CN105975609A - Industrial design product intelligent recommendation method and system - Google Patents
Industrial design product intelligent recommendation method and system Download PDFInfo
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- CN105975609A CN105975609A CN201610329184.5A CN201610329184A CN105975609A CN 105975609 A CN105975609 A CN 105975609A CN 201610329184 A CN201610329184 A CN 201610329184A CN 105975609 A CN105975609 A CN 105975609A
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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
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
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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
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2457—Query processing with adaptation to user needs
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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
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/248—Presentation of query results
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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
Abstract
The invention relates to an intelligent recommendation method and system, belongs to the field of information processing and particularly relates to an industrial design product intelligent recommendation method and system. Industrial design industry related information metadatabase is built by means of data obtained from internet collection and third-party data collection, user data is obtained, then an industrial design industry related information list matched with user interest, identifies and personalized requirements are recommended according to a socialgram and an interest map of users on the basis of the industrial design industry related information metadatabase and a user database, finally recommended information is displayed on an application program interface to be selected by the users, and intelligent recommendation of industrial design industry information is achieved. Industrial design industry professional information obtaining precision is greatly improved for the users.
Description
Technical field
The present invention relates to a kind of intelligent recommendation method and system, belong to field of information processing, be specifically related to
A kind of industrial design product intelligent recommends method and system.
Background technology
For common Internet user, more energy to be paid and screened by search engine
Go out oneself effective information and oneself content interested.For the personage of some professional fields, think
Search engine to be passed through obtains the effective information in some industries purely, at present, also has certain
Difficulty.
The appearance of recommended technology so that Internet user obtains the mode of information from the most with clearly defined objective
Information search be progressively switch to the INFORMATION DISCOVERY meeting user habit of higher level.Along with recommended technology
Develop into intelligent recommendation rank, it is recommended that to the content of user also by the information recommendation mistake meeting popular behavior
Cross recommendation of personalized information.
Under the magnanimity information historical background with mass data has been arrived in internet development, for some specialties
The personage in field, the personalized recommendation of industry internal information is requisite.Lead specific to industrial design
The personalized recommendation in territory, is required for different users, according to their taste, hobby, identity,
Demand provides more accurate recommendation information.At this moment, it is recommended that system is it should be understood that need content recommendation and use
The speciality at family, or based on social network, by finding the user of hobby identical with active user,
Realize recommending.
Summary of the invention
The specialized information acquisition precision that the present invention mainly solves existing for prior art is low and difficult
With the technical problem carrying out personalized recommendation etc. for industrial design field, it is provided that a kind of industry sets
Meter product intelligent recommends method and system.The method and system achieve the intelligence of industrial design trade information
Can recommend, improve professional person and obtain the precision of specialized information.
The above-mentioned technical problem of the present invention is mainly addressed by following technical proposals:
A kind of industrial design product intelligent recommends method, comprises the following steps:
Industrial database establishment step, for collecting the trade information relevant to industrial design, and will search
The information of rope collection is stored in industrial design metadatabase；
Customer data base establishment step, for obtaining the user including affiliated industry and interest characteristics
Information, and described user profile is stored in customer data base；
Trade information recommendation step, for obtaining the interest characteristics of user, described from customer data base
Industrial design metadatabase obtains the industrial design information matched with interest characteristics and recommends use
Family.
Optimizing, above-mentioned a kind of industrial design product intelligent recommends method, and described industrial database is built
In vertical step:
Industry relevant information, and the industry phase that will obtain is obtained from the Internet or third party's metadata provider
Pass information is stored in interim polymerized data base；
Industry relevant information in interim polymerized data base is carried out the knot of content metadata, label data
Structure data parsing is processed；Data after resolving processing are carried out standardization processing and examination & verification to form work
Industry design element data base.
Optimizing, above-mentioned a kind of industrial design product intelligent recommends method, described industry metadatabase
In establishment step:
And searched from social networks, industrial design website, specialty forum by reptile and data mining technology
Website acquisition data held up in index and analysis obtains Internet Industry design industry metadata；To described industry
Design industry metadata carries out filtering, mate after obtain degree of depth matched data；Import third party's degree of depth to close
Make data；Industrial design industry metadata, degree of depth matched data, third party's depth cooperation data are entered
Unified polymerization storehouse and tag library are set up in row coupling combination, ultimately form industrial design industry metadatabase.
