CN112104714A - Accurate pushing method based on user interaction element weight - Google Patents
Accurate pushing method based on user interaction element weight Download PDFInfo
- Publication number
- CN112104714A CN112104714A CN202010895208.XA CN202010895208A CN112104714A CN 112104714 A CN112104714 A CN 112104714A CN 202010895208 A CN202010895208 A CN 202010895208A CN 112104714 A CN112104714 A CN 112104714A
- Authority
- CN
- China
- Prior art keywords
- user
- big data
- behavior
- user interaction
- pushing
- 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
- 230000003993 interaction Effects 0.000 title claims abstract description 39
- 238000000034 method Methods 0.000 title claims abstract description 26
- 238000004458 analytical method Methods 0.000 claims abstract description 17
- 230000002452 interceptive effect Effects 0.000 claims abstract description 10
- 238000012216 screening Methods 0.000 claims description 7
- 230000001737 promoting effect Effects 0.000 claims description 6
- 230000006399 behavior Effects 0.000 abstract description 57
- 238000007405 data analysis Methods 0.000 abstract description 4
- 230000009286 beneficial effect Effects 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/55—Push-based network services
-
- 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
- G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
-
- 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
- G06F16/955—Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/52—Network services specially adapted for the location of the user terminal
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)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Information Transfer Between Computers (AREA)
Abstract
The invention relates to the technical field of big data analysis, in particular to a weight accurate pushing method based on user interaction elements, which comprises the following steps: s1, an interactive element acquisition module acquires behavior big data generated by user interactive element weight; s2, the storage module stores the behavior big data to a cloud server; s3, analyzing the stored behavior big data by an analysis module to obtain a user portrait representing the behavior habit of the user; and S4, the pushing module acquires the website content with the correlation degree reaching the specific threshold value with the user portrait and pushes the website content to the user terminal. The method has the advantages of collecting the weight retrieval key words of the user interaction elements, browsing page records, geographic positions, login times and cooperation/transaction times, carrying out big data analysis on user behaviors, obtaining behavior habits and preferences of users, accurately pushing website contents related to the user habit preferences, and being beneficial to media popularization.
Description
Technical Field
The invention relates to the technical field of big data analysis, in particular to a weight accurate pushing method based on user interaction elements.
Background
With the rapid development of internet technology, people increasingly use the internet to obtain information; in a media community website, a lot of websites intelligently push contents which are interesting to users according to the browsing behaviors of the users.
The chinese patent No. CN104426977A provides a method for actively and accurately pushing information for private network users, which can effectively guide user traffic and obtain information required by the private network users. A method for realizing active and accurate information push aiming at private network users comprises the following steps: a) the private network address client initiates an access request to a target server, and an information push analysis server acquires access flow from an provincial exit link; b) the information push application server receives the access flow, breaks the connection between the private network address client and the target server, and sends a FAKE script to the private network address client; c) the private network address client executes the FAKE script, re-accesses the target server, simultaneously sends a GET request packet to the information push content server, and synchronizes data to the information push content server; d) and sending the target information and the push content to a private network address client.
However, one method for realizing active and accurate information push for a private network user only analyzes the use preference and the favorite content type of the user according to the browsing history and the click history of the user, is insufficient in utilization of big data of the user, is relatively single in analysis, and is not easy to customize push content and popularize media for the user.
Disclosure of Invention
The invention aims to provide a weight accurate pushing method based on user interaction elements, and aims to solve the problems that the use preference and the favorite content type of a user are analyzed only according to the browsing history and the click history of the user, the utilization of big data of the user is insufficient, the analysis is single, and the pushing content and the media promotion are not easy to customize aiming at the user individuation.
The technical scheme of the invention is as follows: a weight accurate pushing method based on user interaction elements comprises the following steps:
s1, an interactive element acquisition module acquires behavior big data generated by user interactive element weight;
s2, the storage module stores the behavior big data to a cloud server;
s3, analyzing the stored behavior big data by an analysis module to obtain a user portrait representing the behavior habit of the user;
and S4, the pushing module acquires the website content with the correlation degree reaching the specific threshold value with the user portrait and pushes the website content to the user terminal.
Further, the user interaction element includes: search keywords, browsing page records, geographic location, login times, and collaboration/deal times.
Further, the storage module stores the behavior big data to a cloud server, the analysis module analyzes the behavior big data to obtain a user portrait representing the behavior habit of the user, the behavior big data is analyzed by the cloud server to obtain a search keyword, a browse page record, a geographic position, a login frequency and a cooperation/transaction frequency, and the user portrait representing the behavior habit of the user is formed according to the search keyword, the browse page record, the geographic position, the login frequency and the cooperation/transaction frequency.
