CN109977312A - A kind of knowledge base recommender system based on content tab - Google Patents
A kind of knowledge base recommender system based on content tab Download PDFInfo
- Publication number
- CN109977312A CN109977312A CN201910236942.2A CN201910236942A CN109977312A CN 109977312 A CN109977312 A CN 109977312A CN 201910236942 A CN201910236942 A CN 201910236942A CN 109977312 A CN109977312 A CN 109977312A
- Authority
- CN
- China
- Prior art keywords
- content
- data
- user
- knowledge
- knowledge base
- 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
Links
- 238000003860 storage Methods 0.000 claims abstract description 32
- 238000007405 data analysis Methods 0.000 claims abstract description 9
- 238000004891 communication Methods 0.000 claims abstract description 6
- 238000000034 method Methods 0.000 claims description 17
- 230000008569 process Effects 0.000 claims description 9
- 239000000284 extract Substances 0.000 claims description 6
- 238000005516 engineering process Methods 0.000 claims description 5
- 239000012141 concentrate Substances 0.000 claims description 3
- 239000000203 mixture Substances 0.000 claims description 3
- 230000003542 behavioural effect Effects 0.000 claims description 2
- 238000011156 evaluation Methods 0.000 claims 1
- 230000006399 behavior Effects 0.000 description 36
- 238000012545 processing Methods 0.000 description 12
- 238000004590 computer program Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 7
- 230000005540 biological transmission Effects 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 238000009412 basement excavation Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000000151 deposition Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Landscapes
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention proposes a kind of knowledge base recommender system based on content tab, recommendation is recommended user, the recommender system includes server-side；And client terminal device, communication link is established by network and the server-side；The server-side includes: data acquisition facility, obtains the behavior property data of one or more users, wherein the behavior property data include user base information collection, user interest information collection and field feedback collection；Data analysis set-up receives the behavior property data, and adds corresponding content tab after analyzing the attribute data, and behavior property data corresponding with the content tab are sent to knowledge base content storage devices；Knowledge base content storage devices, including multiple knowledge base content store databases and knowledge pond, after the knowledge base content storage devices receive the behavior property data, corresponding behavior property data are extracted or stored in the knowledge pond according to the content tab；Server-side recommendation apparatus, by the content push for needing to push to user.Recommender system of the invention improves the validity and accuracy of system data push.
Description
Technical field
The present invention relates to technical field of data processing more particularly to a kind of knowledge base recommender systems based on content tab.
Background technique
Since in recent years, with Internet information technique rapid development and using Internet user quantity it is continuous
Increase, the data yield in internet is extremely huge.These data can be used as the weight of real life, experiment etc. a bit
Refer to, also have plenty of and can be used as the basis of scientific research etc. by amount, then these data how rationally, effective use at
For the important topic of current scientist research.By academia and industry years of researches and application, these are counted
It is also graduallyd mature according to the excavation of surcharge and using technology, most important is exactly (such as to exist in e-commerce, online information application
Line news, Online Music, online video display) etc. fields.
The birth of search engine technique, solves the puzzlement in terms of filtering user information to a certain extent, and user can be with
Information required for it is found by keyword, improves the speed of filter information and the efficiency of use information, but it is also
It is that there is certain limitation, these are all that user actively goes to obtain information, however, in daily life, actively obtaining information
When, unless the assurance of very familiar or keyword is very accurate, be possible to be directly obtained relevant content data, together
When, present network data is many and diverse, the interference informations such as various advertisements is flooded with, so that user produces when obtaining the data that it is needed
Great puzzlement is given birth to.Therefore, how by the behavior property of user, data required for actively pushing it to user become
One of technical problems to be solved needed for the present invention, meanwhile, based on the construction of present individual credit system, in effective time model
In enclosing, its possible bad credit information is pushed to user in time, accurately, so that after user handles related content in time
Rebuilding personal credit becomes another technical problem that present invention needs solve.