Optimizing, above-mentioned a kind of industrial design product intelligent recommends method, and described trade information is recommended
The following sub-step of selection execution in step:
Interest figure recommends sub-step: obtain user history information from customer data base, based on described history
Information analysis also excavates user's interest characteristics under situation of presence information, comes based on described interest characteristics
Carry out the recommendation of information content；Wherein, described user history information include user behavior hobby and evaluate,
Historical circumstances information residing for user；Described context information includes: the regional location residing for user, lookup
One or more in time period；
Socialgram recommends sub-step, obtains user's friend information from customer data base；Based on described good friend
Information is searched good friend in industrial design metadatabase and is associated content, and described good friend is associated commending contents
To user；Wherein, described good friend associates content and includes: friend recommendation content, good friend seen content,
Good friend comments on or delivered one or more in the content of viewpoint.
Optimizing, above-mentioned a kind of industrial design product intelligent recommends method, and described trade information is recommended
The following sub-step of selection execution in step:
Recombining contents sub-step: for sorting according to difference and representing strategy to the content recommendation got
Recombinate or sort；
Relevance sub-step: for content recommendation being carried out auto-associating according to preset strategy；
Participle management sub-step, for being analyzed process, by the text of content recommendation to content recommendation
Sequence is cut into single entry.
A kind of industrial design product intelligent recommendation apparatus, including with lower module:
Industrial database sets up module, for collecting the trade information relevant to industrial design, and will search
The information of rope collection is stored in industrial design metadatabase；
Customer data base sets up module, for obtaining the user including affiliated industry and interest characteristics
Information, and described user profile is stored in customer data base；
Trade information recommending module, for obtaining the interest characteristics of user, described from customer data base
Industrial design metadatabase obtains the industrial design information matched with interest characteristics and recommends use
Family.
Optimizing, above-mentioned a kind of industrial design product intelligent recommendation apparatus, described industrial database is built
In formwork erection block:
Industry relevant information, and the industry phase that will obtain is obtained from the Internet or third party's metadata provider
Pass information is stored in interim polymerized data base；
Industry relevant information in interim polymerized data base is carried out the knot of content metadata, label data
Structure data parsing is processed；Data after resolving processing are carried out standardization processing and examination & verification to form work
Industry design element data base.
Optimize, above-mentioned a kind of industrial design product intelligent recommendation apparatus, described industry metadatabase
Set up in module:
And searched from social networks, industrial design website, specialty forum by reptile and data mining technology
Website acquisition data held up in index and analysis obtains Internet Industry design industry metadata；To described industry
Design industry metadata carries out filtering, mate after obtain degree of depth matched data；Import third party's degree of depth to close
Make data；Industrial design industry metadata, degree of depth matched data, third party's depth cooperation data are entered
Unified polymerization storehouse and tag library are set up in row coupling combination, ultimately form industrial design industry metadatabase.
Optimizing, above-mentioned a kind of industrial design product intelligent recommendation apparatus, described trade information is recommended
Module Selection and call is with lower unit:
Interest figure recommends subelement: obtain user history information from customer data base, based on described history
Information analysis also excavates user's interest characteristics under situation of presence information, comes based on described interest characteristics
Carry out the recommendation of information content；Wherein, described user history information include user behavior hobby and evaluate,
Historical circumstances information residing for user；Described context information includes: the regional location residing for user, lookup
One or more in time period；
Socialgram recommends subelement, obtains user's friend information from customer data base；Based on described good friend
Information is searched good friend in industrial design metadatabase and is associated content, and described good friend is associated commending contents
To user；Wherein, described good friend associates content and includes: friend recommendation content, good friend seen content,
Good friend comments on or delivered one or more in the content of viewpoint.
Optimizing, above-mentioned a kind of industrial design product intelligent recommendation apparatus, described trade information is recommended
Module Selection and call is with lower unit:
Recombining contents subelement: for sorting according to difference and representing strategy to the content recommendation got
Recombinate or sort；
Relevance subelement: for content recommendation being carried out auto-associating according to preset strategy；
Participle management subelement, for being analyzed process, by the text of content recommendation to content recommendation
Sequence is cut into single entry.
Therefore, present invention have the advantage that industrial design field of the present invention trade information intelligence
The method recommended sets up industrial design by collecting from the Internet with third party's data collection acquisition data
Industry relevant information metadatabase and acquisition user data, then in industrial design industry relevant information unit
On the basis of data base and customer data base, socialgram according to user and interest figure are recommended emerging with user
The industrial design industry list of relevant information that interest, identity, individual demand match, finally in application
Show on program interface that the information of recommendation selects for user, it is achieved that the intelligence of industrial design trade information
Can recommend, drastically increase the precision that user obtains for industrial design industry specialized information.