Further, the acquiring of the website content with the user portrait relevance reaching a specific threshold includes screening website content with the user portrait relevance exceeding half from a server storing website data browsed by the user, where the website content includes: news content/promotional content.
Further, the pushing the website content to the user terminal includes: and pushing the website content to the user terminal at regular time in a form of pushing a plurality of pieces each time.
Further, the terminal is: a computer, a tablet, or a smartphone.
Further, the user interaction element analysis module is specifically configured to: and storing the behavior big data into a cloud server, analyzing the behavior big data by using the cloud server to obtain a retrieval keyword, a browsing page record, a geographical position, a login frequency and a cooperation/transaction frequency, and forming a user portrait representing the behavior habit of the user according to the retrieval keyword, the browsing page record, the geographical position, the login frequency and the cooperation/transaction frequency.
Further, the user interaction element acquisition module is specifically configured to: the behavior big data is used for collecting behavior big data generated by a user interaction element, and the behavior big data of the user interaction element comprises: search keywords, browsing page records, geographic location, login times, and collaboration/deal times.
Further, the storage module is specifically configured to: the behavior big data is stored in a cloud server, and the analysis module is specifically used for: and storing the behavior big data, and analyzing the behavior big data to obtain a user portrait representing the behavior habit of the user.
Further, the pushing module is specifically configured to: screening website contents with the relevance degree more than half of that of the user portrait from a server storing website data browsed by a user, and pushing the website contents to a user terminal, wherein the website contents comprise: news content and/or promotional content.
The invention provides a weight accurate pushing method based on user interaction elements through improvement, and compared with the prior art, the weight accurate pushing method based on the user interaction elements has the following improvement and advantages:
(1) according to the method, the user interaction element weight retrieval key words, the browsing page records, the geographic positions, the login times and the cooperation/transaction times are collected, the big data analysis of the user behaviors is carried out, the behavior habits and the preferences of the user are obtained, the website contents related to the user habit preferences are accurately pushed, and the media promotion is facilitated.
(2) The storage module can store the behavior big data to the cloud server, is simple and efficient to manage, and is suitable for the development trend of internet big data cloud computing.
(3) The interactive element acquisition module acquires retrieval keywords, browsing page records, geographic positions, login times and cooperation/transaction times behavior big data generated by the user interactive element weight.
(4) The analysis module analyzes the behavior big data by using the cloud server to obtain a search keyword, a browsing page record, a geographic position, a login frequency and a cooperation/transaction frequency, and forms a user portrait representing the behavior habit of the user according to the search keyword, the browsing page record, the geographic position, the login frequency and the cooperation/transaction frequency.
Drawings
The invention is further explained below with reference to the figures and examples:
FIG. 1 is a schematic view of the flow structure of the present invention;
FIG. 2 is a schematic diagram of an interactive element structure according to the present invention.
Detailed Description
The present invention will be described in detail with reference to fig. 1 to 2, and the technical solutions in the embodiments of the present invention will be clearly and completely described, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
The invention provides a method for accurately pushing based on user interaction element weight through improvement, as shown in fig. 1-2, comprising the following steps:
s1, an interactive element acquisition module acquires behavior big data generated by user interactive element weight;
s2, the storage module stores the behavior big data to a cloud server;
s3, analyzing the stored behavior big data by an analysis module to obtain a user portrait representing the behavior habit of the user;
and S4, the pushing module acquires the website content with the correlation degree reaching the specific threshold value with the user portrait and pushes the website content to the user terminal.
Further, the user interaction elements include: search keywords, browsing page records, geographic location, login times, and collaboration/deal times.
Further, the storage module stores the behavior big data to a cloud server, the analysis module analyzes the behavior big data to obtain a user portrait representing the behavior habit of the user, the behavior big data is analyzed by the cloud server to obtain a search keyword, a browse page record, a geographic position, a login frequency and a cooperation/transaction frequency, and the user portrait representing the behavior habit of the user is formed according to the search keyword, the browse page record, the geographic position, the login frequency and the cooperation/transaction frequency.
Further, acquiring the website content with the user portrait relevance reaching a specific threshold value comprises screening the website content with the user portrait relevance exceeding half from a server storing website data browsed by the user, wherein the website content comprises: news content/promotional content.
Further, pushing the website content to the user terminal includes: and pushing the website content to the user terminal regularly in a form of pushing a plurality of pieces each time.
Further, the terminal is: a computer, a tablet, or a smartphone.
Further, the user interaction element analysis module is specifically configured to: and storing the behavior big data into a cloud server, analyzing the behavior big data by using the cloud server to obtain a retrieval keyword, a browsing page record, a geographic position, a login frequency and a cooperation/transaction frequency, and forming a user portrait representing the behavior habit of the user according to the retrieval keyword, the browsing page record, the geographic position, the login frequency and the cooperation/transaction frequency.