Summary of the invention
In order to solve the above technical problems, the invention proposes a kind of knowledge base recommender system based on content tab, not only
The content that it may pay close attention to according to the behavioral data active push of user can also assess data according to user credit and push in time
Related data, so that effectively help user handles the potential risk of its own well.
According to an aspect of the present invention, the knowledge base recommender system based on content tab that the present invention provides a kind of, will
Recommendation recommends user, which is characterized in that
The recommender system includes server-side；And
Client terminal device establishes communication link by network and the server-side；
The server-side includes:
Data acquisition facility obtains the behavior property data of one or more users, wherein the behavior property data packet
Include user base information collection, user interest information collection and field feedback collection；
Data analysis set-up receives the behavior property data, and adds after analyzing the attribute data corresponding
Content tab, behavior property data corresponding with the content tab are sent to knowledge base content storage devices；
Knowledge base content storage devices, including multiple knowledge base content store databases and knowledge pond, the knowledge base
After content storage devices receive the behavior property data, extracts or deposit in the knowledge pond according to the content tab
Store up corresponding behavior property data；
Server-side recommendation apparatus, by the content push for needing to push to user.
Preferably, the data acquisition facility obtains the behavior property data of one or more users, wherein the row
Include user base information collection, user interest information collection and field feedback collection for attribute data, specifically includes, the number
The behavior that user browses webpage, browsing app client or circle of friends is actively obtained by web crawlers technology according to acquisition device
Data obtain the behavior property data of one or more users, alternatively, user actively sends to the knowledge base recommender system
The content recommendation request of behavior property data based on the user, wherein the content recommendation request includes institute's request content
Classification, format, size information.
Preferably, the data analysis set-up receives the behavior property data, and analyzes the attribute data
After add corresponding content tab, by behavior property data corresponding with the content tab be sent to knowledge base content storage dress
It sets, is realized by following process:
After (1-1) receives the behavior property data, judge which kind of type is the behavior property data belong to, if it is
User base information collection or user interest information collection, enter step (1-2), if it is field feedback collection, in the use
Family feedback information concentrates addition feedback content label and enters step (1-4)；
After what (1-2) was got is user base information collection or user interest information collection, user base information is extracted
The customer attribute information that collection or user interest information are concentrated；
(1-3) described data acquisition facility analyzes the customer attribute information, and transfers in knowledge base content store
Personal credit file data, judge the personal credit data of acquired one or more users whether in reasonable range,
If personal credit pre-warning content label is not added in reasonable range, otherwise in the user behavior attribute data
Add the personal credit data label of the user；
(1-4) will be by user base information collection, user interest information collection or user feedback with corresponding contents label
The behavior property data for one or more compositions that information is concentrated are sent to knowledge repository.
Preferably, the knowledge base content storage devices, including multiple knowledge bases by three corresponding contents tag identifiers
Content store database, wherein the corresponding user base content store database of user base information set content label, Yong Huxing
The corresponding user interest content store database of interesting information set content label and field feedback set content label are corresponding
Field feedback content store database.
Preferably, the corresponding user base content store database of the user base information set content label, for depositing
User's initial content is stored up, the user base information is user in underground pipe net webpage, app client or circle of friends, with
The basic information collection that machine generates obtains corresponding content in the knowledge pond by the keyword selected at random, by the part
Content recommends client by the server-side recommendation apparatus；
The corresponding user interest information content store database of the user interest information set content label, storage is base
In the content-data of user interest, which is the root based on user when browsing webpage, APP client or circle of friends
Believe according to being browsed in the higher content of attention rate in the attention rate of webpage information within a preset time, APP client or circle of friends
The concern time stopped when breath extracts the interest information collection of user, to pass through institute after obtaining corresponding content in knowledge pond
It states server-side recommendation apparatus and recommends client；
The corresponding field feedback content store database of the field feedback set content label, storage is base
The data attention rate data of the score data or expectation acquisition based on recommendation information after user feedback, the partial data are
User is to the feedback data of the attention rate of the data pushed at random, alternatively, the partial data is the recommendation number of user's activly request
According to feedback data, according to passing through institute after content corresponding in knowledge pond described in the data acquisition in the field feedback collection
It states server-side recommendation apparatus and recommends client.