Accompanying drawing explanation
Fig. 1 is the block diagram of the method for embodiment of the present invention industrial design field trade information intelligent recommendation；
Fig. 2 is the flow process of the method for embodiment of the present invention industrial design field trade information intelligent recommendation
Figure.
Fig. 3 is that the interest graph model of embodiment of the present invention industrial design field trade information intelligent recommendation shows
It is intended to；
Fig. 4 is the social mould of the method for embodiment of the present invention industrial design field trade information intelligent recommendation
Type figure；
Fig. 5 is that the embodiment of the present invention determines content attention rate schematic diagram based on interest figure and socialgram.
Detailed description of the invention
Below by embodiment, and combine accompanying drawing, technical scheme is made the most concrete
Explanation.
Embodiment:
Step 1: collected and third party's data collection by the Internet, sets up the relevant letter of industrial design industry
Breath metadatabase.
Described step 1 obtains data from the Internet and sets up being embodied as of industrial design metadatabase
Mode can be: obtains internet data or accesses third party's metadata provider, forming unified polymerization storehouse
Industrial design industry metadatabase is set up with tag library.
Wherein, unified polymerization storehouse and tag library are formed to set up the tool of industrial design industry metadatabase
Body realizes step:
The data obtained from different data sources enter interim polymerized data base；
Carry out content metadata, the structural data of label data resolves processing；
Data after resolving processing are carried out standardization processing and examination & verification to form industrial design industry unit number
According to storehouse；
Its content sources mainly includes that the Internet, mobile phone, User Defined content and third party cooperate
Business's content；
And in actual applications, by reptile and data mining technology from the social networks of main flow, industry
Design website, specialty forum and search engine web site obtain, analytical data, thus have obtained abundant
Internet Industry design industry metadata, carry out filtering, mate after obtain degree of depth matched data, then lead
Entering third party's depth cooperation data, three carries out coupling combination and sets up unified polymerization storehouse and tag library,
Ultimately form industrial design industry metadatabase.
Step 2: obtain user profile data and set up customer data base.
Described step 2 obtains user data to be set up the detailed description of the invention of customer data base and is: to user
Master data imports, described master data include sex, the age, region/geographical position, industry,
The information such as position, the entire period of actual operation, but it is not limited only to these information；User behavior data is led
Entering, described behavioral data includes the work experience of user, social circle, hobby, project experiences
Etc. information, but it is not limited only to these information.
Collect user and enter the time custom of application program；Collect user and enter the place habit of application program
Used；Collect the title of each plate in user enters application program, number of times, browsing histories, pass through application
The key word of search engine search in program；
Analyze and show that user enters the distribution list of the time custom of application program, analyzes and draw use
Family enters the distribution list of the place custom of application program, analyzes each plate in user enters application program
Title, number of times, browsing histories, by application program search engine search key word and draw
The distribution list that user interest point is comprehensively accustomed to, and set up user data with these three distribution list information
Storehouse；
Step 3: according to user's on the basis of industrial design industry metadatabase and customer data base
Socialgram and interest figure are recommended the trade information list etc. matched with user interest.
Described step 3 on the basis of industrial design industry metadatabase and customer data base according to user
Socialgram and interest figure recommend the tool of the industrial design trade information list matched with user interest
Body embodiment is: like the behavior of trade information etc. and evaluation, real-time collecting by gathering user
Relevant context information and the historical information of passing user, analyze and excavate user and be presently in
Demand characteristic under the conditions of sight carries out the recommendation of information content.Generate based on user's historical data and push away
Recommend data, extract interest characteristics, it is recommended that go out the information list mated with user interest.
In actual applications, each user has oneself a data base, " emerging including user
Interest figure " data (user may see relevant industries information) and " socialgram " data (
User may have tens, up to a hundred good friends and the virtual community of activity thereof) and data label association
Data etc..
Wherein carry out recommendation according to socialgram and can include following several: friend recommendation, friend recommendation is given
My content；Good friend sees, the content that good friend has seen；Further expand to social networks first-class
Content that is that friend commented on or that praised etc..
Carry out recommending being embodied as may include that the regional location according to user, lookup according to interest figure
The content that the recommendation information of the information such as time period, use habit customization personalization recommendation are specified is to user.
