CN110874767A - Business hall accurate marketing method based on collaborative filtering - Google Patents
Business hall accurate marketing method based on collaborative filtering Download PDFInfo
- Publication number
- CN110874767A CN110874767A CN201911131616.1A CN201911131616A CN110874767A CN 110874767 A CN110874767 A CN 110874767A CN 201911131616 A CN201911131616 A CN 201911131616A CN 110874767 A CN110874767 A CN 110874767A
- Authority
- CN
- China
- Prior art keywords
- user
- information
- data
- internet
- personal
- 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
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Databases & Information Systems (AREA)
- Strategic Management (AREA)
- Finance (AREA)
- Development Economics (AREA)
- Theoretical Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Game Theory and Decision Science (AREA)
- Economics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Information Transfer Between Computers (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a collaborative filtering-based accurate marketing method for a business hall, which relates to the technical fields of recommendation algorithms, social networks, data capture packages, OpenWrt, elastic search and the like. The accurate pushing can utilize the internet surfing information of a client to construct a basic client personal portrait, then utilizes the social network to further perfect the construction of the user personal portrait, and finally utilizes the collaborative filtering algorithm to push business or messages according to the personal requirements of the user, so that the service level and the profitability of a business hall are improved.
Description
Technical Field
The invention relates to technologies such as a recommendation algorithm, a social network, a data packet capturing, OpenWrt and an elastic search, in particular to a business hall accurate marketing method based on collaborative filtering.
Background
A social network is a network structure made up of many nodes and relationships between nodes. A node generally refers to an individual or an organization. Social networks represent various social relationships through which various people or organizations, from casual acquaintances, who have been brought into ubiquitous relationship with closely-coupled family relationships, are connected in series.
The collaborative filtering is to simply recommend information of interest to a user by using the preferences of a group with a certain interest and common experience, and a person gives a considerable response (such as scoring) to the information through a collaborative mechanism and records the response so as to achieve the purpose of filtering and further help others to filter the information.
In the internet era, business promotion in business halls should be different. Adopting the propaganda modes of offline manual customer service introduction or business introduction leaflet issuing and the like can not accurately catch the requirements of customers and has extremely low efficiency. Therefore, an efficient and accurate method for promoting business is urgently needed.
Disclosure of Invention
In order to solve the technical problems, the invention provides a business hall accurate marketing method based on collaborative filtering, which can optimize a service system for the business hall, further shorten the distance between the business hall and a client and the market, and provide more interesting information and business for users.
The technical scheme of the invention is as follows:
the invention aims to provide a business hall accurate marketing method based on collaborative filtering, wherein an AP hot spot device based on OpenWrt is used for building a wireless micro-portal, and a user completes authentication connection hot spots through a mobile phone number, so that the system can initially complete the acquisition of personal information of the user. And then, the data are cross-compared with the real customer data of the business hall, so that the real-name authentication of the user is realized. Intercepting and capturing user internet data is completed by deploying a packet capturing tool in the AP hotspot equipment, and a user internet data set is formed through a series of data format conversion and screening. The method comprises the steps of initially constructing a personal user portrait by using an elastic search, fusing a social network by using real-name information, and completely constructing the user portrait to finish personal behavior analysis of a user. And finally, carrying out accurate marketing on the user by utilizing collaborative filtering recommendation based on the combination of the user and the article. Specifically, according to the preference of all users for the articles or the information, a 'neighbor' user group similar to the taste and the preference of the current user is found, the preference of all users for the articles or the information is used, the similarity between the articles and the articles is found, and then the similar articles are recommended to the users according to the historical preference information of the users. After the recommendation is completed, the following user data, the business hall passenger flow volume and the distribution rule, the online APP download registration condition, various business browsing conditions, the advertisement click condition and the like can be obtained.
The business hall client surfs the internet by connecting the AP hot spot in the business hall, firstly, the client real-name authentication is completed by comparing the client with the data in the business hall client library after the authentication is completed, and then, the client is accurately pushed by utilizing the real-name information, thereby achieving the purpose of accurate marketing. The accurate pushing can utilize the internet surfing information of a client to construct a basic client personal portrait, then utilizes the social network to further perfect the construction of the user personal portrait, and finally utilizes the collaborative filtering algorithm to push business or messages according to the personal requirements of the user, so that the service level and the profitability of a business hall are improved.