Preferably, the knowledge pond is stored with various types of contents, further includes timing means, period in the knowledge pond
The acquisition personal credit data of property, and undesirable credit record information is stored in knowledge pond in the personal credit data that will acquire
In, by the data changed in personal credit data also real-time update.
Preferably, described to obtain corresponding content from knowledge pond and recommend to user by server-side recommendation apparatus, also wrap
It includes, the knowledge pond further includes sorter, and the sorter carries out matching degree calculating, acquisition to the content that needs are recommended
Recommendation list with degree sequence, and recommend matching degree content corresponding to sequence from high to low to user.
Detailed description of the invention
Fig. 1 shows a kind of knowledge base recommender system block diagram based on content tab that invention provides.
Specific embodiment
Exemplary embodiments of the present disclosure are described in more detail below with reference to accompanying drawings.Although showing the disclosure in attached drawing
Exemplary embodiment, it being understood, however, that may be realized in various forms the disclosure without should be by embodiments set forth here
It is limited.On the contrary, these embodiments are provided to facilitate a more thoroughly understanding of the present invention, and can be by the scope of the present disclosure
It is fully disclosed to those skilled in the art.
The knowledge base recommender system based on content tab that the embodiment of the invention provides a kind of, recommends use for recommendation
Family,
As shown in Figure 1, the recommender system includes server-side；And
Client terminal device establishes communication link by network and the server-side；
The server-side includes:
Data acquisition facility obtains the behavior property data of one or more users, wherein the behavior property data packet
Include user base information collection, user interest information collection and field feedback collection；
Data analysis set-up receives the behavior property data, and adds after analyzing the attribute data corresponding
Content tab, behavior property data corresponding with the content tab are sent to knowledge base content storage devices；
Knowledge base content storage devices, including multiple knowledge base content store databases and knowledge pond, the knowledge base
After content storage devices receive the behavior property data, extracts or deposit in the knowledge pond according to the content tab
Store up corresponding behavior property data；
Server-side recommendation apparatus, by the content push for needing to push to user.
In the present invention, client terminal device includes but is not limited to the communicating terminals such as computer terminal, pad, mobile phone, further includes one
The mobile device that can carry out network communication a bit, is not particularly limited in the present invention.
The data acquisition facility in the knowledge base recommender system based on content tab in the present invention, in the same period
Interior data that can receive one or more client and send, and carry out parallel processing simultaneously, this is in the field of communications
The technology that can be achieved on, therefore, in some network field, the user in multiple client client is in a period of time
When accessing the data such as webpage, app or brush circle of friends, microblogging simultaneously, the data acquisition facility of knowledge base recommender system can be same
The acquisition of Shi Jinhang data.
In terms of data acquisition in the present invention includes two, one, the request of user's active transmission information recommendation, two,
Recommender system actively carries out information recommendation to user.In user's active transmission information recommendation, user is by sending HTTP request
The content that the information of acquisition required for it matches is sent to recommender system to obtain corresponding response, recommender system passes through
User is pushed to after obtaining corresponding matching content after inquiry, data acquisition in this mode is active data request
Process, the process and recommender system of the analysis after data acquisition, processing and screening in this process are actively carried out to user
Carried out when information recommendation data analysis, processing and screening process be it is identical, be hereinafter specifically described.
And can be carried out using various ways during data acquisition, herein, what is taken is web crawlers skill
Art, using the behavior property data that browsed of downloading user user in the search engines such as webpage, behavior herein
Left user browsing when being not the specific movement of user, but being browsed to webpage, app, circle of friends or microblogging
Behavior.It include control module, analysis module and database three parts in the system framework of web crawlers, control module is responsible for
It shares out the work and thinks to each crawler thread of multithreading, analyzer is downloading webpage, carries out the processing of the page, such as by JS foot
The contents processings such as this label, CSS code content, space character, html tag, then store the net downloaded to by database
Page resource.It just include the system of the web crawlers in data acquisition facility herein, to obtain to one or more
The acquisition of the user behavior attribute data of client.
