CN112804342A - Personalized recommendation system and method based on user learning behaviors - Google Patents
Personalized recommendation system and method based on user learning behaviors Download PDFInfo
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- CN112804342A CN112804342A CN202110110154.6A CN202110110154A CN112804342A CN 112804342 A CN112804342 A CN 112804342A CN 202110110154 A CN202110110154 A CN 202110110154A CN 112804342 A CN112804342 A CN 112804342A
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- 230000006399 behavior Effects 0.000 title claims abstract description 41
- 238000000034 method Methods 0.000 title claims abstract description 14
- 238000012360 testing method Methods 0.000 claims description 13
- 238000012423 maintenance Methods 0.000 claims description 5
- 238000012544 monitoring process Methods 0.000 claims description 4
- 230000008569 process Effects 0.000 claims description 3
- 230000003993 interaction Effects 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
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- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
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- 230000006872 improvement Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
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- 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
- H04L67/01—Protocols
- H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
- H04L67/025—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP] for remote control or remote monitoring of applications
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- 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
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- 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
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
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- 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
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- 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
- H04L67/56—Provisioning of proxy services
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L69/00—Network arrangements, protocols or services independent of the application payload and not provided for in the other groups of this subclass
- H04L69/16—Implementation or adaptation of Internet protocol [IP], of transmission control protocol [TCP] or of user datagram protocol [UDP]
- H04L69/161—Implementation details of TCP/IP or UDP/IP stack architecture; Specification of modified or new header fields
- H04L69/162—Implementation details of TCP/IP or UDP/IP stack architecture; Specification of modified or new header fields involving adaptations of sockets based mechanisms
Abstract
The invention provides a personalized recommendation system and method based on user learning behaviors. The personalized recommendation system based on the user learning behaviors comprises: the system comprises a service module, an instruction management module and a Socket instruction channel module, wherein the service module is responsible for maintaining learning contents, the instruction management module is responsible for customizing instruction rules, the instruction channel is used for issuing instructions and uploading user behaviors, the service module is communicated with the instruction management module through an API (application programming interface), and the instruction management module is communicated with the instruction channel through a command sending interface. According to the invention, data uploading is carried out through the Socket channel, and the learning content is pushed to the user through the Socket channel according to the preset instruction rule, so that timely and effective recommendation and guidance of the learning content are realized.
Description
Technical Field
The invention relates to the technical field of personalized recommendation systems based on user behaviors, in particular to personalized recommendation system recommendation and a method based on user learning behaviors.
Background
Currently, some similar components already exist in the market, but lack the ability to collect and boot integrally, do not have the ability to push actively, and wait for the client to acquire. For example, in a notification bar message push application, the component only has the capability of pushing messages to the client side by the server side. Moreover, the communication mode is single, and the function of timely collecting feedback of push messages is not provided. For example, in a common big data recommendation scenario, a background acquires data through data collection, then waits for a mobile phone to acquire configuration data of the background regularly through an HTTP request, and then displays the configuration data, and a series of processes such as re-development, release and the like are required each time a module is newly added.
Disclosure of Invention
In view of this, the invention provides a personalized recommendation system based on user learning behaviors, which on one hand uploads data through a Socket channel to solve the problem that the learning behaviors of a client cannot be collected in time in the prior art, and on the other hand, the learning content is pushed to the user through the Socket channel in time through a preset content recommendation rule to realize timely and effective recommendation and guidance of the learning content.
The personalized recommendation system based on the user learning behaviors is characterized by comprising a service module, an instruction management module and a Socket instruction channel module, wherein the service module is responsible for maintaining learning contents, the instruction management module is used for customizing instruction rules, the instruction channel is used for issuing instructions and uploading user behaviors, the service module is communicated with the instruction management module through an API (application programming interface), and the instruction management module is communicated with the instruction channel through an instruction sending interface.
Further, the service module includes an article management module, a course management module and a test paper management module, the article management module is used for maintaining article contents, the course management module is used for maintaining course contents, and the test paper management module is used for maintaining test paper contents.
Further, the instruction rule includes a trigger condition, a guidance content, and an instruction result, where the trigger condition is a preset condition, the guidance content user completes the content displayed after the trigger condition, the instruction result is a feedback result and a reward after the content displayed is completed, and the trigger condition includes assertion, delay, circulation, and monitoring.
Further, the Socket channel is implemented by a cluster, the cluster includes a Socket adapter and a plurality of Socket servers, the Socket adapter is used for distributing instructions to the Socket servers, and the Socket servers are used for pushing the instructions to agent software of the application program.