The method comprises the following specific steps:
(1) user information collection
The user information collection includes: the first is user authentication information, and the second is internet surfing data of the user. And the user real-name system authentication is completed by comparing the user authentication information with the client database data of the business hall. The user internet data can be automatically transmitted to the server through the script when the data packet reaches a certain amount by deploying a packet capturing tool such as TCPDUMP and the like in the AP hotspot equipment. Format conversion and decryption are carried out on the hexadecimal data of the message, and then some useless formatted message data are removed, so that the user internet data is formed. The MAC address of the internet equipment of the user can be captured in the internet authentication process of the user, and the message also contains the MAC address of the internet equipment of the user, so that the relation between the internet data of the user and the real-name information can be established.
(2) User personal representation construction
The user personal portrait construction is also divided into two parts: firstly, a user personal portrait is preliminarily established based on internet surfing data of the user, and secondly, the establishment of the personal portrait is enriched by utilizing a social network based on real name information of the user. The first step is mainly to use the internet access data set of the user and use keywords with the frequency of appearance in the internet access data set, namely, the keywords appearing in three consecutive days or appearing in more than 3 times in one day, as preference information of the user through the Elasticsearch. After the preliminary preference information of the user is obtained, the service or the information similar to the preference information of the user can be pushed to the client. Then, the real-name system internet information of the user is utilized, the basic personal information (interest, occupation, education degree, sex, age, region and the like) of the user can be obtained from a related social network site (microblog, WeChat, Facebook and the like), so that a fuller personal portrait of the user can be established, and then user behavior analysis can be carried out according to the information such as the content of the user's text, the number of people to read, the number of comments, forwarding, time, place and the like, so that the establishment of the portrait of the user can be enriched, a social relationship network of the user can be established, and more credible group information can be divided.
(3) Accurate pushing
After a user connects an AP hotspot and starts to surf the Internet, the system can acquire an Internet data set of the user, a user portrait can be basically established after preliminary analysis is completed, the personal portrait of the user is analyzed and enriched by utilizing a social network, and similar groups of the user can be expanded (acquired according to user group division information). Therefore, a user-preference weight set can be obtained, and a recommendation model based on collaborative filtering is established. Firstly, similar users of the login users are obtained, the business handling conditions of the similar users are recommended to the login users, and the similar users can be obtained from user group division information and a user-preference weight set; and secondly, finding out the similarity between the services or the information, using the services transacted by the login user before or the browsed information as a recommendation template, and pushing the services or the information similar to the services or the information to the user. And then, continuously changing the recommendation model according to the push effect to improve the recommendation effect.
The invention has the advantages that
1) Compared with the business hall offline promotion business, the online pushing device has the advantages of pertinence, high efficiency, wide pushing range and convenience for users to check.
2) The system has the advantages that the functions are completely integrated, the installation and the deployment of the business hall are convenient, the system can be adopted as long as business promotion requirements exist and the business form is the business hall type, and the application range of the system is wide.
3) Compared with blind service pushing, the method and the device have the advantages that the enterprise image can be improved and the user experience can be improved aiming at the user personalized pushing service.
Drawings
FIG. 1 is a schematic workflow diagram of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer and more complete, the technical solutions in the embodiments of the present invention will be described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention, and based on the embodiments of the present invention, all other embodiments obtained by a person of ordinary skill in the art without creative efforts belong to the scope of the present invention.