After getting behavior property data, need to these data to be analyzed and processed, before elaborate user behavior category
Property data include user base information collection, user interest information collection and field feedback collection.Herein, based on such
Reason divides user behavior attribute data, and user has several possibility when browse network etc. obtains related network information
The case where, (1) has no purpose, and only the data of going to obtain of randomness often obtain suddenly in the data procedures obtained at random
Get interested content, it is also possible to there is certain purpose, however, purpose is relatively fuzzyyer, this approximation that is referred to as is searched
Rope, in this case we term it user base information collection, these contents have randomness, and the extraction ratio of keyword
It is more difficult, it is also more dispersed；(2) with the acquisition of information of stronger purpose, at this moment, the extraction of keyword or similarity
Calculating is just relatively easy, and comparatively, and the concentration degree of the information of user's concern is higher, referred to herein as user interest
Information collection, (3) in existing recommender system, recommender system has a push of information, but it is basic not to the content of feedback into
Row processing, that is to say, that although recommending, if it is suitable, if it is useful, it can not generate and interact between user, at this
Field feedback collection is provided in text.
What is be set forth above is exactly three kinds of information collection mentioned herein, is receiving information collection as one or more
When, it is necessary to the corresponding corresponding label of setting, to targetedly handle these information.
The data analysis set-up receives the behavior property data, and adds after analyzing the attribute data
Behavior property data corresponding with the content tab are sent to knowledge base content storage devices by corresponding content tab, are led to
Following process is crossed to realize:
After (1-1) receives the behavior property data, judge which kind of type is the behavior property data belong to, if it is
User base information collection or user interest information collection, enter step (1-2), if it is field feedback collection, in the use
Family feedback information concentrates addition feedback content label and enters step (1-4)；
After what (1-2) was got is user base information collection or user interest information collection, user base information is extracted
The customer attribute information that collection or user interest information are concentrated；
(1-3) described data acquisition facility analyzes the customer attribute information, and transfers in knowledge base content store
Personal credit file data, judge the personal credit data of acquired one or more users whether in reasonable range,
If personal credit pre-warning content label is not added in reasonable range, otherwise in the user behavior attribute data
Add the personal credit data label of the user；
(1-4) will be by user base information collection, user interest information collection or user feedback with corresponding contents label
The behavior property data for one or more compositions that information is concentrated are sent to knowledge repository.
The knowledge base content storage devices are stored including multiple knowledge base contents by three corresponding contents tag identifiers
Database, wherein the corresponding user base content store database of user base information set content label, user interest information collection
The corresponding user interest content store database of content tab and the corresponding user feedback of field feedback set content label
Information content storing data library.
The corresponding user base content store database of the user base information set content label, at the beginning of storing user
Beginning content, the user base information are user in underground pipe net webpage, app client or circle of friends, are randomly generated
Basic information collection is obtained corresponding content in the knowledge pond, the contents of the section is passed through by the keyword selected at random
The server-side recommendation apparatus recommends client；
The corresponding user interest information content store database of the user interest information set content label, storage is base
In the content-data of user interest, which is the root based on user when browsing webpage, APP client or circle of friends
Believe according to being browsed in the higher content of attention rate in the attention rate of webpage information within a preset time, APP client or circle of friends
The concern time stopped when breath extracts the interest information collection of user, to pass through institute after obtaining corresponding content in knowledge pond
It states server-side recommendation apparatus and recommends client；
The corresponding field feedback content store database of the field feedback set content label, storage is base
The data attention rate data of the score data or expectation acquisition based on recommendation information after user feedback, the partial data are
User is to the feedback data of the attention rate of the data pushed at random, alternatively, the partial data is the recommendation number of user's activly request
According to feedback data, according to passing through institute after content corresponding in knowledge pond described in the data acquisition in the field feedback collection
It states server-side recommendation apparatus and recommends client.
