CN107943907A - A kind of knowledge base commending system based on content tab - Google Patents
A kind of knowledge base commending system based on content tab Download PDFInfo
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- CN107943907A CN107943907A CN201711148081.XA CN201711148081A CN107943907A CN 107943907 A CN107943907 A CN 107943907A CN 201711148081 A CN201711148081 A CN 201711148081A CN 107943907 A CN107943907 A CN 107943907A
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- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
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Abstract
A kind of knowledge base commending system based on content tab, it is characterized by comprising knowledge source, knowledge acquisition module, knowledge recommendation module, scheduling of resource module, it carries out the recommendation of knowledge base knowledge by the recommendation method based on content tab, present system is of less demanding to various equipment running environment, solve the problems, such as that the knowledge recommendation system hardware resources consumption of existing knowledge storehouse is big, and be also easy to grasp for technical staff.
Description
Technical field
The present invention designs a kind of knowledge base knowledge recommendation system, and a kind of specifically knowledge base based on content tab pushes away
Recommend system.
Background technology
Few at present that knowledge recommendation is introduced in knowledge base, general knowledge simply integrates traditional search engine and provides
The search of knowledge content is carried out to user.User is difficult effectively to directly acquire or obtain oneself at any time to think in the knowledge base of magnanimity
The knowledge content wanted, also is difficult to get object content within the extremely short time even by the mode of various conditional information retrievals.
Most contents system can only accomplish " finding relevant information " at present, and " finding the information that I wants " few system
It can accomplish, the real requirement of user is submerged in mass data, how from magnanimity information to be obtained up-to-date information in time, is one
A problem highly studied.
The content of the invention
The present invention proposes a kind of commending system based on content tab, and is applied to knowledge base field, allows user passively
Oneself knowledge content interested is got, it includes：
Knowledge source：Including electronics knowledge resource, paper knowledge resource, electronics knowledge resource includes e-text, electronic pictures, regards
Frequently or voice；
Knowledge acquisition module：It is responsible for gathering knowledge content, or manual entry from knowledge source, knowledge acquisition module collects knowledge
After content, knowledge content is saved in content resource database, it is always new that the knowledge content recommended for holding, which is, and knowledge is adopted
Collection module further includes active content management module, and active content management module is responsible for holding active interior in resource database
Hold, be considered as if a certain knowledge content issuing time exceedes certain actual effect it is expired, and labeled as having filed, without
In later knowledge recommendation list；
Knowledge recommendation module：It is responsible for response user's request, generates knowledge recommendation list, and list is returned into user, in knowledge
Algorithm in recommending module is the proposed algorithm based on content tab, and knowledge recommendation module further includes newest knowledge content is whole
The function module in recommendation list is closed, because newest knowledge is according to time-sequencing, and content has randomness.
Scheduling of resource module：It is responsible for monitoring knowledge base commending system current load situation, knowledge data is adjusted according to algorithm
The time actual effect of holding, so as to ensure the real-time of knowledge, and controls the quantity of knowledge；Also call proposed algorithm carry out calculate and
On the calculating of similarity, the content of calculating includes user's its use habit data during knowledge is browsed, this uses habit
Used data include information, these information such as usage time, knowledge classification, daily knowledge reading time and the number read and are all remembered
Customer data base is recorded, then, data analysis is carried out by user modeling module, and ultimately generate user model and be stored in user's mould
In type database.
The resource module timing operation, constantly updates user model so that the interest transfer of user can reflect in real time
Into user model.
The resource module further includes monitoring system resource module, and the monitoring system resource module is real according to loading condition
When adjust computing resource distribution so that keep knowledge base commending system stablize response time.
The knowledge classification includes classifying to the interest characteristics of the different themes of user, calculates and pays close attention to certain using user
The time span of piece content, and an attention rate index in this, as user to this content.
Brief description of the drawings
Fig. 1 is the knowledge base commending system schematic diagram of the invention based on content tab；
Fig. 2 is the knowledge base commending system knowledge recommendation principle schematic of the invention based on content tab.
