CN114708036A - Product promotion system and method based on social network technology - Google Patents

Product promotion system and method based on social network technology Download PDF

Info

Publication number
CN114708036A
CN114708036A CN202210440821.1A CN202210440821A CN114708036A CN 114708036 A CN114708036 A CN 114708036A CN 202210440821 A CN202210440821 A CN 202210440821A CN 114708036 A CN114708036 A CN 114708036A
Authority
CN
China
Prior art keywords
node information
information
keywords
commodity
module
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.)
Granted
Application number
CN202210440821.1A
Other languages
Chinese (zh)
Other versions
CN114708036B (en
Inventor
张强
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
China Television Zhongguang International Media Wuhan Co ltd
Original Assignee
Beijing Hunter Information Technology Co ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Beijing Hunter Information Technology Co ltd filed Critical Beijing Hunter Information Technology Co ltd
Priority to CN202210440821.1A priority Critical patent/CN114708036B/en
Publication of CN114708036A publication Critical patent/CN114708036A/en
Application granted granted Critical
Publication of CN114708036B publication Critical patent/CN114708036B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

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
    • G06Q30/0241—Advertisements
    • G06Q30/0251—Targeted advertisements
    • G06Q30/0255—Targeted advertisements based on user history
    • 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
    • G06F16/906—Clustering; Classification
    • 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
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • 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
    • G06F16/9536—Search customisation based on social or collaborative filtering
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities
    • G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
    • 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
    • G06Q30/0202—Market predictions or forecasting for commercial activities
    • 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
    • G06Q30/0241—Advertisements
    • G06Q30/0251—Targeted advertisements
    • G06Q30/0257—User requested
    • 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
    • G06Q30/0241—Advertisements
    • G06Q30/0251—Targeted advertisements
    • G06Q30/0264—Targeted advertisements based upon schedule
    • 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
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0631—Item recommendations
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Accounting & Taxation (AREA)
  • Finance (AREA)
  • Databases & Information Systems (AREA)
  • Development Economics (AREA)
  • Strategic Management (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Economics (AREA)
  • Data Mining & Analysis (AREA)
  • General Engineering & Computer Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Game Theory and Decision Science (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Computational Linguistics (AREA)
  • General Health & Medical Sciences (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention relates to the field of product promotion, and discloses a product promotion system and a method based on a social network technology, wherein the product promotion system based on the social network technology comprises the following steps: the basic information extraction module is used for acquiring basic information of the user. The node information extraction module is used for acquiring user node information. The timing module times the current time to obtain the current timeThe previous time point. The correction module is used for obtaining node information Ni+1Then, the node information N is judgedi+1Information of whether to communicate with node NiContrary to this, if yes, the node information N is determinedi+1Frequency vi+1Whether or not it is greater than fviWherein v isiFor node information Ni+1Frequency, f is a scale factor; if yes, the node information N is transmittedi+1Replacement by node information Ni. The prediction module is used for predicting the node information to obtain the predicted node information of the current time. And the commodity recommending module recommends commodities according to the predicted node information and the current node information. The prediction can be carried out in advance, the first opportunity is mastered, and the market is surpassed.