Further, the personalized recommendation system further comprises a client, and the client is used for generating user behaviors and receiving instructions of the Socket instruction channel.
The invention provides a personalized recommendation method based on user learning behaviors, which comprises the following steps:
managing learning content;
acquiring learning behavior data of a user through a Socket channel;
the method comprises the steps that recommended content is sent to a user through a Socket channel according to a preset instruction rule, the Socket channel is realized through a cluster, the cluster comprises a Socket adapter and a plurality of Socket servers, the Socket adapter is used for distributing instructions to the Socket servers, and the Socket servers are used for pushing the instructions to agent software of an application program.
Further, the proxy software is HAProxy.
Further, the above-mentioned management learning content refers to the content including articles, test papers and courses maintained at the server.
Further, the obtaining of the user learning behavior refers to obtaining the learning behavior of the user at the client through the Socket channel in the client learning process, and includes article reading records, examination paper answering conditions and course learning progress.
Further, the instruction rule includes a trigger condition, a guidance content, and an instruction result, where the trigger condition is a preset condition, the guidance content user completes the content displayed after the trigger condition, the instruction result is a feedback result and a reward after the content displayed is completed, and the trigger condition includes assertion, delay, circulation, and monitoring.
Compared with the prior art, the invention has the beneficial effects that: firstly, the personalized recommendation system based on the user learning behaviors realizes the timely collection of the user behaviors through a Socket channel, and plays a role in timely collecting information; secondly, the behavior of the mobile phone end can be actively triggered through an instruction system instead of waiting for the mobile phone end to acquire background data through an HTTP request and then triggering the behavior, so that the effect of actively collecting information is achieved; and thirdly, actively pushing through the server. So that the control of the service part to the mobile terminal becomes more active.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a schematic structural diagram of a personalized recommendation system based on user learning behaviors, provided by an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 shows a personalized recommendation system based on user learning behaviors according to an embodiment of the present invention. The personalized recommendation system based on the user learning behaviors comprises: the system comprises a service part, an instruction channel and a mobile terminal.
The first part of business, including article management module, course management module and examination paper management module, is mainly the maintenance of business entity, such as course, article content, attribute maintenance. The client notifies the business segment through the API interface while watching the video in the learning session. The service part forwards the request to the instruction part through an interface of the API provided by the instruction part. The traffic segment may control whether to turn on the command.
The second part of instruction part verifies whether the current service sets an instruction or not through the instruction management module, and formats the triggering condition, the content and the result of the instruction. And if the instruction is started, pushing the instruction to an instruction channel in a message pushing mode.
A third part of instruction channels, wherein when instruction contents are received, the instruction contents are stored in a Socket Adapter (Socket Adapter) through a sending instruction interface; secondly, distributing the instruction to each Socket Server, such as Socket Server1, Socket Server2 and Socket Server3, through the Socket adapter distribution node; and finally, pushing the data to the application agent software through each server. The application proxy software is HAproxy. The Socket Adapter serves as a Socket adaptation layer and has the function of accurately finding a service node Socket server where a sending client is located in a distributed scene. The instruction channels are distributed (that is, in a manner of supporting clusters), and a scene commonly used by a large number of clients can be supported. The instruction is transmitted through the Socket instruction channel, so that the effect of quickly transmitting the instruction can be achieved, and the timeliness of the recommendation system is greatly improved.
The fourth part of the mobile terminal comprises a Socket Client (Socket Client) and an article module. And the Socket client analyzes the acquired instruction content, judges according to the attribute in the instruction content and dynamically changes the displayed content according to the instruction content. For example: and displaying the test paper pre-configured by the service part, and uploading the learning behavior data of the user, such as the answer condition of the test paper, to the instruction channel end through the Socket channel. Content modules of different content types including but not limited to articles, courses and test papers can be customized according to the needs of specific user groups, and are used for showing relevant business contents to users, and the users can learn and interact through the relevant content modules to generate user learning behavior data.
In the whole system, the interaction between the third part of command channels and the fourth part of mobile terminals is used as interaction in a Socket connection mode, and data are circulated in the two modules in a duplex mode. The interaction modes among the first part of service module, the second part of instruction management module and the third part of instruction channel module are all interaction in the form of API (application program interface).
