with merchants or recommendations.
According to the embodiment, the user can evaluate, comment and feed back through the interface, and the system collects and analyzes user feedback data, so that the user feedback data are used for optimizing a recommendation algorithm and improving a recommendation result, and real-time interaction and continuous optimization are realized.
Step 103: and inputting the feedback information into a topic classification model to determine a corresponding topic in the feedback information.
In this embodiment, first, feedback data of the user needs to be collected, which may include comments, scores, and text feedback left by the user in the application. Such data should include text and related metadata such as time stamps, user identifiers, etc.
The collected text data needs to be cleaned before topic modeling can be performed. This includes removing special characters, punctuation, stop words (e.g., "yes", "in" and like common words), spell checking and normalizing text. Data cleaning helps to improve modeling accuracy.
The cleaned text data is divided into sentences or paragraphs, which helps to better understand the context of the text.
The text data is trained by selecting an appropriate topic modeling algorithm, commonly used topic modeling algorithms include LATENT DIRICHLET Allocation (LDA) and LATENT SEMANTIC ANALYSIS (LSA). This creates a mathematical model that identifies topics in the text. The trained topic model is used for identifying topics of the feedback text of the user, and one or more topics are allocated to each text segment by the model. Each text segment is categorized into one or more topic categories, such as service, food, price, and the like. This step may be a multi-tag classification problem, allowing a text segment to be divided into topic categories.
Step 104: and acquiring behavior data of the feedback user, and carrying out association analysis on the theme and the behavior data.
And when the association analysis is carried out, associating the result of the topic classification with the user behavior data to ensure that the data set contains the topic classification and the related user behavior data of the user feedback. For the purpose of associative analysis, the results of the topic classification and the user behavior data are encoded so that the algorithm can process them, which typically requires converting the text topic into a digital or binary variable.
Illustratively, the user behavior data may include, but is not limited to:
location history and real-time location: user current location and historical location data acquired based on GPS, wi-Fi, or mobile network location technology. This is the core data that enables location dependent merchant recommendations.
Merchant interaction history: the user's interaction with a particular merchant or type of merchant is recorded, such as number of visits, dwell time, click-through advertisements, query routes, etc.
Search history: search queries entered by a user in an application, particularly searches related to local merchants, services, or products.
User evaluation and feedback: scoring, commenting, or other forms of feedback to the merchant by the user. This information is critical to understanding user preferences and improving recommendation algorithms.
Purchase records and transaction data: the user's purchase history at the relevant merchant, including the type of goods or services purchased, the amount of the expense, the frequency of purchase, etc.
Application usage behavior: activities of the user in the system such as frequency of use, function of use, duration of stay on a particular page, etc.
Personal settings and preferences: preferences set by the user in the application, such as merchant type of interest, price range, service type, etc.
Response history: the user's response to the past recommendation includes viewing, ignoring, clicking on a link, or actually accessing the merchant.
Further, suitable association analysis algorithms are selected, and commonly used algorithms include Apriori algorithm and FP-Growth algorithm for discovering frequent association rules between items. Selected association analysis algorithms are run to find frequent association rules , between the user feedback topics and the user behavior, which describe the correlation between topics and behavior. Association rules are interpreted to understand the association between the topic classification and the user's behavior. This may include determining whether the topic category affects user behavior, such as whether the user is more likely to return to the merchant or continue using the application.
Frequent association rules include, but are not limited to, user behavior and merchant preferences, i.e., determining an association between a particular merchant type and a user's location by analyzing the user's location history and ratings of certain types of merchants.
There is a correlation between feedback topics and user satisfaction, i.e., topics extracted from user feedback (e.g., quality of service, price, etc.) and user satisfaction or loyalty.
The cross-selling opportunities, i.e., by analyzing the user's purchase history and merchant type, may find potential cross-selling or promotional opportunities.
Step 105: generating a feedback suggestion based on the emotion polarity and the correlation analysis result to adjust the recommendation algorithm based on the feedback suggestion.
According to the embodiment, the recommendation accuracy and the individuation degree are improved by continuously optimizing the recommendation algorithm. In this embodiment, summary feedback and suggestions are generated based on the results of emotion analysis, topic modeling, and correlation analysis in order to improve the recommendation algorithm and merchant recommendation results.