Tag attributes relating dot according to industrial design industry relevant information metadatabase content is formed and meets
User interest, identity, the list of individual demand.
Structural data is processed；Content recommendation is recombinated；Content is associated management；
Information is carried out participle management；Carry out tag control.
Described structural data is processed particularly as follows: obtain content recommendation data after, can basis
Difference sorts and represents strategy and carries out recombining contents.
Described content is associated management particularly as follows: complete labeling process after system according to default plan
Slightly carry out auto-associating.
Described information is carried out participle management particularly as follows: be analyzed the text of information metadata processing,
There is provided and text sequence is cut into single entry.
Step 4: recommending to show on interface the industrial design of consequently recommended (analyzing through step 3)
Trade information list selects for user.
In actual applications, also step can automatically be performed according to actual change process: to industrial design row
Industry metadatabase is updated, supplements and strengthens.
Specific embodiment described herein is only to present invention spirit explanation for example.The present invention
Person of ordinary skill in the field described specific embodiment can be made various amendment or
Supplement or use similar mode to substitute, but without departing from the spirit of the present invention or surmount appended power
Scope defined in profit claim.
Claims (10)
1. an industrial design product intelligent recommends method, it is characterised in that comprise the following steps:
Industrial database establishment step, for collecting the trade information relevant to industrial design, and will search
The information of rope collection is stored in industrial design metadatabase；
Customer data base establishment step, for obtaining the user including affiliated industry and interest characteristics
Information, and described user profile is stored in customer data base；
Trade information recommendation step, for obtaining the interest characteristics of user, described from customer data base
Industrial design metadatabase obtains the industrial design information matched with interest characteristics and recommends use
Family.
A kind of industrial design product intelligent the most according to claim 1 recommends method, and its feature exists
In, in described industrial database establishment step:
Industry relevant information, and the industry phase that will obtain is obtained from the Internet or third party's metadata provider
Pass information is stored in interim polymerized data base；
Industry relevant information in interim polymerized data base is carried out the knot of content metadata, label data
Structure data parsing is processed；Data after resolving processing are carried out standardization processing and examination & verification to form work
Industry design element data base.
A kind of industrial design product intelligent the most according to claim 1 recommends method, and its feature exists
In, in described industry metadatabase establishment step:
And searched from social networks, industrial design website, specialty forum by reptile and data mining technology
Website acquisition data held up in index and analysis obtains Internet Industry design industry metadata；To described industry
Design industry metadata carries out filtering, mate after obtain degree of depth matched data；Import third party's degree of depth to close
Make data；Industrial design industry metadata, degree of depth matched data, third party's depth cooperation data are entered
Unified polymerization storehouse and tag library are set up in row coupling combination, ultimately form industrial design industry metadatabase.
A kind of industrial design product intelligent the most according to claim 1 recommends method, and its feature exists
In, the following sub-step of selection execution in described trade information recommendation step:
Interest figure recommends sub-step: obtain user history information from customer data base, based on described history
Information analysis also excavates user's interest characteristics under situation of presence information, comes based on described interest characteristics
Carry out the recommendation of information content；Wherein, described user history information include user behavior hobby and evaluate,
Historical circumstances information residing for user；Described context information includes: the regional location residing for user, lookup
One or more in time period；
Socialgram recommends sub-step, obtains user's friend information from customer data base；Based on described good friend
Information is searched good friend in industrial design metadatabase and is associated content, and described good friend is associated commending contents
To user；Wherein, described good friend associates content and includes: friend recommendation content, good friend seen content,
Good friend comments on or delivered one or more in the content of viewpoint.
A kind of industrial design product intelligent the most according to claim 1 recommends method, and its feature exists
In, the following sub-step of selection execution in described trade information recommendation step:
Recombining contents sub-step: for sorting according to difference and representing strategy to the content recommendation got
Recombinate or sort；
Relevance sub-step: for content recommendation being carried out auto-associating according to preset strategy；
Participle management sub-step, for being analyzed process, by the text of content recommendation to content recommendation
Sequence is cut into single entry.
6. an industrial design product intelligent recommendation apparatus, it is characterised in that include with lower module:
Industrial database sets up module, for collecting the trade information relevant to industrial design, and will search
The information of rope collection is stored in industrial design metadatabase；
Customer data base sets up module, for obtaining the user including affiliated industry and interest characteristics
Information, and described user profile is stored in customer data base；
Trade information recommending module, for obtaining the interest characteristics of user, described from customer data base
Industrial design metadatabase obtains the industrial design information matched with interest characteristics and recommends use
Family.
