CN103368898A - Method and system for accomplishing information push - Google Patents
Method and system for accomplishing information push Download PDFInfo
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Abstract
The invention discloses a method and a system for accomplishing information push. Media data can be analyzed in order to obtain a semantic feature of a media content. Network behaviors of a user are also analyzed in order to obtain a preference feature of the user. Data matching, among the information to be pushed to the user, the semantic feature and the preference feature, is carried out so as to obtain quasi-recommendation information which is pushed to the user. According to the invention, the information comprising advertising information and meeting requirements is pushed to the user by combining the correlation on the semantic feature of the media content and the preference feature of the user, so as to improve an effect on information push and users' satisfaction.
Description
Technical field
The present invention relates to Internet technology, be specifically related to a kind of method and system of realizing information pushing.
Background technology
Along with the develop rapidly of the Internet, the web advertisement is with low cost because of it, be easy to upgrade and infinitely arrive the advantage such as potentiality is increasing with surprising rapidity, and the effectiveness of web advertisement performance more and more seems important.Yet when in the industry cycle generally web advertisement prospect being represented to have an optimistic view of, advertisement practitioner and website operators also are faced with the puzzlement of a series of difficult problems.
The legacy network advertisement lacks intention, performance only be simple Word message or picture.By contrast, rich-media ads has the following functions characteristics: the first, and novelty and interactive significantly improving of driving ad click rate.The second, property of participation and measurability help brand building.The 3rd, service has alleviated the prejudice of people to the web advertisement to a certain extent with being standardized in.
The novelty of rich-media ads and the interactive subproblem that solved to a certain extent, still, at present advertising message represent media content and the non-correlations such as text, image and video of paying close attention to the user, also not necessarily meet the user preference feature.The user can just close it before advertisement occurs usually, and ran into the mandatory advertisement that can not close, also had very strong psychology and conflicted.In the course of time, just formed so-called " advertisement blind area ", namely people turn a blind eye to the ubiquitous web advertisement, cause user satisfaction to reduce, and advertising results seriously reduce.
Summary of the invention
In view of this, main purpose of the present invention is to provide a kind of method and system of realizing information pushing, so as with satisfactory information pushing to the user, improve effect and user satisfaction that information pushing reaches.
For achieving the above object, technical scheme of the present invention is achieved in that
A kind of method that realizes information pushing is characterized in that, the method comprises:
Media data analysis is obtained the semantic feature of media content, also the analysis user network behavior is to obtain the user preference feature; The information that will be pushed to the user and described semantic feature, user preference feature are carried out Data Matching, finally be pushed to user's accurate recommendation information.
The described process that media data analysis is obtained the semantic feature of media content comprises: by the media content analysis system, the data in the network media are carried out feature extraction, obtain the semantic feature of media content, and deposit the semantic feature storehouse in;
Described analysis user network behavior comprises with the process that obtains the user preference feature: by user terminal the user network behavioral data is carried out collection analysis and process, form user preference information, and therefrom extract the user preference feature, deposit the user preference storehouse in.
When setting up the semantic feature storehouse of media content, the network media is carried out semantic feature extraction, form semantic feature information; Wherein, corresponding arrangement of mirrors head or the scene of each group semantic feature information becomes semantic characteristic set by the time series arrangement;
When setting up the user preference storehouse, collection analysis is carried out in the user network behavior process, extract the user preference feature and form the user preference storehouse, the final arrangement forms the user preference characteristic set.
Described process of carrying out Data Matching comprises:
Data Matching is carried out to obtain matching result in semantic feature storehouse and the user preference storehouse of storage semantic feature, and then matching result and advertisement base mated, obtain the information by the time sequence permutation, and choose corresponding information as accurate recommendation information according to the user interest degree.
The method also comprises: according to recommendation rules, described accurate recommendation information is pushed to user interface real-time dynamicly in suitable mode.