The knowledge pond is stored with various types of contents, further includes timing means in the knowledge pond, periodically obtains
Undesirable credit record information is stored in knowledge pond in the personal credit data that takes personal credit data, and will acquire, will be a
The data changed in people's credit data also real-time update.
It is described to obtain corresponding content from knowledge pond and recommend to user by server-side recommendation apparatus, further include, it is described
Knowledge pond further includes sorter, and the sorter carries out matching degree calculating to the content that needs are recommended, and it is high to obtain matching degree
The recommendation list of low sequence, and recommend matching degree content corresponding to sequence from high to low to user.
In the present invention, corresponding three classes database is set, the information of different clients is on the one hand stored in, such as based on use
Family basic information is based on user interest information collection or is based on field feedback collection, and the purpose that these are arranged is, by not
It being stored with data in the database under content tab, the data analysis after being is provided fundamental basis and foundation, meanwhile, it deposits here
The data content of storage is the summations that data from different user, and in knowledge pond are Various types of data, by database
Data storage, can analyze out which data is the interested data of user, which data is that user may need to pay close attention to
Data, which data were pushed to user, and after push the effect that generates how, can analyze in the present system
Out.
User obtain data during, when and influenced by various noises, be difficult to get valid data, Shi Eryin
It does not know how to obtain valid data for having no fixed purpose property, this has all obtained very good solution in the present invention.
It should be understood by those skilled in the art that, embodiments herein can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the application, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The application is referring to method, the process of equipment (system) and computer program product according to the embodiment of the present application
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
In a typical configuration, calculating equipment includes one or more processors (CPU), input/output interface, net
Network interface and memory.
Memory may include the non-volatile memory in computer-readable medium, random access memory (RAM) and/
Or the forms such as Nonvolatile memory, such as read-only memory (ROM) or flash memory (flash RAM).Memory is computer-readable Jie
The example of matter.
Computer-readable medium includes permanent and non-permanent, removable and non-removable media can be by any method
Or technology come realize information store.Information can be computer readable instructions, data structure, the module of program or other data.
The example of the storage medium of computer includes, but are not limited to phase change memory (PRAM), static random access memory (SRAM), moves
State random access memory (DRAM), other kinds of random access memory (RAM), read-only memory (ROM), electric erasable
Programmable read only memory (EEPROM), flash memory or other memory techniques, read-only disc read only memory (CD-ROM) (CD-ROM),
Digital versatile disc (DVD) or other optical storage, magnetic cassettes, tape magnetic disk storage or other magnetic storage devices
Or any other non-transmission medium, can be used for storage can be accessed by a computing device information.As defined in this article, it calculates
Machine readable medium does not include temporary computer readable media (transitory media), such as the data-signal and carrier wave of modulation.
The above is only embodiments herein, are not intended to limit this application.To those skilled in the art,
Various changes and changes are possible in this application.It is all within the spirit and principles of the present application made by any modification, equivalent replacement,
Improve etc., it should be included within the scope of the claims of this application.
Claims (7)
1. a kind of knowledge base recommender system based on content tab, recommends user for recommendation, which is characterized in that
The recommender system includes server-side；And
Client terminal device establishes communication link by network and the server-side；
The server-side includes:
Data acquisition facility obtains the behavior property data of one or more users, wherein the behavior property data include using
Family basic information collection, user interest information collection and field feedback collection；
Data analysis set-up receives the behavior property data, and adds in corresponding after analyzing the attribute data
Hold label, behavior property data corresponding with the content tab are sent to knowledge base content storage devices；
Knowledge base content storage devices, including multiple knowledge base content store databases and knowledge pond, the knowledge base content
After storage device receives the behavior property data, phase is extracted or stored in the knowledge pond according to the content tab
The behavior property data answered；
Server-side recommendation apparatus, by the content push for needing to push to user.