Description

Product promotion system and method based on social network technology
Technical Field
The invention relates to the field of product promotion, in particular to a product promotion system and method based on social network technology.
Background
With the development of science and technology, people enter the electronic information age, sales are advanced with time, and the development of information is well mastered, so that the development of products is the most important to popularize in the future. The product promotion refers to a stage that enterprise products enter a market after coming out; the network popularization of the product needs to be assisted by certain network tools and resources, and the website popularization method is reasonable utilization of certain website popularization means and tools; some enterprises have own product enterprise websites and can popularize the websites; the product promotion method includes many methods such as an information publishing promotion method, a search engine promotion method, an e-mail promotion method, a resource cooperation promotion method, and the like.
The social network refers to an internet application service for helping people establish a social network, the social network comprises forums, electronic bulletins, discussion groups, chatting, friend making, personal spaces and the like, user experience is mainly used as a core, based on friend making, the user is helped to expand the popularity through various activities and applications, and meanwhile, the requirements of users on communication, sharing and the like in the process are met.
Disclosure of Invention
In order to solve the above technical problem, the present invention provides a product promotion system based on a social network technology, including:
the basic information extraction module is used for acquiring basic information of the user;
the node information extraction module is used for acquiring user node information;
the timing module is used for timing the current time to obtain a current time point;
a correction module for obtaining node information Ni+1Then, the node information N is judgedi+1Information of whether to communicate with node NiContrary to this, if yes, the node information N is determinedi+1Frequency vi+1Whether or not it is greater than fviWherein v isiFor node information Ni+1Frequency, f is a scale factor; if yes, the node information N is transmittedi+1Replacement by node information Ni；
The prediction module is used for predicting the node information to obtain the predicted node information of the current time;
and the commodity recommending module recommends commodities according to the predicted node information and the current node information.
Preferably: the node information extraction module comprises a text information acquisition module, a keyword extraction module, a keyword statistics module and a keyword determination module; the text information acquisition module is used for acquiring text information, and the keyword extraction module is used for extracting keywords from the text information; the keyword counting module counts the keywords, calculates the occurrence frequency of the keywords, judges whether the occurrence frequency of the keywords exceeds a standard value, if so, the keyword determining module determines the keywords as the keywords and combines the keywords to obtain the node information Ni。
Preferably: when the keywords are extracted, performing component grouping, wherein the component grouping divides text information into subject keywords, predicate keywords, object keywords, final keywords, object keywords and complementary keywords; extracting component grouping keywords from the text information, calculating to obtain the occurrence frequency of each component keyword, judging whether the occurrence frequency of each component keyword exceeds a standard value, if so, identifying the component grouping keywords as useful keywords, and combining the component grouping keywords to obtain node information Ni。
Preferably: the product promotion system based on the social network technology comprises a commodity type definition module, the commodity type definition module extracts commodity purchasing information from node information in the current time period and classifies the commodity information, the commodity type definition module sorts the number of classified commodities to obtain the most commodity types, the commodity type definition module sorts the brands of the commodities of the most commodity types, and the commodity recommendation module recommends to a user according to the commodity brand sorting sequence.
Preferably: the product promotion system based on the social network technology comprises a personnel classification module, wherein the personnel classification module is used for extracting commodity purchasing information from node information in the current time period and classifying the commodity information, the commodity type definition module sequences the number of classified commodities to obtain the most commodity types, obtains commodity prices P in the most commodity types, and judges whether the commodity prices P are larger than a high consumption standard P or not1If yes, defining that the user belongs to a high consumption group; if not, judging whether the commodity price P is larger than a low consumption standard P2(ii) a If yes, defining that the user belongs to a middle consumption crowd; if not, defining that the user belongs to a low consumption crowd; and the most kinds of commodities are according to the high price consumption standard P1Low consumption standard P2The method comprises the following steps of (1) classifying the commodities into high-grade commodities, medium-grade commodities and low-grade commodities; and the commodity recommending module recommends the commodity grade corresponding to the consumption grade of the user to the user.