The Socket channel can carry out long-term transmission on user learning behavior data generated by the client, and transmits the user learning behavior data to the instruction part and the service part for analyzing and formulating instruction rules so as to further optimize the content recommendation effect. The user behavior data comprises the learning conditions of articles, courses and test papers. Clients can customize clients for different content types including but not limited to articles, courses and test papers, according to the needs of a particular user population.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention; various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. The personalized recommendation system based on the user learning behaviors is characterized by comprising a service module, an instruction management module and a Socket instruction channel module, wherein the service module is responsible for maintaining learning contents, the instruction management module is responsible for customizing instruction rules, the instruction channel is used for issuing instructions and uploading user behaviors, the service module and the instruction management module are communicated through an API (application programming interface), and the instruction management module and the instruction channel are communicated through a sending instruction interface.
2. The system of claim 1, wherein the business module comprises an article management module, a course management module and a test paper management module, the article management module is used for article content maintenance, the course management module is used for course content maintenance, and the test paper management module is used for test paper content maintenance.
3. The personalized recommendation system based on user learning behavior according to claim 1, wherein the instruction rule includes a trigger condition, a guidance content and an instruction result, the trigger condition is a preset condition, the guidance content user completes the displayed content after the trigger condition, the instruction result is a feedback result and a reward after the displayed content is completed, and the trigger condition includes assertion, delay, circulation and monitoring.
4. The personalized recommendation system based on the user learning behavior as claimed in claim 1, wherein the Socket channel is implemented by a cluster, the cluster comprises a Socket adapter and a plurality of Socket servers, the Socket adapter is used for distributing instructions to the Socket servers, and the Socket servers are used for pushing the instructions to agent software of an application program.
5. The personalized recommendation system based on user learning behaviors as claimed in claim 1, further comprising a client for generating user behaviors and receiving instructions of the Socket instruction channel.
6. A personalized recommendation method based on user learning behaviors is characterized by comprising the following steps:
managing learning content;
acquiring learning behavior data of a user through a Socket channel;
the method comprises the steps that recommended content is sent to a user through a Socket channel according to a preset instruction rule, the Socket channel is realized through a cluster, the cluster comprises a Socket adapter and a plurality of Socket servers, the Socket adapter is used for distributing instructions to the Socket servers, and the Socket servers are used for pushing the instructions to agent software of an application program.
7. The method as claimed in claim 6, wherein the step of managing learning content refers to maintaining content including articles, test papers and courses on the server side.
8. The personalized recommendation method based on the user learning behaviors as claimed in claim 6, wherein the obtaining of the user learning behaviors refers to obtaining the user learning behaviors at the client through the Socket channel in the process of learning by the client, wherein the user learning behaviors include article reading records, examination paper answering conditions and course learning progress.
9. The personalized recommendation method based on the user learning behavior according to claim 6, wherein the instruction rule includes a trigger condition, a guidance content and an instruction result, the trigger condition is a preset condition, the guidance content user completes the content displayed after the trigger condition, the instruction result is a feedback result and a reward after the content displayed is completed, and the trigger condition includes assertion, delay, circulation and monitoring.
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| CN202110110154.6A CN112804342A (en) | 2021-01-27 | 2021-01-27 | Personalized recommendation system and method based on user learning behaviors |
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN103281366A (en) * | 2013-05-21 | 2013-09-04 | 山东地纬计算机软件有限公司 | Embedded agency monitoring device and method supporting real-time operating state acquiring |
| WO2015192530A1 (en) * | 2014-06-17 | 2015-12-23 | 中兴通讯股份有限公司 | Method and device for recommending course in online education |
| CN109460517A (en) * | 2018-11-19 | 2019-03-12 | 苏州友教习亦教育科技有限公司 | Personalized information push method based on Cloud Server |
| CN110688574A (en) * | 2019-09-25 | 2020-01-14 | 湖南新云网科技有限公司 | Learning content recommendation method, system, device and readable storage medium |
| US20200097608A1 (en) * | 2018-09-24 | 2020-03-26 | Salesforce.Com, Inc. | Method and system for service agent assistance of article recommendations to a customer in an app session |
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- 2021-01-27 CN CN202110110154.6A patent/CN112804342A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103281366A (en) * | 2013-05-21 | 2013-09-04 | 山东地纬计算机软件有限公司 | Embedded agency monitoring device and method supporting real-time operating state acquiring |
| WO2015192530A1 (en) * | 2014-06-17 | 2015-12-23 | 中兴通讯股份有限公司 | Method and device for recommending course in online education |
| US20200097608A1 (en) * | 2018-09-24 | 2020-03-26 | Salesforce.Com, Inc. | Method and system for service agent assistance of article recommendations to a customer in an app session |
| CN109460517A (en) * | 2018-11-19 | 2019-03-12 | 苏州友教习亦教育科技有限公司 | Personalized information push method based on Cloud Server |
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