2. the knowledge base recommender system according to claim 1 based on content tab, which is characterized in that the data acquisition
Device obtains the behavior property data of one or more users, wherein the behavior property data include user base information
Collection, user interest information collection and field feedback collection, specifically include, and the data acquisition facility passes through web crawlers technology
It actively obtains user and browses the behavioral data of webpage, browsing app client or circle of friends to obtain one or more users'
Behavior property data, alternatively, user actively sends the behavior property data based on the user to the knowledge base recommender system
Content recommendation request, wherein the content recommendation request includes classification, format, the size information of institute's request content.
3. the knowledge base recommender system according to claim 2 based on content tab, which is characterized in that the data analysis
Device receives the behavior property data, and adds corresponding content tab after analyzing the attribute data, will be with institute
It states the corresponding behavior property data of content tab and is sent to knowledge base content storage devices, realized by following process:
After (1-1) receives the behavior property data, judge which kind of type is the behavior property data belong to, if it is user
Basic information collection or user interest information collection, enter step (1-2), anti-in the user if it is field feedback collection
Feedforward information concentrates addition feedback content label and enters step (1-4)；
After what (1-2) was got is user base information collection or user interest information collection, extract user base information collection or
The customer attribute information that person's user interest information is concentrated；
(1-3) described data acquisition facility analyzes the customer attribute information, and transfers the individual in knowledge base content store
Credit evaluation data, judge the personal credit data of acquired one or more users whether in reasonable range, if
In reasonable range, then personal credit pre-warning content label is not added, otherwise add in the user behavior attribute data
The personal credit data label of the user；
(1-4) will be by user base information collection, user interest information collection or field feedback with corresponding contents label
The behavior property data for the one or more compositions concentrated are sent to knowledge repository.
4. the knowledge base recommender system according to claim 3 based on content tab, which is characterized in that in the knowledge base
Storage device is stored, including multiple knowledge base content store databases by three corresponding contents tag identifiers, wherein user base
The corresponding user base content store database of information set content label, the corresponding user of user interest information set content label are emerging
Interesting content store database and the corresponding field feedback content store database of field feedback set content label.
5. the knowledge base recommender system according to claim 4 based on content tab, which is characterized in that
The corresponding user base content store database of the user base information set content label, for store user initially in
Hold, the user base information is user in underground pipe net webpage, app client or circle of friends, the basis being randomly generated
Information collection obtains corresponding content in the knowledge pond by the keyword selected at random, the contents of the section is passed through described
Server-side recommendation apparatus recommends client；
The corresponding user interest information content store database of the user interest information set content label, storage is based on using
The content-data of family interest, the user data are to browse webpage, APP client or when circle of friends based on user, according to
The attention rate of webpage information in preset time, when browsing information in the higher content of attention rate or circle of friends in APP client
The concern time of stop extracts the interest information collection of user, to pass through the clothes after obtaining corresponding content in knowledge pond
Business end recommendation apparatus recommends client；
The corresponding field feedback content store database of the field feedback set content label, storage is based on using
The data attention rate data of the score data or expectation acquisition based on recommendation information after the feedback of family, which is user
To the feedback data of the attention rate of the data pushed at random, alternatively, the partial data is the recommending data of user's activly request
Feedback data, according to after content corresponding in knowledge pond described in the data acquisition in the field feedback collection pass through the clothes
Business end recommendation apparatus recommends client.
6. the knowledge base recommender system according to claim 5 based on content tab, which is characterized in that deposit in the knowledge pond
Various types of contents are contained, further include timing means in the knowledge pond, periodically obtain personal credit data, and will obtain
Undesirable credit record information is stored in knowledge pond in the personal credit data taken, by what is changed in personal credit data
Data also real-time update.