As an optional implementation manner of this embodiment, updated merchant information is obtained, and merchant information in the merchant information base is updated to make a recommendation based on the updated merchant information base.
In this alternative implementation, a partnership is established with the merchant, and the latest information update of the merchant is obtained. Merchant information in the merchant database is updated periodically or in real-time, including new business merchants, business hour changes, and the like.
As an optional implementation manner of this embodiment, after sending the recommended merchant to the user side, the method further includes: and receiving a screening request sent by a user terminal and providing data of merchants indicated by the screening request.
In this optional implementation manner, after determining the recommended merchant, the recommended result and the merchant information may be displayed on the interface, and then the search and filtering function may be triggered on the interface, so that the user may query and screen the merchant according to the requirement.
The application also provides a merchant intelligent recommending device, which comprises a recommending unit, a recommending unit and a recommending unit, wherein the recommending unit is configured to determine a recommended merchant from a stored merchant information base by using a recommending algorithm based on the position of a user side and send the recommended merchant to the user side; the first processing unit is configured to acquire feedback information of a user based on recommended merchants, sent by a user side, and determine emotion polarities corresponding to the feedback information; the second processing unit is configured to input the feedback information into a topic classification model to determine a corresponding topic in the feedback information; acquiring behavior data of the feedback user, and performing relevance analysis on the theme and the behavior data; an optimization unit configured to generate a feedback suggestion based on the emotion polarity, the result of the relevance analysis, to adjust the recommendation algorithm based on the feedback suggestion.
As an optional implementation manner of this embodiment, performing the association analysis on the theme and the behavior data includes: encoding the topic and the behavioral data; processing the codes based on a correlation analysis algorithm to determine a correlation rule, wherein the correlation rule indicates correlation between the behavior data of the subject.
As an optional implementation manner of this embodiment, the recommending unit is further configured to obtain real-time location information of the user terminal after receiving a short message sent by the user terminal; acquiring historical data of a corresponding user based on the user side; generating merchant recommendation information by using a recommendation model based on the historical data of the user and the real-time position information; and sending the merchant recommendation information to a user terminal in a short message form.
The present application also provides a computer readable medium storing a computer program operable to perform the method provided in fig. 1 above.
The application also provides a schematic block diagram of the electronic device shown in fig. 2, which corresponds to fig. 1. At the hardware level, as shown in fig. 2, the electronic device includes a processor, an internal bus, a network interface, a memory, and a nonvolatile storage, and may of course include hardware required by other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs to implement a model loading method as described above with respect to fig. 1. Of course, other implementations, such as logic devices or combinations of hardware and software, are not excluded from the present application, that is, the execution subject of the following processing flows is not limited to each logic unit, but may be hardware or logic devices.
In the 90 s of the 20 th century, improvements to one technology could clearly be distinguished as improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software (improvements to the process flow). However, with the development of technology, many improvements of the current method flows can be regarded as direct improvements of hardware circuit structures. Designers almost always obtain corresponding hardware circuit structures by programming improved method flows into hardware circuits. Therefore, an improvement of a method flow cannot be said to be realized by a hardware entity module. For example, a programmable logic device (Programmable Logic Device, PLD) (e.g., field programmable gate array (Field Programmable GATE ARRAY, FPGA)) is an integrated circuit whose logic functions are determined by user programming of the device. A designer programs to "integrate" a digital system onto a PLD without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, such programming is mostly implemented with "logic compiler (logic compiler)" software, which is similar to the software compiler used in program development and writing, and the original code before being compiled is also written in a specific programming language, which is called hardware description language (Hardware Description Language, HDL), but HDL is not just one, but a plurality of kinds, such as ABEL(Advanced Boolean Expression Language)、AHDL(Altera Hardware Description Language)、Confluence、CUPL(Cornell University Programming Language)、HDCal、JHDL(Java Hardware Description Language)、Lava、Lola、MyHDL、PALASM、RHDL(Ruby Hardware Description Language), and VHDL (Very-High-SPEED INTEGRATED Circuit Hardware Description Language) and Verilog are currently most commonly used. It will also be apparent to those skilled in the art that a hardware circuit implementing the logic method flow can be readily obtained by merely slightly programming the method flow into an integrated circuit using several of 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, application SPECIFIC INTEGRATED Circuits (ASICs), programmable logic controllers, and embedded microcontrollers, examples of controllers 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 of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller may thus be regarded as a kind of hardware component, and means for performing various functions included therein may also be regarded as structures within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, 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 functionally divided into various units, respectively. Of course, the functions of each element may be implemented in the same piece or pieces of software and/or hardware when implementing the present application.