Embodiment
Embodiment 1, as shown in Figure 1, a kind of commending system based on content tab, and is applied to knowledge base field, allows use
Family it is passive get oneself knowledge content interested, it includes：
Knowledge source：Including electronics knowledge resource, paper knowledge resource, electronics knowledge resource includes e-text, electronic pictures, regards
Frequently or voice；
Knowledge acquisition module：It is responsible for gathering knowledge content, or manual entry from knowledge source, knowledge acquisition module collects knowledge
After content, knowledge content is saved in content resource database, it is always new that the knowledge content recommended for holding, which is, and knowledge is adopted
Collection module further includes active content management module, and active content management module is responsible for holding active interior in resource database
Hold, be considered as if a certain knowledge content issuing time exceedes certain actual effect it is expired, and labeled as having filed, without
In later knowledge recommendation list；
Knowledge recommendation module：It is responsible for response user's request, generates knowledge recommendation list, and list is returned into user, in knowledge
Algorithm in recommending module is the proposed algorithm based on content tab, and knowledge recommendation module further includes newest knowledge content is whole
The function module in recommendation list is closed, because newest knowledge is according to time-sequencing, and content has randomness.
Scheduling of resource module：It is responsible for monitoring knowledge base commending system current load situation, knowledge data is adjusted according to algorithm
The time actual effect of holding, so as to ensure the real-time of knowledge, and controls the quantity of knowledge；Also call proposed algorithm carry out calculate and
On the calculating of similarity, the content of calculating includes user's its use habit data during knowledge is browsed, this uses habit
Used data include information, these information such as usage time, knowledge classification, daily knowledge reading time and the number read and are all remembered
Customer data base is recorded, then, data analysis is carried out by user modeling module, and ultimately generate user model and be stored in user's mould
In type database.
The resource module timing operation, constantly updates user model so that the interest transfer of user can reflect in real time
Into user model.
The resource module further includes monitoring system resource module, and the monitoring system resource module is real according to loading condition
When adjust computing resource distribution so that keep knowledge base commending system stablize response time.
The knowledge classification includes classifying to the interest characteristics of the different themes of user, calculates and pays close attention to certain using user
The time span of piece content, and an attention rate index in this, as user to this content.
The knowledge base commending system knowledge based on content tab that the present invention is further explained with reference to specific case
Recommend principle.As shown in Fig. 2,
First, there is a modeling to knowledge point metadata, knowledge is such as divided into different types, with knowledge class shown in Fig. 2
Include knowledge A, knowledge B, knowledge C exemplified by type,
Knowledge A includes：Education, culture, science and technology；
Knowledge B includes：Music, military affairs；
Knowledge C includes：History, economy, culture；
First, there is a modeling to knowledge point metadata, knowledge is such as divided into different types, with knowledge class shown in Fig. 2
Include knowledge A, knowledge B, knowledge C exemplified by type,
Knowledge A includes：Education, culture, science and technology；
Knowledge B includes：Music, military affairs；
Knowledge C includes：History, economy, culture；
Then, user behavior is analyzed by scheduling of resource module, calls proposed algorithm to be calculated and on similarity
Calculate, the content of calculating includes user's its use habit data during knowledge is browsed, which includes making
With time, the information such as knowledge classification, daily knowledge reading time and number read, scheduling of resource module by these information all by
Customer data base is recorded, as scheduling of resource module calculates,
User A is during knowledge is browsed, its use habit has the custom for visiting knowledge A, and the knowledge classification of reading is education, text
Change and scientific and technological class；
User B is during knowledge is browsed, its use habit has the custom for visiting knowledge B, and the knowledge classification of reading is music, army
Thing class；
For user C during knowledge is browsed, its use habit has the custom for visiting knowledge C, and the knowledge classification of reading is history, passes through
Ji, culture；
Scheduling of resource module recorded above-mentioned result of calculation in customer data base.
Then, user modeling module carries out data analysis, and the similarity between knowledge is found by the metadata of knowledge, because
Type is all " culture " knowledge A and knowledge C is considered as similar knowledge, and then draws the model of user A, i.e. knowledge A+ knowledge
C, further, the model of user A include " education, culture, science and technology "+" history, economy ", and scheduling of resource module is by user A's
Model is stored in user model database.
Finally, knowledge recommendation module calls the user model of user A, by knowledge A+ knowledge C, i.e. " education, culture, science and technology "
+ " history, economy " knowledge is pushed to user A.
The content recommendation system of property one by one, best effect are realized according to user preferences and demand recommendation.