7. the knowledge base recommender system according to claim 5 based on content tab, which is characterized in that described from knowledge pond
The middle corresponding content of acquisition is recommended by server-side recommendation apparatus to user, further includes that the knowledge pond further includes sorter,
The content progress matching degree calculating that the sorter recommends needs, the recommendation list of acquisition matching degree sequence, and to
User recommends matching degree content corresponding to sequence from high to low.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910236942.2A CN109977312B (en) | 2019-03-27 | 2019-03-27 | Knowledge base recommendation system based on content tags |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910236942.2A CN109977312B (en) | 2019-03-27 | 2019-03-27 | Knowledge base recommendation system based on content tags |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109977312A true CN109977312A (en) | 2019-07-05 |
| CN109977312B CN109977312B (en) | 2022-12-09 |
Family
ID=67080719
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910236942.2A Active CN109977312B (en) | 2019-03-27 | 2019-03-27 | Knowledge base recommendation system based on content tags |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109977312B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110597989A (en) * | 2019-08-30 | 2019-12-20 | 腾讯科技（深圳）有限公司 | Data processing method and device and computer storage medium |
| CN111460285A (en) * | 2020-03-17 | 2020-07-28 | 北京百度网讯科技有限公司 | Information processing method, device, electronic equipment and storage medium |
| CN112102676A (en) * | 2020-09-29 | 2020-12-18 | 深圳市元征科技股份有限公司 | Training content generation method and device |
| CN112417310A (en) * | 2019-08-21 | 2021-02-26 | 上海掌门科技有限公司 | Method for establishing intelligent service index and recommending intelligent service |
| CN113569007A (en) * | 2021-06-18 | 2021-10-29 | 武汉理工数字传播工程有限公司 | Method, device and storage medium for processing knowledge service resources |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106649316A (en) * | 2015-10-29 | 2017-05-10 | 北京国双科技有限公司 | Video pushing method and device |
| US20170169341A1 (en) * | 2015-12-14 | 2017-06-15 | Le Holdings (Beijing) Co., Ltd. | Method for intelligent recommendation |
| CN108681919A (en) * | 2018-05-10 | 2018-10-19 | 苏州跃盟信息科技有限公司 | A kind of content delivery method and device |
-
2019
- 2019-03-27 CN CN201910236942.2A patent/CN109977312B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106649316A (en) * | 2015-10-29 | 2017-05-10 | 北京国双科技有限公司 | Video pushing method and device |
| US20170169341A1 (en) * | 2015-12-14 | 2017-06-15 | Le Holdings (Beijing) Co., Ltd. | Method for intelligent recommendation |
| CN108681919A (en) * | 2018-05-10 | 2018-10-19 | 苏州跃盟信息科技有限公司 | A kind of content delivery method and device |
Non-Patent Citations (2)
| Title |
|---|
| 方辉等: "基于Web的制造信息主动推荐服务研究", 《计算机集成制造系统》 * |
| 邵秀丽等: "用户个性化推荐系统的设计与实现", 《计算机工程与设计》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112417310A (en) * | 2019-08-21 | 2021-02-26 | 上海掌门科技有限公司 | Method for establishing intelligent service index and recommending intelligent service |
| CN112417310B (en) * | 2019-08-21 | 2023-10-03 | 上海掌门科技有限公司 | Method for establishing intelligent service index and recommending intelligent service |
| CN110597989A (en) * | 2019-08-30 | 2019-12-20 | 腾讯科技（深圳）有限公司 | Data processing method and device and computer storage medium |
| CN111460285A (en) * | 2020-03-17 | 2020-07-28 | 北京百度网讯科技有限公司 | Information processing method, device, electronic equipment and storage medium |
| CN111460285B (en) * | 2020-03-17 | 2023-11-03 | 阿波罗智联(北京)科技有限公司 | Information processing method, apparatus, electronic device and storage medium |
| CN112102676A (en) * | 2020-09-29 | 2020-12-18 | 深圳市元征科技股份有限公司 | Training content generation method and device |
| CN112102676B (en) * | 2020-09-29 | 2022-09-09 | 深圳市元征科技股份有限公司 | Training content generation method and device |
| CN113569007A (en) * | 2021-06-18 | 2021-10-29 | 武汉理工数字传播工程有限公司 | Method, device and storage medium for processing knowledge service resources |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109977312B (en) | 2022-12-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105989074B (en) | A kind of method and apparatus recommend by mobile device information cold start-up | |