A kind of system that realizes information pushing, this system comprise media content analysis system, user preference analytical system, information matches system; Wherein,
Described media content analysis system is used for media data analysis is obtained the semantic feature of media content;
Described user preference analytical system is used for the analysis user network behavior to obtain the user preference feature;
Described information matches system is used for the information that will be pushed to the user and described semantic feature, user preference feature are carried out Data Matching, finally is pushed to user's accurate recommendation information.
Described media content analysis system, when media data analysis is obtained the semantic feature of media content, be used for: the data to the network media are carried out feature extraction, obtain the semantic feature of media content, and deposit the semantic feature storehouse in;
Described user preference analytical system,, be used for when obtaining the user preference feature at the analysis user network behavior: by user terminal the user network behavioral data is carried out collection analysis and process, form user preference information, and therefrom extract the user preference feature, deposit the user preference storehouse in.
When setting up the semantic feature storehouse of media content, described media content analysis system is used for the network media is carried out semantic feature extraction, forms semantic feature information; Wherein, corresponding arrangement of mirrors head or the scene of each group semantic feature information becomes semantic characteristic set by the time series arrangement;
When setting up the user preference storehouse, described user preference analytical system is used for that collection analysis is carried out in the user network behavior to be processed, and extracts the user preference feature and forms the user preference storehouse, and final the arrangement forms the user preference characteristic set.
Described information matches system is when carrying out Data Matching, be used for: Data Matching is carried out to obtain matching result in semantic feature storehouse and the user preference storehouse that will store semantic feature, and then matching result and advertisement base mated, obtain the information by the time sequence permutation, and choose corresponding information as accurate recommendation information according to the user interest degree.
This system also comprises information transmission system, is used for: according to recommendation rules, described accurate recommendation information is pushed to user interface real-time dynamicly in suitable mode.
The present invention realizes the technology of information pushing, can with the satisfactory information pushing that comprises advertising message to the user, improve effect and user satisfaction that information pushing reaches.
Description of drawings
Fig. 1 is the novel Rich Media intelligent advertisement entire system Organization Chart of the embodiment of the invention;
Fig. 2 is the media content analysis system construction drawing of the embodiment of the invention;
Fig. 3 is the user preference analytical system structure chart of the embodiment of the invention;
Fig. 4 is the advertisement recommended flowsheet figure of the embodiment of the invention;
Fig. 5 is the general flow chart that the embodiment of the invention realizes information pushing.
Embodiment
When practical application, can by in conjunction with media content correlation and user preference feature, information (such as advertising message) be pushed to user interface real-time dynamicly.The below describes the process that realizes propelling movement only take advertising message as example:
(1) sets up the semantic feature storehouse.By the media content analysis system, the data such as the text in the network media, image, video are carried out feature extraction, obtain the semantic feature of media content, and deposit the semantic feature storehouse in.
(2) set up the user preference storehouse that dynamically updates.The user preference analytical system is carried out the collection analysis processing by user terminal to the user network behavioral data, forms user preference information, and can therefrom extract the user preference feature, deposits the user preference storehouse in.The user preference information Real-time Collection, the user preference storehouse dynamically updates.
(3) form advertising message and recommend the storehouse.The information matches system carries out Data Matching with the advertising message in semantic feature, user preference feature and the advertisement base, extracts the advertising message of user preferences, forms accurate recommended advertisements information, deposits advertising message in and recommends the storehouse.
(4) by the advertisement pushing system, the advertising message accurate recommended advertisements information in the storehouse of recommending is pushed to user interface real-time dynamicly in suitable mode.In actual applications, advertisement pushing system belongs to information transmission system.