The invention also provides a product promotion method based on the social network technology, which is applied to the product promotion system based on the social network technology, and comprises the following steps:
s1, acquiring basic user information and node information Ni；
S2, timing the current time to obtain the current time point;
s3, judging node information Ni+1Information of whether to communicate with node NiContrarily, if yes, S4 is executed;
s4, judging node information Ni+1Frequency vi+1Whether or not it is greater than fviIf so, go to S5;
s5, converting the node information Ni+1Replacement by node information Ni；
S6, predicting the node information to obtain the predicted node information of the current time;
and S7, recommending commodities according to the predicted node information and the current node information.
Preferably: the node information NiThe acquisition method comprises the following steps:
s11, acquiring the text information, and performing component grouping on the text information;
s12, extracting keywords from the component groups;
s13, carrying out statistics on the keywords, and calculating to obtain the occurrence frequency of the keywords;
s14, judging whether the occurrence frequency of the keywords exceeds a standard value, if so, executing S15;
s15, identifying the keywords as useful keywords;
s16, and combining the keywords to obtain node information Ni。
Preferably: the method for recommending commodities comprises the following steps:
s71, extracting the purchased commodity information from the node information in the current time period, and classifying the commodity information;
s72, sequencing the number of classified commodities to obtain the most commodity types and commodity prices P;
s73, judging whether the commodity price P is larger than a high consumption standard P1If yes, go to S74, if no, go to S75;
s74, defining that the user belongs to a high-consumption crowd;
s75, judging whether the commodity price P is larger than a low consumption standard P2(ii) a If so, performing S76, if not, performing S77;
s76, defining that the user belongs to middle consumption crowd;
s77, defining that the user belongs to a low-consumption crowd;
s78, according to the price high consumption standard P, the most commodity types1Low consumption standard P2The method comprises the following steps of (1) classifying the commodities into high-grade commodities, medium-grade commodities and low-grade commodities;
and S79, recommending the commodity grade corresponding to the consumption grade of the user to the user.
The invention further provides a computer terminal, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the steps of the product promotion method based on the social network technology.
The present invention also provides a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of a social networking technology-based product promotion method described above.
The invention has the technical effects and advantages that: by combining the basic information and the node information, the user information can be grasped constantly, and the accuracy of information extraction is improved. And the nodes are used for correcting in real time, so that information collection errors are avoided, and the overall accuracy of the information is improved. By predicting the node information, the user demand can be known in advance, the business opportunity can be grasped in advance, the advance response can be realized, and the business opportunity can be obtained.
Drawings
Fig. 1 is a block diagram of a product promotion system based on social networking technology according to the present invention.
Fig. 2 is a flowchart of a product promotion method based on social network technology according to the present invention.
FIG. 3 shows node information N in a product promotion method based on social network technology according to the present inventioniA flow chart of the method is obtained.
Fig. 4 is a flowchart of a method for recommending commodities in a product promotion method based on a social network technology according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments. The embodiments of the present invention have been presented for purposes of illustration and description, and are not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiment was chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.
Example 1
Referring to fig. 1, in the present embodiment, a social networking technology-based product promotion system is provided and includes:
the basic information extraction module is used for acquiring basic information of the user, wherein the basic information can be name, age, gender, native place and the like, and the basic information cannot change along with the change of time. The basic information acquired by the basic information extraction module can be acquired through user registration information, registration ID, and the like, which is not described herein in detail.