According to the invention, the wireless micro-portal is built by using the AP hotspot equipment based on OpenWrt, and the user completes the authentication connection hotspot through the mobile phone number, so that the system can initially complete the acquisition of the personal information of the user. And then, the data are cross-compared with the real customer data of the business hall, so that the real-name authentication of the user is realized. Intercepting and capturing user internet data is completed by deploying a packet capturing tool in the AP hotspot equipment, and a user internet data set is formed through a series of data format conversion and screening. The method comprises the steps of initially constructing a personal user portrait by using an elastic search, fusing a social network by using real-name information, and completely constructing the user portrait to finish personal behavior analysis of a user. And finally, carrying out accurate marketing on the user by utilizing collaborative filtering recommendation based on the combination of the user and the article. Specifically, according to the preference of all users for the articles or the information, a 'neighbor' user group similar to the taste and the preference of the current user is found, the preference of all users for the articles or the information is used, the similarity between the articles and the articles is found, and then the similar articles are recommended to the users according to the historical preference information of the users. After the recommendation is completed, the following user data, the business hall passenger flow volume and the distribution rule, the online APP download registration condition, various business browsing conditions, the advertisement click condition and the like can be obtained. By analyzing the data, parameters of the push model are continuously adjusted, and recommendation accuracy is improved.
As shown in fig. 1, after the user internet device completes identity authentication, the AP hotspot device can access the network normally, and the system records the identity information of the user, the MAC address of the device, and the internet data information of the user.
And then the real information is compared with a real customer information base of a business hall to finish the real information authentication of the user. The network access data comprises the MAC address of the network access equipment, so that the association between the real identity information of the user and the network access information is established.
By analyzing the internet data set of the user, the personal behavior portrait of the user can be established preliminarily, and then the social network analysis is fused, so that the personal portrait of the user can be established completely. And establishing a user recommendation model by utilizing the personal portrait information of the user, and performing personalized recommendation according to user preference. And after the recommendation is completed, the recommendation effect is analyzed, and the recommendation model is continuously changed, so that the recommendation effect is improved.
The above description is only a preferred embodiment of the present invention, and is only used to illustrate the technical solutions of the present invention, and not to limit the protection scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (8)
1. An accurate marketing method of business hall based on collaborative filtering is characterized in that,
the method comprises the steps that the Internet is accessed by connecting AP hot spots in a business hall, after authentication is completed, the authentication is firstly completed by comparing data in a business hall client library, client real-name authentication is completed, and then accurate business pushing is performed on a client by utilizing real-name information;
the basic personal portrait of the client is constructed by utilizing the internet surfing information of the client, then the construction of the personal portrait of the user is further perfected by utilizing a social network, and finally, the business or the message is pushed according to the personal requirement of the user by utilizing a collaborative filtering algorithm.
2. The method of claim 1,
an AP hot spot device based on OpenWrt is used for building a wireless micro portal, and a user completes authentication connection hot spots through a mobile phone number and initially completes acquisition of personal information of the user;
then, the data are cross-compared with the real customer data of the business hall, and the real-name authentication of the user is realized; intercepting and capturing user internet data by deploying a packet capturing tool in the AP hotspot equipment, and forming a user internet data set through data format conversion and screening;
using an elastic search to preliminarily construct a personal user portrait, then fusing a social network by using real-name information, and completely constructing the user portrait to finish personal behavior analysis of a user;
and finally, carrying out accurate marketing on the user by utilizing collaborative filtering recommendation based on the combination of the user and the article.
3. The method of claim 2,
according to the preferences of all users for the articles or the information, finding a 'neighbor' user group similar to the taste and the preferences of the current users and using the preferences of all users for the articles or the information, finding the similarity between the articles and the articles, and then recommending the similar articles to the users according to the historical preference information of the users; after the recommendation is completed, the following user data, the business hall passenger flow volume and distribution rule, the online APP download registration condition, various business browsing conditions and the advertisement click condition can be obtained; by analyzing the data, parameters of the push model are continuously adjusted.
4. The method of claim 3,
the collection of user information includes: firstly, user authentication information and secondly, internet surfing data of the user;
the user real-name system authentication is completed by comparing the user authentication information with the client database data of the business hall;
deploying a packet capturing tool in AP hot spot equipment by using user internet data, and automatically transmitting a data packet to a server through a script; format conversion and decryption are carried out on the hexadecimal data of the message, and then useless formatted message data are removed, so that the user internet data is formed.