A kind of industrial design product intelligent recommendation apparatus the most according to claim 6, its feature exists
In, described industrial database is set up in module:
Industry relevant information, and the industry phase that will obtain is obtained from the Internet or third party's metadata provider
Pass information is stored in interim polymerized data base；
Industry relevant information in interim polymerized data base is carried out the knot of content metadata, label data
Structure data parsing is processed；Data after resolving processing are carried out standardization processing and examination & verification to form work
Industry design element data base.
A kind of industrial design product intelligent recommendation apparatus the most according to claim 6, its feature exists
In, described industry metadatabase is set up in module:
And searched from social networks, industrial design website, specialty forum by reptile and data mining technology
Website acquisition data held up in index and analysis obtains Internet Industry design industry metadata；To described industry
Design industry metadata carries out filtering, mate after obtain degree of depth matched data；Import third party's degree of depth to close
Make data；Industrial design industry metadata, degree of depth matched data, third party's depth cooperation data are entered
Unified polymerization storehouse and tag library are set up in row coupling combination, ultimately form industrial design industry metadatabase.
A kind of industrial design product intelligent recommendation apparatus the most according to claim 6, its feature exists
In, described trade information recommending module Selection and call is with lower unit:
Interest figure recommends subelement: obtain user history information from customer data base, based on described history
Information analysis also excavates user's interest characteristics under situation of presence information, comes based on described interest characteristics
Carry out the recommendation of information content；Wherein, described user history information include user behavior hobby and evaluate,
Historical circumstances information residing for user；Described context information includes: the regional location residing for user, lookup
One or more in time period；
Socialgram recommends subelement, obtains user's friend information from customer data base；Based on described good friend
Information is searched good friend in industrial design metadatabase and is associated content, and described good friend is associated commending contents
To user；Wherein, described good friend associates content and includes: friend recommendation content, good friend seen content,
Good friend comments on or delivered one or more in the content of viewpoint.
A kind of industrial design product intelligent recommendation apparatus the most according to claim 6, its feature
Being, described trade information recommending module Selection and call is with lower unit:
Recombining contents subelement: for sorting according to difference and representing strategy to the content recommendation got
Recombinate or sort；
Relevance subelement: for content recommendation being carried out auto-associating according to preset strategy；
Participle management subelement, for being analyzed process, by the text of content recommendation to content recommendation
Sequence is cut into single entry.
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| CN201610329184.5A CN105975609A (en) | 2016-05-18 | 2016-05-18 | Industrial design product intelligent recommendation method and system |
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| CN106600213A (en) * | 2016-11-15 | 2017-04-26 | 广东家易科技有限公司 | Intelligent resume management system and method |
| CN106776653A (en) * | 2015-11-24 | 2017-05-31 | 北京国双科技有限公司 | Data digging method and device |
| CN110969222A (en) * | 2018-09-29 | 2020-04-07 | 西门子股份公司 | Information providing method and system |
| CN111400430A (en) * | 2020-03-11 | 2020-07-10 | 广联达科技股份有限公司 | Method and system for quickly combining prices in digital building list pricing |
| CN111984862A (en) * | 2020-07-30 | 2020-11-24 | 苏州热工研究院有限公司 | Experience feedback data pushing method and device for nuclear power plant business department |
| CN113204816A (en) * | 2021-04-23 | 2021-08-03 | 万翼科技有限公司 | Method and related device for optimizing garage design based on database |
| CN113742587A (en) * | 2021-09-07 | 2021-12-03 | 海粟智链(青岛)科技有限公司 | Internet popularization method suitable for industrial products |
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| CN111984862A (en) * | 2020-07-30 | 2020-11-24 | 苏州热工研究院有限公司 | Experience feedback data pushing method and device for nuclear power plant business department |
| CN113204816A (en) * | 2021-04-23 | 2021-08-03 | 万翼科技有限公司 | Method and related device for optimizing garage design based on database |
| CN113742587A (en) * | 2021-09-07 | 2021-12-03 | 海粟智链(青岛)科技有限公司 | Internet popularization method suitable for industrial products |
| CN113742587B (en) * | 2021-09-07 | 2024-01-12 | 海粟智链(青岛)科技有限公司 | Internet popularization method suitable for industrial products |
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