| CN109977312A (en) | A kind of knowledge base recommender system based on content tab | |
| CN104636402B (en) | A kind of classification of business object, search, method for pushing and system | |
| CN107679217B (en) | Associated content extraction method and device based on data mining | |
| CN108664513B (en) | Method, device and equipment for pushing keywords | |
| CN106484766B (en) | Searching method and device based on artificial intelligence | |
| CN103942712A (en) | Product similarity based e-commerce recommendation system and method thereof | |
| CN107578263A (en) | A kind of detection method, device and the electronic equipment of advertisement abnormal access | |
| CN106709073A (en) | Browser notification pushing method and browser terminal | |
| CN107644100A (en) | Information processing method, device and system and computer-readable recording medium | |
| CN110175895A (en) | A kind of item recommendation method and device | |
| CN108122153A (en) | Personalized recommendation method based on cloud computing tupe under e-commerce environment | |
| CN101957968A (en) | Online transaction service aggregation method based on Hadoop | |
| Dias et al. | Automating the extraction of static content and dynamic behaviour from e-commerce websites | |
| CN110119401A (en) | Processing method, device, server and the storage medium of user's portrait | |
| CN109190027A (en) | Multi-source recommended method, terminal, server, computer equipment, readable medium | |
| CN112116426A (en) | Method and device for pushing article information | |
| CN107391528A (en) | Front end assemblies Dependency Specification searching method and equipment | |
| CN107622090A (en) | Acquisition methods, the apparatus and system of object | |
| CN111966916A (en) | Recommendation method and device, electronic equipment and computer readable storage medium | |
| CN113327154B (en) | E-commerce user message pushing method and system based on big data | |
| Filipowska et al. | Improving user experience in e-commerce by application of process mining techniques | |
| CN107845005A (en) | webpage generating method and device | |
| CN104050174B (en) | A kind of personal page generation method and device | |
| Gudla et al. | Enhanced service recommender and ranking system using browsing patterns of users |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20230522 Address after: 230000 floor 1, building 2, phase I, e-commerce Park, Jinggang Road, Shushan Economic Development Zone, Hefei City, Anhui Province Patentee after: Dragon totem Technology (Hefei) Co.,Ltd. Address before: No. 128, Linghu Road, Anqing, Anhui, Anhui Patentee before: ANQING NORMAL University Effective date of registration: 20230522 Address after: 710000 F2-867, 6th Floor, Building A2, Guorun City, Xixian Avenue, Fengdong New City, Xi'an City, Shaanxi Province Patentee after: Xi'an Zhongfu Information Technology Co.,Ltd. Address before: 230000 floor 1, building 2, phase I, e-commerce Park, Jinggang Road, Shushan Economic Development Zone, Hefei City, Anhui Province Patentee before: Dragon totem Technology (Hefei) Co.,Ltd. |
|
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20240129 Address after: No. 22, 5th Community, Wanghe Village, Suwu Town, Minqin County, Wuwei City, Gansu Province, 733300 Patentee after: Gao Wanxu Country or region after: China Address before: 710000 F2-867, 6th Floor, Building A2, Guorun City, Xixian Avenue, Fengdong New City, Xi'an City, Shaanxi Province Patentee before: Xi'an Zhongfu Information Technology Co.,Ltd. Country or region before: China |
|
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20240402 Address after: 710000 room C2, floor 9, new century building, No. 4, Gaoxin Second Road, Lianhu District, Xi'an City, Shaanxi Province Patentee after: Shaanxi Zhongzun Intellectual Property Agency Co.,Ltd. Country or region after: China Address before: No. 22, 5th Community, Wanghe Village, Suwu Town, Minqin County, Wuwei City, Gansu Province, 733300 Patentee before: Gao Wanxu Country or region before: China |