5. The method of claim 4,
the MAC address of the internet equipment of the user is captured in the internet authentication process of the user, and the message contains the MAC address of the internet equipment of the user, so that the relation between the internet data of the user and the real-name information can be established.
6. The method of claim 5,
the user personal portrait construction is divided into two parts: firstly, preliminarily establishing a user personal portrait based on internet surfing data of a user, and secondly, enriching establishment of the personal portrait by utilizing a social network based on real name information of the user;
the first step is mainly to use the internet access data set of the user and use keywords with the frequency of appearance in the internet access data set, namely, the keywords appearing in three consecutive days or appearing in more than 3 times in one day, as the preference information of the user through the Elasticsearch; after the preliminary hobby information of the user is obtained, the service or the information similar to the hobby information of the user can be pushed to the client; and then, the basic personal information of the user can be acquired from the related social network site by utilizing the real-name system internet information of the user, so that a richer user personal portrait is established, and then the user behavior analysis is carried out according to the content of the user text pushing, the reading number, the comment times, the forwarding, the time and the place information.
7. The method of claim 6,
after a user connects an AP hotspot and starts to surf the internet, the system can acquire an internet data set of the user, a user portrait can be basically established after preliminary analysis is completed, the personal portrait of the user is analyzed and enriched by utilizing a social network, and similar groups of the user can be expanded, namely the user is acquired according to user group division information; therefore, a user-preference weight set can be obtained, and a recommendation model based on collaborative filtering is established.
8. The method of claim 7,
acquiring similar users of the login users, recommending the business handling conditions of the similar users to the login users, and acquiring the similar users from user group division information and a user-preference weight set;
finding out the similarity between the services or the information, taking the services transacted by the login user before or the browsed information as a recommended template, and pushing the services or the information similar to the services or the information to the user; and then, continuously changing the recommendation model according to the push effect to improve the recommendation effect.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911131616.1A CN110874767A (en) | 2019-11-19 | 2019-11-19 | Business hall accurate marketing method based on collaborative filtering |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911131616.1A CN110874767A (en) | 2019-11-19 | 2019-11-19 | Business hall accurate marketing method based on collaborative filtering |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110874767A true CN110874767A (en) | 2020-03-10 |
Family
ID=69717127
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911131616.1A Pending CN110874767A (en) | 2019-11-19 | 2019-11-19 | Business hall accurate marketing method based on collaborative filtering |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110874767A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111639972A (en) * | 2020-05-29 | 2020-09-08 | 中国联合网络通信集团有限公司 | Multimedia data delivery method and device |
| CN111753207A (en) * | 2020-06-29 | 2020-10-09 | 华东师范大学 | Collaborative filtering model of neural map based on comments |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015013847A1 (en) * | 2013-08-02 | 2015-02-05 | 贾玲 | Information pushing system of wifi terminal and implementation method therefor |
| CN105608171A (en) * | 2015-12-22 | 2016-05-25 | 青岛海贝易通信息技术有限公司 | User portrait construction method |
| CN106504099A (en) * | 2015-09-07 | 2017-03-15 | 国家计算机网络与信息安全管理中心 | A kind of system for building user's portrait |
| CN108304441A (en) * | 2017-11-14 | 2018-07-20 | 腾讯科技（深圳）有限公司 | Network resource recommended method, device, electronic equipment, server and storage medium |
| CN108830439A (en) * | 2018-04-09 | 2018-11-16 | 国网山东省电力公司 | Electricity business hall intelligent Service monitoring method and system based on recognition of face |
| CN108960975A (en) * | 2018-06-15 | 2018-12-07 | 广州麦优网络科技有限公司 | Personalized Precision Marketing Method, server and storage medium based on user's portrait |