It will be appreciated by those skilled in the art that 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 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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, 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 one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
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 media for a computer 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, read only 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, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
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 one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
It will be appreciated by those skilled in the art that 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 media (including but not limited to disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
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 media including memory storage devices.
The embodiments of the present application are described in a progressive manner, and the same and similar parts of the embodiments are all referred to each other, and each embodiment is mainly described in the differences from the other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (10)
1. The intelligent recommendation method for the merchant is characterized by comprising the following steps of:
determining a recommended merchant from a stored merchant information base by using a recommendation algorithm based on the position of the user side and sending the recommended merchant to the user side;
acquiring feedback information of a user based on recommended merchants, which is sent by a user terminal, and determining emotion polarities corresponding to the feedback information;
inputting the feedback information into a topic classification model to determine a corresponding topic in the feedback information;
Acquiring behavior data of the feedback user, and performing relevance analysis on the theme and the behavior data;
Generating a feedback suggestion based on the emotion polarity and the correlation analysis result to adjust the recommendation algorithm based on the feedback suggestion.
2. The intelligent recommendation method of merchants according to claim 1, wherein determining recommended merchants from a stored merchant information base by using a recommendation algorithm based on the location of the user side and transmitting the recommended merchants to the user side comprises:
after receiving a short message sent by a user side, acquiring real-time position information of the user side;
acquiring historical data of a corresponding user based on the user side;
Generating merchant recommendation information by using a recommendation model based on the historical data of the user and the real-time position information;
And sending the merchant recommendation information to a user terminal in a short message form.
3. The method of claim 1, further comprising obtaining updated merchant information and updating merchant information in the merchant information base to make recommendations based on the updated merchant information base.
4. The intelligent recommendation method for merchants of claim 1, wherein after sending the recommended merchant to the user side, the method further comprises:
and receiving a screening request sent by a user terminal and providing data of merchants indicated by the screening request.
5. The business intelligence recommendation method of claim 1, wherein performing a relevance analysis on the topic and the behavioral data comprises:
encoding the topic and the behavioral data;
processing the codes based on a correlation analysis algorithm to determine a correlation rule, wherein the correlation rule indicates correlation between the behavior data of the subject.
6. An intelligent recommendation device for merchants, comprising:
The recommendation unit is configured to determine a recommended merchant from the stored merchant information base by using a recommendation algorithm based on the position of the user side and send the recommended merchant to the user side;
the first processing unit is configured to acquire feedback information of a user based on recommended merchants, sent by a user side, and determine emotion polarities corresponding to the feedback information;
The second processing unit is configured to input the feedback information into a topic classification model to determine a corresponding topic in the feedback information; acquiring behavior data of the feedback user, and performing relevance analysis on the theme and the behavior data;
An optimization unit configured to generate a feedback suggestion based on the emotion polarity, the result of the relevance analysis, to adjust the recommendation algorithm based on the feedback suggestion.
7. The merchant intelligent recommendation device of claim 6, wherein performing a relevance analysis on the topic and the behavioral data comprises: encoding the topic and the behavioral data;
processing the codes based on a correlation analysis algorithm to determine a correlation rule, wherein the correlation rule indicates correlation between the behavior data of the subject.
8. The merchant intelligent recommendation device of claim 6, wherein the recommendation unit is further configured to:
after receiving a short message sent by a user side, acquiring real-time position information of the user side;
acquiring historical data of a corresponding user based on the user side;
Generating merchant recommendation information by using a recommendation model based on the historical data of the user and the real-time position information;
And sending the merchant recommendation information to a user terminal in a short message form.