Particularly, when setting up the semantic feature storehouse of media content, can carry out semantic feature extraction to the network media, form semantic feature information.Corresponding arrangement of mirrors head or the scene of each group semantic feature information becomes semantic characteristic set by the time series arrangement:
{Media，Sematics，t}＝{(MediaID，S
1，t
1)，(MediaID，S
2，t
2)...(MediaID，S
k，t
k)...(MediaID，S
n，t
n)}；1≤k≤n；
Wherein, MediaID presentation medium sign; { (MediaID, S
1, t
1), (MediaID, S
2, t
2) ... (MediaID, S
k, t
k) ... (MediaID, S
n, t
n) expression network media the semantic feature arrangement set; S represents the semantic feature information of camera lens or scene; T represents the time started of camera lens or scene; (MediaID, S
1, t
1) when the expression user clicks the broadcast media, semantic feature information and the initial time of first paragraph camera lens or scene, t
1=0; (MediaID, S
n, t
n) semantic feature information and the time of expression when watching network media final stage camera lens or scene.
When setting up the user preference storehouse, can carry out collection analysis to the user network behavior and process, extract the user preference feature and form the user preference storehouse.Finally can arrange and form the user preference characteristic set:
{User，Preference，Degree}＝{(UserID，P
1，D
1)，(UserID，P
2，D
2)...(UserID，P
k，D
k)...(UserID，P
m，D
m)}；1≤k≤m，Dk＝0，1；
Wherein, UserID represents user ID; { (UserID, P
1, D
1), (UserID, P
2, D
2) ... (UserID, P
k, D
k) ... (UserID, P
m, D
m) expression user the preference characteristic set; P represents user's preference feature; D represents the interest-degree of user preference feature, and during D=1, the expression user is interesting to information, during D=0, then represents indifferent to.
When forming advertising message recommendation storehouse, Data Matching can be carried out to obtain matching result in semantic feature storehouse and the user preference storehouse of storage semantic feature, and then matching result and advertisement base mated, obtain one group by the advertising message of time sequence permutation, and choose corresponding advertising message as accurate recommended advertisements information according to user interest degree D.
Advertising message in the definition advertisement base is A, and accurate recommended advertisements information is in the advertising message recommendation storehouse
Then can obtain relational expression:
Accurate recommended advertisements information is arranged according to time series, forms the advertisement information aggregate:
Wherein,
The advertising message set that expression pushes by the time sequence the user;
Expression enters the advertising message that presents when media page is paid close attention to media content.
Below in conjunction with accompanying drawing the specific embodiment of the present invention is described.
Referring to Fig. 1, system shown in Figure 1 comprises media content analysis system, user preference analytical system, information matches system, advertisement pushing system, Database Systems etc.Media content analysis system processing media data, the semantic feature storehouse of formation media content; User preference analysis system processes user network behavioural information forms the user preference storehouse; The information matches system carries out Data Matching with semantic feature library information, user preference library information and advertisement base information, forms advertising message and recommends the storehouse; The advertisement pushing system recommends the accurate recommended advertisements information pushing in the storehouse to arrive user interface advertising message.
Referring to Fig. 2, system shown in Figure 2 is used for that the network media is carried out data characteristics to be extracted, and obtains the semantic feature of media content, and deposits the semantic feature storehouse in.
Particularly, the network media is carried out semantic feature extraction, form semantic feature information.Corresponding arrangement of mirrors head or the scene of each group semantic feature information becomes semantic characteristic set by the time series arrangement:
{Media，Sematics，t}＝{(MediaID，S
1，t
1)，(MediaID，S
2，t
2)...(MediaID，S
k，t
k)...(MediaID，S
n，t
n)}；1≤k≤n；
For example, be that Media100, length are cut apart for the video media of 120s carries out scene to one section MediaID, be divided into five sections scenes, extract the semantic feature information of every section scene, form semantic feature set { (Media100, music, 0), (Media100, tourism, 20), (Media100, household, 68), (Media100, cuisines, 82), (Media100, network game, 106) }.At 0-20s, the semantic feature information of first paragraph scene is music; At 20s-68s, the semantic feature information of second segment scene is tourism; At 68s-82s, the semantic feature information of the 3rd section scene is household; At 82s-106s, the semantic feature information of the 4th section scene is cuisines; At 106s-120s, the semantic feature information of the 5th section scene is network game.
Referring to Fig. 3, system shown in Figure 3 is used for by user terminal the user network behavioral data being carried out collection analysis to be processed, and forms user preference information, therefrom extracts the user preference feature, deposits the user preference storehouse in.The user preference information Real-time Collection, the user preference storehouse dynamically updates.Finally can arrange and form the user preference characteristic set:
{User，Preference，Degree}＝{(UserID，P
1，D
1)，(UserID，P
2，D
2)...(UserID，P
k，D
k)...(UserID，P
m，D
m)}；1≤k≤m，D
k＝0，1；
For example, the UserID user's that is User8 user preference information carried out feature extraction after, form user preference characteristic set as shown in table 1 { (User8, music, 1) according to the theme representation, (User8, football, 1), (User8, tourism, 1), (User8, sport car, 0), (User8, cuisines, 1), (User8, household, 0), (User8, stock, 0), (User8, network game, 0) }.
| D (interest-degree) | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 |
| P (preference feature) | Music | Football | Tourism | Sport car | Cuisines | Household | Stock | Network game |
Table 1
Then, the information matches system carries out Data Matching with the advertising message in semantic feature, user preference feature and the advertisement base, extracts the advertising message of user preferences, forms accurate recommended advertisements information, deposits advertising message in and recommends the storehouse.Advertising message in the definition advertisement base is A, and accurate recommended advertisements information is in the advertising message recommendation storehouse
Can obtain relational expression:
Accurate recommended advertisements information is arranged according to time series, forms the advertisement information aggregate:
For example, advertising message set as shown in table 2 { dandruff control shampoo containing, guitar concert admission ticket, the steel nail football boot, chafing dish, furniture store's household, elaboration purchases by group net, roadster for green tea, countryside one-day tour } is arranged in advertisement base.
| Theme | Advertising message |
| Music | The guitar concert |
| Tourism | The countryside one-day tour |
| Household | The furniture store |
| Sport car | Roadster |
| Purchase by group | Elaboration purchases by group net |
| Shampoo | Dandruff control shampoo containing |
| Football | The steel nail football boot |
| Cuisines | Chafing dish |
Table 2
Referring to Fig. 4, among Fig. 4, according to recommendation rules, the advertisement pushing system is pushed to user interface with the advertising message accurate recommended advertisements information in the storehouse of recommending real-time dynamicly in suitable mode.Particularly, can judge whether advertising message meets the media Semantic Similarity, if do not meet, recommended advertisements information not; If meet, continue to judge whether advertising message meets the user preference feature, if meet, recommended advertisements information; Otherwise, recommended advertisements information not.
For user User8, network media Media100, the advertising message of propelling movement can be as shown in table 3:
| Time point t | 0 | 20 | 68 | 82 | 106 |
| Advertising message A | The guitar concert | The countryside one-day tour | Nothing | Chafing dish | Nothing |
Table 3
Wherein, user Ueser8 plays media Media100,
When 0s, the concert of showing advertisements guitar;
When 20s, advertisement switches to the countryside one-day tour;
When 68s, without advertising message;
When 82s, advertisement switches to chafing dish;
At 106 o'clock, without advertising message until media play finish.
That is, the advertising message of propelling movement can embody in the mode of advertisement information aggregate.
In conjunction with above description as seen, no matter be advertising message or the out of Memory that need to be pushed to the user, the present invention realizes that the operation thinking of information pushing can represent flow process as shown in Figure 5, this flow process may further comprise the steps:
Step 510: media data analysis is obtained the semantic feature of media content, also the analysis user network behavior is to obtain the user preference feature.
Step 520: the information that be pushed to the user and described semantic feature, user preference feature are carried out Data Matching, finally be pushed to user's accurate recommendation information.
In sum as seen, no matter be method or system, the present invention realizes the technology of information pushing, can with the satisfactory information pushing that comprises advertising message to the user, improve effect and user satisfaction that information pushing reaches.
The above is preferred embodiment of the present invention only, is not for limiting protection scope of the present invention.
Claims (10)
1. a method that realizes information pushing is characterized in that, the method comprises:
Media data analysis is obtained the semantic feature of media content, also the analysis user network behavior is to obtain the user preference feature; The information that will be pushed to the user and described semantic feature, user preference feature are carried out Data Matching, finally be pushed to user's accurate recommendation information.
2. method according to claim 1 is characterized in that,
The described process that media data analysis is obtained the semantic feature of media content comprises: by the media content analysis system, the data in the network media are carried out feature extraction, obtain the semantic feature of media content, and deposit the semantic feature storehouse in;
Described analysis user network behavior comprises with the process that obtains the user preference feature: by user terminal the user network behavioral data is carried out collection analysis and process, form user preference information, and therefrom extract the user preference feature, deposit the user preference storehouse in.
3. method according to claim 2 is characterized in that,
When setting up the semantic feature storehouse of media content, the network media is carried out semantic feature extraction, form semantic feature information; Wherein, corresponding arrangement of mirrors head or the scene of each group semantic feature information becomes semantic characteristic set by the time series arrangement;
When setting up the user preference storehouse, collection analysis is carried out in the user network behavior process, extract the user preference feature and form the user preference storehouse, the final arrangement forms the user preference characteristic set.
4. according to claim 2 or 3 described methods, it is characterized in that, described process of carrying out Data Matching comprises:
Data Matching is carried out to obtain matching result in semantic feature storehouse and the user preference storehouse of storage semantic feature, and then matching result and advertisement base mated, obtain the information by the time sequence permutation, and choose corresponding information as accurate recommendation information according to the user interest degree.
5. method according to claim 1 is characterized in that, the method also comprises: according to recommendation rules, described accurate recommendation information is pushed to user interface real-time dynamicly in suitable mode.
6. a system that realizes information pushing is characterized in that, this system comprises media content analysis system, user preference analytical system, information matches system; Wherein,
Described media content analysis system is used for media data analysis is obtained the semantic feature of media content;
Described user preference analytical system is used for the analysis user network behavior to obtain the user preference feature;
Described information matches system is used for the information that will be pushed to the user and described semantic feature, user preference feature are carried out Data Matching, finally is pushed to user's accurate recommendation information.
7. system according to claim 6 is characterized in that,
Described media content analysis system, when media data analysis is obtained the semantic feature of media content, be used for: the data to the network media are carried out feature extraction, obtain the semantic feature of media content, and deposit the semantic feature storehouse in;
Described user preference analytical system,, be used for when obtaining the user preference feature at the analysis user network behavior: by user terminal the user network behavioral data is carried out collection analysis and process, form user preference information, and therefrom extract the user preference feature, deposit the user preference storehouse in.
8. system according to claim 7 is characterized in that,
When setting up the semantic feature storehouse of media content, described media content analysis system is used for the network media is carried out semantic feature extraction, forms semantic feature information; Wherein, corresponding arrangement of mirrors head or the scene of each group semantic feature information becomes semantic characteristic set by the time series arrangement;
When setting up the user preference storehouse, described user preference analytical system is used for that collection analysis is carried out in the user network behavior to be processed, and extracts the user preference feature and forms the user preference storehouse, and final the arrangement forms the user preference characteristic set.
9. according to claim 7 or 8 described systems, it is characterized in that, described information matches system is when carrying out Data Matching, be used for: Data Matching is carried out to obtain matching result in semantic feature storehouse and the user preference storehouse that will store semantic feature, and then matching result and advertisement base mated, obtain the information by the time sequence permutation, and choose corresponding information as accurate recommendation information according to the user interest degree.
10. system according to claim 6 is characterized in that, this system also comprises information transmission system, is used for: according to recommendation rules, described accurate recommendation information is pushed to user interface real-time dynamicly in suitable mode.
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