The node information extraction module is used for obtaining user node information, wherein the node information can comprise current state information, current state demand information, current state purchase information and the like, and the node information can be changed along with the change of time, conditions and current states. For example, if the user is currently looking for work and issues work unit information after a period of time, the user status information is determined to be in an on-duty state, the state continues, and the node information extraction module does not extract information about the status information. After working for three years, the current information is the current information of three years, and the current demand information of the user is the demand of the automobile if the vehicle information is frequently consulted this year. The node information extraction module may include a text information acquisition module, a keyword extraction module, a keyword statistics module, and a keyword determination module, where the text information acquisition module is configured to acquire text information, and the text information may be acquired from search content, input content, publication content, chat content, and the like, and details are not described here. The keyword extraction module is used for extracting keywords from the text information so as to obtain the keywords, the keyword counting module counts the keywords, calculates the occurrence frequency of the keywords, judges whether the occurrence frequency of the keywords exceeds a standard value, if so, the keyword determining module determines the keywords as useful keywords and combines the keywords to obtain the node information Ni. And when the keywords are extracted, component grouping is carried out, and the component grouping divides the text information into subject keywords, predicate keywords, object keywords, final keywords, object keywords and complementary keywords. Extracting component grouping keywords from text information, calculating to obtain the occurrence frequency of each component keyword, judging whether the occurrence frequency of each component keyword exceeds a standard value, if so, identifying the component grouping keywords as useful keywords, and performing component grouping keyword extraction on each component keyword to obtain the component grouping keywordCombining the keywords of the component groups to obtain node information Ni. For example, the text information is that "i leave a certain enterprise that lets me hurt today, the text information is subjected to component grouping to extract a subject keyword as me, i is a certain in the basic information, a predicate keyword is a part of the wrecking and leaves, an object keyword is a certain enterprise, a final keyword is a part of the hurting of me, and the like, and the times of occurrence of the keywords of each component grouping of the same text information within a month are 4 times, 6 times of occurrence of a certain enterprise, 20 times of occurrence of i and 3 times exceeding a standard value, so that a useful subject keyword is a certain, a predicate keyword is a part of the wrecking, an object keyword is a certain enterprise, and keywords of each component grouping are combined to obtain node information that is a part of the certain enterprise.
The timing module is configured to time the current time to obtain a current time point, where the current time point obtained by the timing module may be in units of days or weeks, where days are preferred, and details are not described herein.
A correction module for correcting the node information NiCorrecting when the node information extraction module obtains the node information Ni+1Then, the node information N is judgedi+1Information of whether to communicate with node NiContrary to this, if yes, the node information N is determinedi+1Frequency vi+1Whether or not it is greater than fviWherein v isiFor node information Ni+1Frequency, f is a scaling factor. If yes, the node information N is transmittedi+1Substitution into node information Ni. The contradictory can be determined by definition, or can be antisense words, and the specific words are not described in detail.
The prediction module is configured to predict the node information to obtain predicted node information at the current time, for example, a user enters a certain enterprise three years ago, the prediction module may predict current purchasing power, and the prediction of the purchasing power may remove ordinary consumption according to the income status of the user, which is not specifically described herein.
And the commodity recommending module recommends commodities according to the predicted node information and the current node information. For example, the predicted node information is the purchasing ability of people, and the current node information may be the direction of interest, so that the recommended goods may be made. For example, vehicle information is frequently consulted this year, the current demand information of the user is the demand of the vehicle, and the price of the vehicle browsed by the user is generally 10 to 20 ten thousand. If the purchasing power of the prediction node information is 25 ten thousand for people, 15 to 25 ten thousand of automobiles can be recommended to the user, which is not described herein in detail.
Example 2
The product promotion system based on the social network technology comprises a commodity type definition module, the commodity type definition module extracts commodity purchasing information from node information in the current time period and classifies the commodity information, the commodity type definition module sorts the number of classified commodities to obtain the most commodity types, the commodity type definition module sorts the brands of the commodities of the most commodity types, and the commodity recommendation module recommends to a user according to the commodity brand sorting sequence. The brand ranking may be according to quality, sales quantity, price, rating, etc., and is not described herein in detail.
Example 3