9. A computer-readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method of any of the preceding claims 1-5.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of the preceding claims 1-5 when executing the program.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311620254.9A CN117932167A (en) | 2023-11-30 | 2023-11-30 | Intelligent recommendation method and device for merchants |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311620254.9A CN117932167A (en) | 2023-11-30 | 2023-11-30 | Intelligent recommendation method and device for merchants |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117932167A true CN117932167A (en) | 2024-04-26 |
Family
ID=90769186
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311620254.9A Pending CN117932167A (en) | 2023-11-30 | 2023-11-30 | Intelligent recommendation method and device for merchants |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117932167A (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012051583A1 (en) * | 2010-10-15 | 2012-04-19 | Ness Computing, Inc. | A computer system and method for analyzing data sets and providing personalized recommendations |
| CN109165997A (en) * | 2018-07-19 | 2019-01-08 | 阿里巴巴集团控股有限公司 | It does shopping under a kind of line the generation method and device of recommendation |
| US20200320609A1 (en) * | 2019-04-08 | 2020-10-08 | Toshiba Tec Kabushiki Kaisha | Method and system for providing personalized recommendations in real-time |
| WO2021221635A1 (en) * | 2020-04-29 | 2021-11-04 | Hewlett-Packard Development Company, L.P. | Feedback insight recommendation |
| CN114201685A (en) * | 2021-12-15 | 2022-03-18 | 南通全织道家纺科技有限公司 | Commodity recommendation method based on user behavior big data |
| CN116228180A (en) * | 2023-03-09 | 2023-06-06 | 贵州自由客网络技术有限公司 | User information recommendation method, device and storage medium |
-
2023
- 2023-11-30 CN CN202311620254.9A patent/CN117932167A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012051583A1 (en) * | 2010-10-15 | 2012-04-19 | Ness Computing, Inc. | A computer system and method for analyzing data sets and providing personalized recommendations |
| CN109165997A (en) * | 2018-07-19 | 2019-01-08 | 阿里巴巴集团控股有限公司 | It does shopping under a kind of line the generation method and device of recommendation |
| US20200320609A1 (en) * | 2019-04-08 | 2020-10-08 | Toshiba Tec Kabushiki Kaisha | Method and system for providing personalized recommendations in real-time |
| WO2021221635A1 (en) * | 2020-04-29 | 2021-11-04 | Hewlett-Packard Development Company, L.P. | Feedback insight recommendation |
| CN114201685A (en) * | 2021-12-15 | 2022-03-18 | 南通全织道家纺科技有限公司 | Commodity recommendation method based on user behavior big data |
| CN116228180A (en) * | 2023-03-09 | 2023-06-06 | 贵州自由客网络技术有限公司 | User information recommendation method, device and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113837804B (en) | Information recommendation method, device and equipment | |
| US20210174164A1 (en) | System and method for a personalized search and discovery engine | |
| US10853379B2 (en) | Method and apparatus for quickly evaluating entities | |
| CN108733825B (en) | Object trigger event prediction method and device | |
| CN110503206A (en) | A kind of prediction model update method, device, equipment and readable medium | |
| CN110674408B (en) | Service platform, and real-time generation method and device of training sample | |
| US10902341B1 (en) | Machine learning based list recommendations | |
| US8442849B2 (en) | Emotional mapping | |
| CN110428298A (en) | A kind of shop recommended method, device and equipment | |
| CN113688313A (en) | Training method of prediction model, information pushing method and device | |
| EP2825981A1 (en) | Publishing product information | |
| CN112418920A (en) | Training method of information recommendation model, information recommendation method and device | |
| US20210397670A1 (en) | User-directed suggestions | |
| CN112733024A (en) | Information recommendation method and device | |
| US20230341950A1 (en) | Intelligent Keyboard | |
| CN111191132B (en) | Information recommendation method and device and electronic equipment | |
| CN114329207A (en) | Multi-service information sequencing system, method, storage medium and electronic equipment | |
| Wayesa et al. | Pattern-based hybrid book recommendation system using semantic relationships | |
| CN115048577A (en) | Model training method, device, equipment and storage medium | |
| CN113641894A (en) | Information recommendation method and device | |
| CN113343095A (en) | Model training and information recommendation method and device | |
| CN111046304A (en) | Data searching method and device | |
| CN113010809A (en) | Information recommendation method and device | |
| CN111177562B (en) | Recommendation ordering processing method and device for target object and server | |
| CN111639269A (en) | Site recommendation method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |