CN109063198A - Melt the multidimensional visual search recommender system of media resource - Google Patents
Melt the multidimensional visual search recommender system of media resource Download PDFInfo
- Publication number
- CN109063198A CN109063198A CN201811052017.6A CN201811052017A CN109063198A CN 109063198 A CN109063198 A CN 109063198A CN 201811052017 A CN201811052017 A CN 201811052017A CN 109063198 A CN109063198 A CN 109063198A
- Authority
- CN
- China
- Prior art keywords
- resource
- user
- video
- search
- word
- 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
Landscapes
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses a kind of multidimensional visual search recommender systems for melting media resource, the system combines personalized recommendation technology and visualization technique, user is in user interface input inquiry keyword, database sharing index is provided to matchmaker by open source full-text search engine Lucene, to the JSON data feedback of search result data building different-format to four kinds of level, time, map and word cloud different visualization tools, and show on a user interface；User is after visualization interface interacts, and recommending module is according to the media resource that the media resource having clicked on is that user recommends similarity high.The visualization that system provides four aspects of search result shows, while being that user recommends the highest media materials resource of correlation according to user mutual behavior.
Description
Technical field
The present invention relates to field of computer technology, in particular to a kind of multidimensional visual search for melting media resource recommends system
System can provide library progress quick-searching to matchmaker according to level, time, three, space dimension and carry out search result visual
It shows.
Background technique
Independent matchmaker all has been established at present and provides library for domestic major TV station, stores a large amount of video, audio, text, picture
Resource.For these across media resource, major TV station also establish oneself media asset management system to these media resources into
Row rationally utilizes, unified filing, centralized management and rationally circulation, improves the utility value of program resource.Media asset management system master
Wanting function includes the functions such as retrieval, downloading, cataloguing, program storage, back-stage management, and the inquiry of most media asset management system
Function lays particular emphasis on the inquiry of various ways, and is only showed to obtained result set is inquired with list mode, and ways of presentation is dull,
Interdependence between resource do not know, while lacking the interaction with user yet, and another aspect matchmaker provides base resource from permanent
From the point of view of be increased always, for these ever-increasing mass datas inquiry, current inquiring technology is likely difficult to support,
It is badly in need of according to business scenario Optimizing Queries service, for another further aspect when the result set numerous contents of search, user will take many
Time is browsing one by one and is searching in related content, therefore is also by the content that user mutual behavior is user's recommendation high quality
One of demand urgently to be solved.
Summary of the invention
In view of the above-mentioned deficiencies in the prior art, it is an object of the present invention to provide a kind of multidimensional visual search for melting media resource
Recommender system, the system can pass through Optimizing Queries condition quick-searching element in ever-increasing voluminous media material database
Aptitude source, and the search result of huge number is visualized to user, while the text similarity of resource manuscript is made
The recommendation of related resource improves user interactivity, facilitates reporter, editor and therefrom obtains selected topic inspiration.
The multidimensional visual search recommender system for melting media resource of the invention is by personalized recommendation technology and visualizes skill
Art combines, which is broadly divided into four user interface, data query and processing, visualization model, recommending module parts.With
Family provides database sharing index, the keyword warp of user's input to matchmaker in user interface input inquiry keyword, by Lucene
It crosses Lucene inquiry and returns to search result.To the JSON data feedback of search result data building different-format to level, the time,
Map, four kinds of different visualization tools of word cloud, and show on a user interface.User after visualization interface interacts,
It is the media resource that user recommends similarity high according to the media resource having clicked on.
(1) user interface
User interface is divided into the field of search, and level shows area, geographical show area, and timeline show area is handed over according to user
The video display and manuscript word cloud for the single resource that the related resource list area of user for each other recommendation, user choose are shown.Client
Carried out data transmission between end and server-side by JSON formatted data.User completes all behaviour in a complete interface
Make, reduces the complexity of operation.
(2) keyword search module
Server end is indexed structure using the common field that open source full-text search engine Lucene provides lane database to matchmaker
It builds, and configures Chinese word segmentation packet IKAnalyzer for Lucene, user is obtained by user interface input inquiry keyword, Lucene
It takes and corresponding search result is returned to according to index after keyword, and carry out the pretreatment of data, according to the difference of exhibition method, structure
Build the JSON data of different-format.
(3) visualization model
Visualization model is divided into four kinds of modes to show search result set, first is that based on Zhejiang Television Station organizational structure
Level shows, second is that showed based on the map of geographic coordinate information that resource carries, third is that according to every resource in result set
The time shaft of chronological order shows, fourth is that after clicking single resource, after being segmented according to the manuscript of the single resource
Word cloud shows.
(4) recommending module
When the user clicks after single resource, according to the manuscript information for this resource that user clicks, pass through vector space mould
Type algorithm compares other resource manuscript information in search result set, in addition returns to the text word cloud exhibition of the single resource
It is existing.The high resource of the degree of correlation is obtained after similarity calculation, recommends these resources for user, in addition user can also be under
The resource sorted lists for carrying number and browsing number ranking to select oneself desired.
The utility model has the advantages that
The visualization that visual search recommender system of the invention provides four aspects of search result shows, while basis
User mutual behavior is that user recommends the highest media materials resource of correlation.
Detailed description of the invention
Fig. 1 is the network architecture diagram of client of the present invention and server.
Fig. 2 is present system operational flowchart.
Specific embodiment
Further more detailed description is made to technical solution of the present invention in the following with reference to the drawings and specific embodiments.Obviously,
Described embodiment is only a part of the embodiments of the present invention, instead of all the embodiments.Based on the reality in the present invention
Apply example, those of ordinary skill in the art's every other embodiment obtained without making creative work, all
It should belong to the scope of protection of the invention.
It as shown in Fig. 1, is the network architecture schematic diagram of present system.In the present embodiment, which includes interconnection
Net 10, single machine client 20, router 30, firewall 40, backstage Tomcat server 50, Oracle database service device 60,
MySql database server 70.Single machine client 20 can be connected to internet 10, and be connected by router 30 and firewall 40
To the backstage Tomcat server 50 of deployment and Intranet, backstage Tomcat server 50 can be connected to storage center matchmaker and provide database
Oracle database 60, and storage geography information and user information MySql database 70.
Operating procedure is as follows:
It as shown in Fig. 2, is operating process schematic diagram provided in an embodiment of the present invention.The flow and method includes following step
It is rapid:
Step S1: the interface of visual search recommender system is opened in the single machine client 20 of connection internet 10.
Step S2: keyword is inputted in search box and is inquired.
Step S3: search result set is returned by searching for Lucene index file created from the background.
Step S4: from the background by the search result data of return according to three level, geography, time direction encapsulation of data, and
Foreground interface is returned to JSON formatted data by api interface.
Step S5: foreground obtains data and takes out hierarchical data.
Step S6: user can select oneself need according to channel-column-reporter-video four-layer structure in hierarchical data
The video and audio resource wanted.User clicks the title of a certain video and audio resource.
Step S7: user is provided with pressing for addition geography information in hierarchical data before every video and audio resource
Button facilitates reporter to edit and carries out addition geography information manually to the video and audio resource for not having geography information in center matchmaker money, including
Geographical location and description information.
Step S8: after step S6, backstage Tomcat server 50 obtains this video and audio resource that user clicks
Manuscript information.
Step S9: backstage Tomcat server 50 carries out participle operation to the manuscript information, use IKAnalyzer as
Chinese word segmentation packet, and after participle, the word frequency occurred in manuscript according to word is ranked up.
Step 10: the participle after sequence being returned into 20 interface of client, the displaying of word cloud, root are carried out by Cloud.js
According to the sequence of word frequency, the bigger word of word frequency, ranking gets over the word of front, and the word ratio of displaying is also bigger.
Step 11: after obtaining manuscript information by step S8, Tomcat server 50 will be in the manuscript and step S3
The manuscript information for other video and audios that search result is concentrated carries out vector similarity calculating, obtains the value of similarity, and carry out phase
It sorts like degree.
Step 12: according to after the sequence of return as a result, the audio-visual content that display is recommended is gone back in the recommendation interface
Display can be ranked up according to video and audio pageview and video and audio click volume.
Step 13: after step 10 and step 12, the manuscript word cloud of the single video and audio, associated recommendation video will be obtained
The specifying information of list and this video and audio such as title, high standard definition, reporter, creation time etc. are showed in the foreground circle of client 20
On face.
Step 14: step S7 adds video and audio geography information manually, is saved in the MySql database server 70 on backstage
In geodata.
Step 15: marking the tool of this video in map when user clicks single video and audio title in hierarchical data
Body geography information and description information, visualize geodata.
Step 16: foreground obtains data and take-off time data.
Step 17: time data are visualized in the form of timeline.What timeline showed is that user's search key obtains
After obtaining search result, every video resource in search result is carried out on timeline from as far as close sequence.In time dimension
On directly show relationship between video search result collection.This system shows to be realized using TimelineJs.User's search
Timeline request of data is dealt into data Layer by logical layer by keyword, and data Layer forms JSON data and is saved in JSON file
In, expression layer passes through load JSON file presentation time line.
Claims (7)
1. a kind of multidimensional visual search recommender system for melting media resource, which is characterized in that the system is by personalized recommendation skill
Art and visualization technique combine, and mainly include user interface, data query and processing, visualization model, recommending module four
Part；User provides database sharing to matchmaker in user interface input inquiry keyword, by open source full-text search engine Lucene
Index constructs the JSON data feedback of different-format to four kinds of level, time, map and word cloud differences to search result data
Visualization tool, and show on a user interface；User is after visualization interface interacts, and recommending module is according to point
The media resource hit is the media resource that user recommends similarity high.
2. the multidimensional visual search recommender system according to claim 1 for melting media resource, which is characterized in that described
User interface includes that the field of search, level show area, geographical show area, timeline show area, hand over user for each other to recommend according to user
Related resource list area, user the video display area and manuscript word cloud show area of the single resource that choose.
3. the multidimensional visual search recommender system according to claim 1 for melting media resource, which is characterized in that Lucene
Configured with Chinese word segmentation packet IKAnalyzer.
4. the multidimensional visual search recommender system according to claim 1 for melting media resource, which is characterized in that visualization
Module shows search result set in four manners, first is that the level based on TV station's organizational structure shows, second is that based on money
The map of geographic coordinate information from band shows, third is that according to the time shaft of the chronological order of every resource in result set
Show, fourth is that the word cloud after being segmented according to the manuscript of the single resource shows after clicking single resource.
5. the multidimensional visual search recommender system according to claim 1 for melting media resource, which is characterized in that work as user
After clicking single resource, according to the manuscript information for this resource that user clicks, search is tied by vector space model
Other resource manuscript information in fruit set carry out similarity calculation, and are from high to low that user recommends by similarity.
6. the multidimensional visual search recommender system according to claim 5 for melting media resource, which is characterized in that for pushing away
The resource sorted lists that the resource user recommended can select oneself desired according to downloading number and browsing number ranking.
7. a kind of multidimensional visual search recommended method for melting media resource, which is characterized in that method includes the following steps:
Step S1: the interface of visual search recommender system is opened in the single machine client 20 of connection internet 10；
Step S2: keyword is inputted in search box and is inquired；
Step S3: search result set is returned by searching for Lucene index file created from the background；
Step S4: from the background by the search result data of return according to three level, geography, time direction encapsulation of data, and pass through
Api interface returns to foreground interface with JSON formatted data；
Step S5: foreground obtains data and takes out hierarchical data；
Step S6: user can select oneself to need according to channel-column-reporter-video four-layer structure in hierarchical data
Video and audio resource, user click the title of a certain video and audio resource；
Step S7: in hierarchical data, it is provided with the button of addition geography information before every video and audio resource, facilitates use
Family editor to center matchmaker money in do not have geography information video and audio resource carry out manually addition geography information, including geographical location and
Description information；
Step S8: after step S6, backstage Tomcat server 50 obtains the manuscript for this video and audio resource that user clicks
Information；
Step S9: backstage Tomcat server 50 carries out participle operation to the manuscript information, uses IKAnalyzer as Chinese
Participle packet, and after participle, the word frequency occurred in manuscript according to word is ranked up；
Step 10: the participle after sequence being returned into 20 interface of client, the displaying of word cloud is carried out by Cloud.js, according to word
The sequence of frequency, the bigger word of word frequency, ranking get over the word of front, and the word ratio of displaying is also bigger；
Step 11: after obtaining manuscript information by step S8, Tomcat server 50 is by the search in the manuscript and step S3
The manuscript information of other video and audios in result set carries out vector similarity calculating, obtains the value of similarity, and carry out similarity
It sorts from high to low；
Step 12: corresponding audio-visual content is successively shown according to the ranking results of return, it, can be with root in the recommendation interface
Display is ranked up according to video and audio pageview and video and audio click volume；
Step 13: by the manuscript word of associated recommendation list of videos, the single video and audio obtained by step 10 that step 12 obtains
Cloud and the specifying information of this video and audio such as title, high standard definition, reporter, creation time etc. are showed in the foreground interface of client 20
On；
Step 14: the MySql database server on backstage is saved directly to after step S7 adds video and audio geography information manually
In 70 geodata；
Step 15: marking this video specifically in map when user clicks single video and audio title in hierarchical data
Information and description information are managed, geodata is visualized；
Step 16: foreground obtains data and take-off time data；
Step 17: time data being visualized in the form of timeline using TimelineJs realization；What timeline showed is to use
After family search key obtains search result, every video resource in search result is carried out on timeline from as far as close row
Sequence.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811052017.6A CN109063198B (en) | 2018-09-10 | 2018-09-10 | Multi-dimensional visual search recommendation system for fusing media resources |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811052017.6A CN109063198B (en) | 2018-09-10 | 2018-09-10 | Multi-dimensional visual search recommendation system for fusing media resources |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109063198A true CN109063198A (en) | 2018-12-21 |
| CN109063198B CN109063198B (en) | 2022-02-11 |
Family
ID=64760184
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811052017.6A Active CN109063198B (en) | 2018-09-10 | 2018-09-10 | Multi-dimensional visual search recommendation system for fusing media resources |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109063198B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111444356A (en) * | 2020-03-11 | 2020-07-24 | 北京字节跳动网络技术有限公司 | Search-based recommendation method and device |
| CN111625716A (en) * | 2020-05-12 | 2020-09-04 | 聚好看科技股份有限公司 | Media asset recommendation method, server and display device |
| CN112000820A (en) * | 2020-08-10 | 2020-11-27 | 海信电子科技(武汉)有限公司 | Media asset recommendation method and display device |
| CN112417260A (en) * | 2019-08-20 | 2021-02-26 | 腾讯科技（深圳）有限公司 | Localized recommendation method and device and storage medium |
| US20220261891A1 (en) * | 2021-02-12 | 2022-08-18 | The Toronto-Dominion Bank | Systems and methods for presenting multimedia content |
| CN117149892A (en) * | 2023-07-14 | 2023-12-01 | 深圳全企通信息技术有限公司 | Enterprise dimension information display method based on big data display screen |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102364473A (en) * | 2011-11-09 | 2012-02-29 | 中国科学院自动化研究所 | Netnews search system and method based on geographic information and visual information |
| US20130246422A1 (en) * | 2010-09-12 | 2013-09-19 | Rishi Bhargava | System and method for clustering host inventories |
| CN104536956A (en) * | 2014-07-23 | 2015-04-22 | 中国科学院计算技术研究所 | A Microblog platform based event visualization method and system |
| CN105005578A (en) * | 2015-05-21 | 2015-10-28 | 中国电子科技集团公司第十研究所 | Multimedia target information visual analysis system |
| CN106202411A (en) * | 2016-07-11 | 2016-12-07 | 北京搜狗科技发展有限公司 | A kind of Search Results methods of exhibiting and device |
| CN107978310A (en) * | 2017-11-30 | 2018-05-01 | 腾讯科技（深圳）有限公司 | Audio-frequency processing method and device |
-
2018
- 2018-09-10 CN CN201811052017.6A patent/CN109063198B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130246422A1 (en) * | 2010-09-12 | 2013-09-19 | Rishi Bhargava | System and method for clustering host inventories |
| CN102364473A (en) * | 2011-11-09 | 2012-02-29 | 中国科学院自动化研究所 | Netnews search system and method based on geographic information and visual information |
| CN104536956A (en) * | 2014-07-23 | 2015-04-22 | 中国科学院计算技术研究所 | A Microblog platform based event visualization method and system |
| CN105005578A (en) * | 2015-05-21 | 2015-10-28 | 中国电子科技集团公司第十研究所 | Multimedia target information visual analysis system |
| CN106202411A (en) * | 2016-07-11 | 2016-12-07 | 北京搜狗科技发展有限公司 | A kind of Search Results methods of exhibiting and device |
| CN107978310A (en) * | 2017-11-30 | 2018-05-01 | 腾讯科技（深圳）有限公司 | Audio-frequency processing method and device |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112417260A (en) * | 2019-08-20 | 2021-02-26 | 腾讯科技（深圳）有限公司 | Localized recommendation method and device and storage medium |
| CN112417260B (en) * | 2019-08-20 | 2023-08-18 | 腾讯科技（深圳）有限公司 | Localized recommendation method, device and storage medium |
| CN111444356A (en) * | 2020-03-11 | 2020-07-24 | 北京字节跳动网络技术有限公司 | Search-based recommendation method and device |
| CN111444356B (en) * | 2020-03-11 | 2024-02-13 | 北京字节跳动网络技术有限公司 | Recommendation method and device based on search |
| CN111625716A (en) * | 2020-05-12 | 2020-09-04 | 聚好看科技股份有限公司 | Media asset recommendation method, server and display device |
| CN111625716B (en) * | 2020-05-12 | 2023-10-31 | 聚好看科技股份有限公司 | Media asset recommendation method, server and display device |
| CN112000820A (en) * | 2020-08-10 | 2020-11-27 | 海信电子科技(武汉)有限公司 | Media asset recommendation method and display device |
| US20220261891A1 (en) * | 2021-02-12 | 2022-08-18 | The Toronto-Dominion Bank | Systems and methods for presenting multimedia content |
| US11481843B2 (en) * | 2021-02-12 | 2022-10-25 | The Toronto-Dominion Bank | Systems and methods for presenting multimedia content |
| CN117149892A (en) * | 2023-07-14 | 2023-12-01 | 深圳全企通信息技术有限公司 | Enterprise dimension information display method based on big data display screen |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109063198B (en) | 2022-02-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109063198A (en) | Melt the multidimensional visual search recommender system of media resource | |
| US11681736B2 (en) | System and method for tagging a region within a frame of a distributed video file | |
| Meng et al. | KASR: a keyword-aware service recommendation method on mapreduce for big data applications | |
| US8266131B2 (en) | Method and a system for searching information using information device | |
| US7865498B2 (en) | Broadcast network platform system | |
| Leuski et al. | Lighthouse: Showing the way to relevant information | |
| CN111680254B (en) | Content recommendation method and device | |
| CN102024009A (en) | Generating method and system of video scene database and method and system for searching video scenes | |
| CN104598556A (en) | Search method and search device | |
| GB2465378A (en) | Image based search system and method | |
| US11494822B2 (en) | Content curation and product linking system and method | |
| JP2003526144A (en) | Method and apparatus for defining search queries and user profiles and viewing search results | |
| CN103984740A (en) | Combination label based search page display method and system | |
| US10346492B2 (en) | Content placement criteria expansion | |
| US8782099B2 (en) | Graphical objects bonding society system and method of operation for a game | |
| JP4955179B2 (en) | Search user interface for building and managing user profiles and search criteria | |
| US20100030618A1 (en) | System and method for visualizing a marketing strategy | |
| KR20170114321A (en) | Personalized news contents filtering system and method using the Keyword analysis | |
| JP2003323458A (en) | Information retrieval method and its device, execution program of method and recording medium recording execution program of method | |
| US20090287677A1 (en) | Streaming media instant answer on internet search result page | |
| WO2014185004A1 (en) | Information processing device, information processing method, and program | |
| CN111159435B (en) | Multimedia resource processing method, system, terminal and computer readable storage medium | |
| CN107870713B (en) | Picture and text integrated picture processing method with compatibility | |
| Dang et al. | FmFinder: Search and filter your favorite songs | |
| WO2021238084A1 (en) | Voice packet recommendation method, apparatus and device, and storage medium |
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 |