The product promotion system based on the social network technology comprises a personnel classification module, wherein the personnel classification module is used for extracting commodity purchasing information from node information in the current time period and classifying the commodity information, the commodity type definition module sequences the number of classified commodities to obtain the most commodity types, obtains commodity prices P in the most commodity types, and judges whether the commodity prices P are larger than a high consumption standard P or not1If yes, defining that the user belongs to a high consumption group; if not, judging whether the commodity price P is larger than a low consumption standard P or not2(ii) a If yes, defining that the user belongs to a middle consumption crowd; if not, defining that the user belongs to a low consumption group. And the most kinds of commodities are according to the high price consumption standard P1Low consumption standard P2It is divided into high-grade goods, medium-grade goods and low-grade goods. And the commodity recommending module recommends the commodity grade corresponding to the consumption grade of the user to the user.
Example 4
Referring to fig. 2, in this embodiment, a product promotion method based on social network technology is provided, including the following steps:
s1, acquiring basic user information and node information Ni。
And S2, timing the current time to acquire the current time point.
S3, judging node information Ni+1Information of whether to communicate with node NiContrarily, if yes, S4 is executed.
S4, judging node information Ni+1Frequency vi+1Whether or not it is greater than fviIf so, S5 is executed.
S5, converting the node information Ni+1Replacement by node information Ni。
And S6, predicting the node information to obtain the predicted node information of the current time.
And S7, recommending commodities according to the predicted node information and the current node information.
Referring to fig. 3, in S1, node information NiThe acquisition method comprises the following steps:
and S11, acquiring the text information and grouping the components of the text information.
And S12, extracting keywords from the component groups.
And S13, counting the keywords and calculating the occurrence frequency of the keywords.
S14, judging whether the occurrence frequency of the keywords exceeds a standard value, if so, executing S15.
And S15, identifying the keyword as a useful keyword.
S16, and combining the keywords to obtain node information Ni。
Referring to fig. 4, in S7, the method of recommending merchandise includes the steps of:
and S71, extracting the purchased commodity information from the node information in the current time period, and classifying the commodity information.
And S72, sequencing the classified commodity quantity to obtain the most commodity types and commodity prices P.
S73, judging whether the commodity price P is larger than a high consumption standard P1If so, S74 is executed, if notThen S75 is executed.
And S74, defining that the user belongs to the high-consumption group.
S75, judging whether the commodity price P is larger than a low consumption standard P2(ii) a If so, S76 is performed, and if not, S77 is performed.
And S76, defining that the user belongs to the middle consumption crowd.
And S77, defining that the user belongs to a low-consumption group.
S78, according to the price high consumption standard P, the most kinds of commodities1Low consumption standard P2It is divided into high-grade goods, medium-grade goods and low-grade goods.
And S79, recommending the commodity grade corresponding to the consumption grade of the user to the user.
As a preferred embodiment of the present invention, a computer terminal comprises a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor implements the steps of a product promotion method based on social networking technology when executing the program.
When the product popularization method based on the social network technology is applied, the product popularization method can be applied in a software mode, for example, the product popularization method is designed into an independently running program and is installed on a computer terminal, and the computer terminal can be a computer, a smart phone, a control system and other Internet of things equipment. The product promotion method based on the social network technology can also be designed into an embedded running program and installed on a computer terminal, such as a single chip microcomputer.
As a preferred embodiment of the present invention, a computer-readable storage medium has a computer program stored thereon. The program, when executed by a processor, implements the steps of a method for product promotion based on social networking technology. When the product promotion method based on the social network technology is applied, the product promotion method can be applied in a software mode, for example, a program which is designed to be independently operated by a computer readable storage medium, wherein the computer readable storage medium can be a U disk and is designed to be a U shield, and the U disk is designed to be a program which starts the whole method through external triggering.
It should be apparent that the described embodiments are only some of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by one of ordinary skill in the art and related arts based on the embodiments of the present invention without any creative effort, shall fall within the protection scope of the present invention. Structures, devices, and methods of operation not specifically described or illustrated herein are generally practiced in the art without specific recitation or limitation.