Further, the user interaction element acquisition module is specifically configured to: the behavior big data is used for collecting behavior big data generated by the user interaction elements, and the behavior big data of the user interaction elements comprises: search keywords, browsing page records, geographic location, login times, and collaboration/deal times.
Further, the storage module is specifically configured to: the behavior big data is stored in a cloud server, and the analysis module is specifically used for: and storing the behavior big data, and analyzing the behavior big data to obtain a user portrait representing the behavior habit of the user.
Further, the pushing module is specifically configured to: the method comprises the steps of screening website contents with the relevance degree more than half of that of a user portrait from a server for storing website data browsed by a user, and pushing the website contents to a user terminal, wherein the website contents comprise: news content and/or promotional content.
The working principle of the invention is as follows: the user interaction element acquisition module is specifically used for acquiring behavior big data generated by user interaction elements, and the behavior big data of the user interaction elements comprises: the system comprises a retrieval key word, a browsing page record, a geographic position, a login frequency and a cooperation/transaction frequency, wherein the storage module is specifically used for storing behavior big data to a cloud server, the analysis module is specifically used for storing the behavior big data to the cloud server, the cloud server is used for analyzing the behavior big data to obtain the retrieval key word, the browsing page record, the geographic position, the login frequency and the cooperation/transaction frequency, a user portrait representing a user behavior habit is formed according to the retrieval key word, the browsing page record, the geographic position, the login frequency and the cooperation/transaction frequency, and a pushing module is specifically used for screening website contents with the association degree of more than half of the website contents with the user portrait from a server storing the website data browsed by a user, and the website contents comprise: news content and/or promotion content, at last, the website content is pushed to the user terminal, in a form of pushing a plurality of items at each time, the website content is pushed to the user terminal at regular time, and the terminal is as follows: a computer, a tablet, or a smartphone.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method for accurately pushing weights based on user interaction elements is characterized by comprising the following steps: the method comprises the following steps:
s1, an interactive element acquisition module: acquiring behavior big data generated by the weight of the user interaction element;
s2, a storage module: storing the behavior big data to a cloud server;
s3, an analysis module: analyzing the stored behavior big data to obtain a user portrait representing the behavior habit of the user;
s4, a pushing module: and acquiring the website content with the correlation degree with the user portrait reaching a specific threshold value, and pushing the website content to the user terminal.
2. The accurate pushing method based on the user interaction element weight according to claim 1, wherein: the user interaction elements include: search keywords, browsing page records, geographic location, login times, and collaboration/deal times.
3. The accurate pushing method based on the user interaction element weight according to claim 1, wherein: the storage module stores the behavior big data to a cloud server, the analysis module analyzes the behavior big data to obtain a user portrait representing the behavior habit of the user, the behavior big data is analyzed by the cloud server to obtain a search keyword, a browse page record, a geographic position, a login frequency and a cooperation/transaction frequency, and the user portrait representing the behavior habit of the user is formed according to the search keyword, the browse page record, the geographic position, the login frequency and the cooperation/transaction frequency.
4. The accurate pushing method based on the user interaction element weight according to claim 3, wherein: the acquiring of the website content with the user portrait relevance reaching a specific threshold comprises screening website content with the user portrait relevance exceeding half from a server storing website data browsed by a user, wherein the website content comprises: news content/promotional content.
5. The accurate pushing method based on the user interaction element weight according to claim 4, wherein: the pushing the website content to the user terminal includes: and pushing the website content to the user terminal at regular time in a form of pushing a plurality of pieces each time.
6. The accurate pushing method based on the user interaction element weight according to claim 1, wherein: the terminal is as follows: a computer, a tablet, or a smartphone.
7. The accurate pushing method based on the user interaction element weight according to claim 1, wherein: the user analysis module is specifically configured to: and storing the behavior big data into a cloud server, analyzing the behavior big data by using the cloud server to obtain a retrieval keyword, a browsing page record, a geographical position, a login frequency and a cooperation/transaction frequency, and forming a user portrait representing the behavior habit of the user according to the retrieval keyword, the browsing page record, the geographical position, the login frequency and the cooperation/transaction frequency.
8. The accurate pushing method based on the user interaction element weight according to claim 1, wherein: the user interaction element acquisition module is specifically configured to: the behavior big data is used for collecting behavior big data generated by a user interaction element, and the behavior big data of the user interaction element comprises: search keywords, browsing page records, geographic location, login times, and collaboration/deal times.
9. The accurate pushing method based on the user interaction element weight according to claim 1, wherein: the storage module is specifically configured to: and storing the behavior big data to a cloud server.