Simply recommend to be all based on the calculating and processing of mass data greatly, but some high complicated calculations are efficiently run in mass data
The resource that method needs to consume is also surprising, so for intelligent recommendation, compares conjunction for the recommendation method based on content tab
Suitable, it is of less demanding to various equipment running environment, and is also easy to grasp for technical staff.Using this method
System in, recommended object is indicated using the feature of its content, and commending system is by learning the interest of user, by user
Model and recommended object carry out similarity-rough set to realize feature extraction, and the content of text class, its feature comparatively compared with
Easily extraction, so, in the knowledge content system that text to be described, using the recommendation method based on content tab, effect is very
Significantly.
Claims (4)
1. a kind of knowledge base commending system based on content tab, it is characterised in that it includes：
Knowledge source：Including electronics knowledge resource, paper knowledge resource, electronics knowledge resource includes e-text, electronic pictures, regards
Frequently or voice；
Knowledge acquisition module：It is responsible for gathering knowledge content, or manual entry from knowledge source, knowledge acquisition module collects knowledge
After content, knowledge content is saved in content resource database, it is always new that the knowledge content recommended for holding, which is, and knowledge is adopted
Collection module further includes active content management module, and active content management module is responsible for holding active interior in resource database
Hold, be considered as if a certain knowledge content issuing time exceedes certain actual effect it is expired, and labeled as having filed, without
In later knowledge recommendation list；
Knowledge recommendation module：It is responsible for response user's request, generates knowledge recommendation list, and list is returned into user, in knowledge
Algorithm in recommending module is the proposed algorithm based on content tab, and knowledge recommendation module further includes newest knowledge content is whole
The function module in recommendation list is closed, because newest knowledge is according to time-sequencing, and content has randomness；
Scheduling of resource module：It is responsible for monitoring knowledge base commending system current load situation, is kept according to algorithm adjustment knowledge data
Time actual effect, so as to ensure the real-time of knowledge, and control the quantity of knowledge；Also call proposed algorithm calculated and on
The calculating of similarity, the content of calculating include user's its use habit data during knowledge is browsed, the use habit number
All it is recorded to according to information, these information such as time, knowledge classification, daily knowledge reading time and the number read are included the use of
Customer data base, then, carries out data analysis, and ultimately generate user model and be stored in user model number by user modeling module
According in storehouse.
2. the knowledge base commending system according to claim 1 based on content tab, it is characterised in that：The resource module
Timing operation, constantly updates user model so that the interest transfer of user can reflect in user model in real time.
3. the knowledge base commending system according to claim 1 based on content tab, it is characterised in that：The resource module
Monitoring system resource module is further included, the monitoring system resource module adjusts point of computing resource according to loading condition in real time
Match somebody with somebody, so as to be kept for the response time that knowledge base commending system is stablized.
4. the knowledge base commending system according to claim 1 based on content tab, it is characterised in that：The knowledge classification
Interest characteristics including the different themes to user is classified, and calculates the time span that certain content is paid close attention to using user, and
An attention rate index in this, as user to this content.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112052389A (en) * | 2020-08-27 | 2020-12-08 | 安徽聚戎科技信息咨询有限公司 | Knowledge recommendation method based on regional chain |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102630052A (en) * | 2012-04-16 | 2012-08-08 | 上海交通大学 | Real time streaming-oriented television program recommendation system |
| CN104142940A (en) * | 2013-05-08 | 2014-11-12 | 华为技术有限公司 | Information recommendation processing method and information recommendation processing device |
| CN104834668A (en) * | 2015-03-13 | 2015-08-12 | 浙江奇道网络科技有限公司 | Position recommendation system based on knowledge base |
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- 2017-11-17 CN CN201711148081.XA patent/CN107943907A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102630052A (en) * | 2012-04-16 | 2012-08-08 | 上海交通大学 | Real time streaming-oriented television program recommendation system |
| CN104142940A (en) * | 2013-05-08 | 2014-11-12 | 华为技术有限公司 | Information recommendation processing method and information recommendation processing device |
| CN104834668A (en) * | 2015-03-13 | 2015-08-12 | 浙江奇道网络科技有限公司 | Position recommendation system based on knowledge base |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112052389A (en) * | 2020-08-27 | 2020-12-08 | 安徽聚戎科技信息咨询有限公司 | Knowledge recommendation method based on regional chain |
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Application publication date: 20180420 |