Claims (10)

1. A social networking technology-based product promotion system, comprising:
the basic information extraction module is used for acquiring basic information of the user;
the node information extraction module is used for acquiring user node information;
the timing module is used for timing the current time to obtain a current time point;
a correction module for obtaining node information Ni+1Then, the node information N is judgedi+1Information of whether to communicate with node NiContrary to this, if yes, the node information N is determinedi+1Frequency vi+1Whether or not it is greater than fviWherein v isiFor node information Ni+1Frequency, f is a scale factor; if yes, the node information N is transmittedi+1Substitution into node information Ni；
The prediction module is used for predicting the node information to obtain the predicted node information of the current time;
and the commodity recommending module recommends commodities according to the predicted node information and the current node information.
2. The product promotion system based on the social network technology according to claim 1, wherein the node information extraction module comprises a text information acquisition module, a keyword extraction module, a keyword statistics module and a keyword determination module; the text information acquisition module is used for acquiring text information, and the keyword extraction module is used for extracting keywords from the textExtracting keywords from the information; the keyword counting module counts the keywords, calculates the occurrence frequency of the keywords, judges whether the occurrence frequency of the keywords exceeds a standard value, if so, the keyword determining module determines the keywords as the keywords and combines the keywords to obtain the node information Ni。
3. The product promotion system based on social networking technology according to claim 2, wherein the keywords are extracted by component grouping, and the component grouping divides text information into subject keywords, predicate keywords, object keywords, fixed keywords, subject keywords, and complement keywords; extracting component grouping keywords from the text information, calculating to obtain the occurrence frequency of each component keyword, judging whether the occurrence frequency of each component keyword exceeds a standard value, if so, identifying the component grouping keywords as useful keywords, and combining the component grouping keywords to obtain node information Ni。
4. The product promotion system based on the social network technology as claimed in claim 1, wherein the product promotion system based on the social network technology comprises a commodity type definition module, the commodity type definition module extracts purchased commodity information from node information in a current time period and classifies the commodity information, the commodity type definition module sorts the number of classified commodities to obtain the most commodity types, the commodity type definition module sorts the brands of the commodities of the most commodity types, and the commodity recommendation module recommends to a user according to the commodity brand sorting order.
5. The product promotion system based on social networking technology according to claim 1, wherein the product promotion system based on social networking technology comprises a people classification module, the people classification module is used for extracting information of purchased commodities from node information in the current time period and classifying the commodity information, the commodity type definition module is used for sequencing the number of classified commodities,obtaining the most commodity types, obtaining the commodity price P in the most commodity types, and judging whether the commodity price P is more than a high consumption standard P1If yes, defining that the user belongs to a high consumption group; if not, judging whether the commodity price P is larger than a low consumption standard P2(ii) a If yes, defining that the user belongs to a middle consumption crowd; if not, defining that the user belongs to a low consumption crowd; and the most kinds of commodities are according to the high price consumption standard P1Low consumption standard P2The method comprises the following steps of (1) classifying the commodities into high-grade commodities, medium-grade commodities and low-grade commodities; and the commodity recommending module recommends the commodity grade corresponding to the consumption grade of the user to the user.
6. A product promotion method based on social network technology is applied to the product promotion system based on social network technology of any one of claims 1 to 5, and is characterized in that the product promotion method based on social network technology comprises the following steps:
s1, obtaining user basic information and node information Ni；
S2, timing the current time to obtain the current time point;
s3, judging node information Ni+1Information of whether to communicate with node NiContrarily, if yes, S4 is executed;
s4, judging node information Ni+1Frequency vi+1Whether or not it is greater than fviIf so, go to S5;
s5, converting the node information Ni+1Replacement by node information Ni；
S6, predicting the node information to obtain the predicted node information of the current time;
and S7, recommending commodities according to the predicted node information and the current node information.
7. The product promotion method based on social networking technology according to claim 6, wherein the node information N isiThe acquisition method comprises the following steps:
s11, acquiring the text information, and performing component grouping on the text information;
s12, extracting keywords from the component groups;
s13, carrying out statistics on the keywords, and calculating to obtain the occurrence frequency of the keywords;
s14, judging whether the occurrence frequency of the keywords exceeds a standard value, if so, executing S15;
s15, identifying the keywords as useful keywords;
s16, combining the keywords to obtain node information Ni。
8. The product promotion method based on social networking technology according to claim 6, wherein the method for recommending commodities comprises the following steps:
s71, extracting the purchased commodity information from the node information in the current time period, and classifying the commodity information;
s72, sequencing the number of classified commodities to obtain the maximum commodity types and commodity prices P;
s73, judging whether the commodity price P is larger than a high consumption standard P1If yes, performing S74, if no, performing S75;
s74, defining that the user belongs to a high-consumption crowd;
s75, judging whether the commodity price P is larger than a low consumption standard P2(ii) a If so, performing S76, if not, performing S77;
s76, defining that the user belongs to middle consumption crowd;
s77, defining that the user belongs to a low-consumption crowd;
s78, according to the price high consumption standard P, the most commodity types1Low consumption standard P2The method comprises the following steps of (1) classifying the commodities into high-grade commodities, medium-grade commodities and low-grade commodities;
and S79, recommending the commodity grade corresponding to the consumption grade of the user to the user.
9. A computer terminal comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of a method for social networking technology based product promotion of any one of claims 6 to 8.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of a method for social networking technology-based product promotion of any one of claims 6 to 8.
CN202210440821.1A 2022-04-26 2022-04-26 Product promotion system and method based on social network technology Active CN114708036B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210440821.1A CN114708036B (en) 2022-04-26 2022-04-26 Product promotion system and method based on social network technology