| CN109889560A (en) * | 2017-12-14 | 2019-06-14 | 广东电网有限责任公司东莞供电局 | A kind of exchange method and system for the identification of business hall client identity |
| CN110335060A (en) * | 2019-05-20 | 2019-10-15 | 微民保险代理有限公司 | Product information method for pushing, device, storage medium and computer equipment |
-
2019
- 2019-11-19 CN CN201911131616.1A patent/CN110874767A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015013847A1 (en) * | 2013-08-02 | 2015-02-05 | 贾玲 | Information pushing system of wifi terminal and implementation method therefor |
| CN106504099A (en) * | 2015-09-07 | 2017-03-15 | 国家计算机网络与信息安全管理中心 | A kind of system for building user's portrait |
| CN105608171A (en) * | 2015-12-22 | 2016-05-25 | 青岛海贝易通信息技术有限公司 | User portrait construction method |
| CN108304441A (en) * | 2017-11-14 | 2018-07-20 | 腾讯科技（深圳）有限公司 | Network resource recommended method, device, electronic equipment, server and storage medium |
| CN109889560A (en) * | 2017-12-14 | 2019-06-14 | 广东电网有限责任公司东莞供电局 | A kind of exchange method and system for the identification of business hall client identity |
| CN108830439A (en) * | 2018-04-09 | 2018-11-16 | 国网山东省电力公司 | Electricity business hall intelligent Service monitoring method and system based on recognition of face |
| CN108960975A (en) * | 2018-06-15 | 2018-12-07 | 广州麦优网络科技有限公司 | Personalized Precision Marketing Method, server and storage medium based on user's portrait |
| CN110335060A (en) * | 2019-05-20 | 2019-10-15 | 微民保险代理有限公司 | Product information method for pushing, device, storage medium and computer equipment |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111639972A (en) * | 2020-05-29 | 2020-09-08 | 中国联合网络通信集团有限公司 | Multimedia data delivery method and device |
| CN111753207A (en) * | 2020-06-29 | 2020-10-09 | 华东师范大学 | Collaborative filtering model of neural map based on comments |
| CN111753207B (en) * | 2020-06-29 | 2021-03-26 | 华东师范大学 | Collaborative filtering method for neural map based on comments |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20170177586A1 (en) | Methods and systems for social matching | |
| CN103346957B (en) | A kind of system and method according to contact person's message alteration contact head image expression | |
| Ziv et al. | An exploration on mobile social networking: Dodgeball as a case in point | |
| US8843463B2 (en) | Providing content by using a social network | |
| CN103617547B (en) | A kind of business recommended method and system | |
| EP2698967A1 (en) | Social network data mining method for terminal user, and relevant method, device and system | |
| US20090271244A1 (en) | Situation-aware ad-hoc social interaction | |
| US10984452B2 (en) | User/group servicing based on deep network analysis | |
| CN105959325A (en) | Method and system for automatically pushing questionnaire | |
| US20130311283A1 (en) | Data mining method for social network of terminal user and related methods, apparatuses and systems | |
| US10091323B2 (en) | Social discovery feed for facilitating social exploration in social networking environments | |
| CN103559265A (en) | Individualized push method of cell phone client | |
| US20130311482A1 (en) | Multi-user timeline for facilitating social discovery in social networking environments | |
| US11558340B2 (en) | Systems and methods for providing an online platform for facilitating a communication connection between an individual and an elected official | |
| US20140189022A1 (en) | System and method for inputting end-user-selected electronic data, for inputting end-user-selected gradation values of said electronic data, and for creating correlation outputs of said inputted gradation values for optimization of systems and processes | |
| CN107491997A (en) | Information-pushing method and system | |
| CN110874767A (en) | Business hall accurate marketing method based on collaborative filtering | |
| US20130185323A1 (en) | Social network service | |
| CN103678624A (en) | Searching method, searching server, and searching request executing method and terminal | |
| KR20180065395A (en) | System and method for providing advertisement using sns | |
| KR101905012B1 (en) | Sns managed system for integrated management of social network services | |
| JP6947502B2 (en) | How to automatically convert user roles based on user context and its information system | |
| CN109670107B (en) | Stranger social activity recommendation method and system based on big interest data | |
| US9213725B2 (en) | Systems and methods for generating automated social interactions in social networking environments | |
| KR20230021998A (en) | Customized local festival recommendation service and method using user mydata |
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 |