10. The accurate pushing method based on the user interaction element weight according to claim 1, wherein: the pushing module is specifically configured to: screening website contents with the relevance degree more than half of that of the user portrait from a server storing website data browsed by a user, and pushing the website contents to a user terminal, wherein the website contents comprise: news content and/or promotional content.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010895208.XA CN112104714A (en) | 2020-08-31 | 2020-08-31 | Accurate pushing method based on user interaction element weight |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010895208.XA CN112104714A (en) | 2020-08-31 | 2020-08-31 | Accurate pushing method based on user interaction element weight |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112104714A true CN112104714A (en) | 2020-12-18 |
Family
ID=73756754
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010895208.XA Pending CN112104714A (en) | 2020-08-31 | 2020-08-31 | Accurate pushing method based on user interaction element weight |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112104714A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114625975A (en) * | 2022-05-16 | 2022-06-14 | 山东省科院易达科技咨询有限公司 | Knowledge graph-based customer behavior analysis system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102750334A (en) * | 2012-06-01 | 2012-10-24 | 北京市农林科学院农业科技信息研究所 | Agricultural information accurate propelling method based on data mining (DM) |
| CN102804735A (en) * | 2011-03-17 | 2012-11-28 | 广州市动景计算机科技有限公司 | Browser pre-fetching method and system thereof |
| WO2018157818A1 (en) * | 2017-03-02 | 2018-09-07 | 广州市动景计算机科技有限公司 | Method and apparatus for inferring preference of user, terminal device, and storage medium |
| CN109033441A (en) * | 2018-08-16 | 2018-12-18 | 安徽大尺度网络传媒有限公司 | A kind of method for pushing and device based on big data analysis |
| CN111581513A (en) * | 2020-05-07 | 2020-08-25 | 安徽龙讯信息科技有限公司 | Website intelligent information aggregation system |
-
2020
- 2020-08-31 CN CN202010895208.XA patent/CN112104714A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102804735A (en) * | 2011-03-17 | 2012-11-28 | 广州市动景计算机科技有限公司 | Browser pre-fetching method and system thereof |
| CN102750334A (en) * | 2012-06-01 | 2012-10-24 | 北京市农林科学院农业科技信息研究所 | Agricultural information accurate propelling method based on data mining (DM) |
| WO2018157818A1 (en) * | 2017-03-02 | 2018-09-07 | 广州市动景计算机科技有限公司 | Method and apparatus for inferring preference of user, terminal device, and storage medium |
| CN109033441A (en) * | 2018-08-16 | 2018-12-18 | 安徽大尺度网络传媒有限公司 | A kind of method for pushing and device based on big data analysis |
| CN111581513A (en) * | 2020-05-07 | 2020-08-25 | 安徽龙讯信息科技有限公司 | Website intelligent information aggregation system |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114625975A (en) * | 2022-05-16 | 2022-06-14 | 山东省科院易达科技咨询有限公司 | Knowledge graph-based customer behavior analysis system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US9971842B2 (en) | Computerized systems and methods for generating a dynamic web page based on retrieved content | |
| US8713003B2 (en) | System and method for ranking content and applications through human assistance | |
| US20180232362A1 (en) | Method and system relating to sentiment analysis of electronic content | |
| US9712588B1 (en) | Generating a stream of content for a channel | |
| CN102708174B (en) | Method and device for displaying rich media information in browser | |
| US8060492B2 (en) | System and method for generation of URL based context queries | |
| CN100541495C (en) | A kind of searching method of individual searching engine | |
| CN111008265B (en) | Enterprise information searching method and device | |
| CN108874812B (en) | Data processing method, server and computer storage medium | |
| US20100125604A1 (en) | System and method for url based query for retrieving data related to a context | |
| EP2579167A1 (en) | Method for active information push and server therefor | |
| US20100125569A1 (en) | System and method for autohyperlinking and navigation in url based context queries | |
| US20100125605A1 (en) | System and method for data privacy in url based context queries | |
| US8868570B1 (en) | Selection and display of online content items | |
| CN102037464A (en) | Search results with most clicked next objects | |
| CN102521251A (en) | Method for directly realizing personalized search, device for realizing method, and search server | |
| Lee et al. | Leveraging microblogging big data with a modified density-based clustering approach for event awareness and topic ranking | |
| US9171045B2 (en) | Recommending queries according to mapping of query communities | |
| US20140250116A1 (en) | Identifying time sensitive ambiguous queries | |
| CN111447575A (en) | Short message pushing method, device, equipment and storage medium | |
| CN104090923A (en) | Method and device for displaying rich media information in browser | |
| Lee et al. | An automatic topic ranking approach for event detection on microblogging messages | |
| Baeza-Yates et al. | The new frontier of web search technology: Seven challenges | |
| CN112104714A (en) | Accurate pushing method based on user interaction element weight | |
| CN101788981A (en) | Deep web mobile search method, server and system |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20201218 |
|
| RJ01 | Rejection of invention patent application after publication |