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210440821.1A CN114708036B (en) 2022-04-26 2022-04-26 Product promotion system and method based on social network technology

Publications (2)

Publication Number Publication Date
CN114708036A true CN114708036A (en) 2022-07-05
CN114708036B CN114708036B (en) 2023-04-07

Family

ID=82174449

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210440821.1A Active CN114708036B (en) 2022-04-26 2022-04-26 Product promotion system and method based on social network technology

Country Status (1)

Country Link
CN (1) CN114708036B (en)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102509233A (en) * 2011-11-29 2012-06-20 汕头大学 User online action information-based recommendation method
US20150269595A1 (en) * 2014-03-24 2015-09-24 Salesforce.Com, Inc. Contact recommendations based on purchase history
CN109299992A (en) * 2018-07-16 2019-02-01 深圳市谷熊网络科技有限公司 A kind of method, apparatus and terminal to user's recommendation information
CN111428143A (en) * 2020-06-11 2020-07-17 北京每日优鲜电子商务有限公司 Commodity recommendation method and system, server and storage medium

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102509233A (en) * 2011-11-29 2012-06-20 汕头大学 User online action information-based recommendation method
US20150269595A1 (en) * 2014-03-24 2015-09-24 Salesforce.Com, Inc. Contact recommendations based on purchase history
CN109299992A (en) * 2018-07-16 2019-02-01 深圳市谷熊网络科技有限公司 A kind of method, apparatus and terminal to user's recommendation information
CN111428143A (en) * 2020-06-11 2020-07-17 北京每日优鲜电子商务有限公司 Commodity recommendation method and system, server and storage medium

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
张俊伟、杨柳等: "基于文本挖掘的商品推荐", 《哈尔滨商业大学学报（自然科学版）》 *

Also Published As

Publication number Publication date
CN114708036B (en) 2023-04-07

Similar Documents

Publication Publication Date Title
CN105224699B (en) News recommendation method and device
CN108805598B (en) Similarity information determination method, server and computer-readable storage medium
CN109299994B (en) Recommendation method, device, equipment and readable storage medium
CN108427891B (en) Neighborhood recommendation method based on differential privacy protection
US20160379268A1 (en) User behavior data analysis method and device
US20140108200A1 (en) Method and system for recommending search phrases
US20120109946A1 (en) Determination of category information using multiple
JP5615857B2 (en) Analysis apparatus, analysis method, and analysis program
US20160239865A1 (en) Method and device for advertisement classification
CN110910199A (en) Item information sorting method and device, computer equipment and storage medium
CN103778260A (en) Individualized microblog information recommending system and method
CN105159910A (en) Information recommendation method and device
CN103870972A (en) Data recommendation method and data recommendation system
US10223726B2 (en) Information provisioning device, method, and medium for evaluating and estimating gift candidates
CN110111167A (en) A kind of method and apparatus of determining recommended
CN109767302B (en) Method and device for constructing big data accurate model
CN112149003B (en) Commodity community recommendation method and device and computer equipment
CN111400613A (en) Article recommendation method, device, medium and computer equipment
CN111310038A (en) Information recommendation method and device, electronic equipment and computer-readable storage medium
CN107104875B (en) Information pushing method and device
CN113051480A (en) Resource pushing method and device, electronic equipment and storage medium
CN111104590A (en) Information recommendation method, device, medium and electronic equipment
CN103136250A (en) Method and device of information change identification, and method and system of information search
CN113327132A (en) Multimedia recommendation method, device, equipment and storage medium
CN114708036B (en) Product promotion system and method based on social network technology

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
TA01 Transfer of patent application right

Effective date of registration: 20230322

Address after: 430061 Floor 1, Building 2, Phase II Office Building, Aquatic Science and Technology Park, No. 1, East Yard, Chagang New Village, Wuchang District, Wuhan, Hubei Province

Applicant after: China Television Zhongguang International Media (Wuhan) Co.,Ltd.

Address before: 100080 No.46, 1st floor, building 1, No.64 West Street, Haidian District, Beijing

Applicant before: Beijing Hunter Information Technology Co.,Ltd.

TA01 Transfer of patent application right
GR01 Patent grant
GR01 Patent grant