CN109214418A - The method for digging and device, computer equipment and readable medium that user is intended to - Google Patents
The method for digging and device, computer equipment and readable medium that user is intended to Download PDFInfo
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Abstract
The present invention provides a kind of method for digging and device, computer equipment and readable medium that user is intended to.Its method includes: the historical behavior data according to user, excavates several intent informations of user；Obtain the sequence index of each intent information；According to the sequence index of each intent information, N number of target intention information is extracted from several intent informations, to carry out advertisement recommendation.Technical solution of the present invention can make up for it the deficiencies in the prior art, accurately and efficiently excavate the intent information of user, in order to which information carries out effectively advertisement recommendation according to the user's intention, recommend efficiency so as to effectively improve advertisement.
Description
[technical field]
The present invention relates to method for digging and device, meter that computer application technology more particularly to a kind of user are intended to
Calculate machine equipment and readable medium.
[background technique]
With the propulsion of time of cell-phone, content ecology flourishes, and more and more displaying class scene flows measure present people
In daily life, advertiser has a mind to obtain more displaying class business flows.
In the prior art, under information flow scene, purpose of the user primarily for browsing, no explicitly retrieval behavior,
Advertiser can not directly acquire the intention of user.Mostly just according to some characteristic informations of user such as age, occupational identity, emerging
The features such as interest hobby carry out advertisement recommendation to user.But under this scene, since the real intention of user can not be obtained,
Cause the advertisement not necessarily user recommended really to want to click the advertisement checked in browsing, recommends to imitate so as to cause advertisement
Rate is lower.
Based on the above issues, the present invention it is urgent to provide under a kind of information flow scene user be intended to excavation scheme, so as to
In being intended to progress advertisement recommendation according to user, so that further increasing advertisement recommends efficiency.
[summary of the invention]
The present invention provides method for digging and device, computer equipment and readable medium that a kind of user is intended to, for more
The deficiencies in the prior art are mended, a kind of excavation scheme that effectively user is intended to is provided.
The present invention provides a kind of method for digging that user is intended to, which comprises
According to the historical behavior data of user, several intent informations of the user are excavated；
Obtain the sequence index of each intent information；
According to the sequence index of each intent information, N number of target intention information is extracted from several intent informations,
To carry out advertisement recommendation.
Still optionally further, in method as described above, the sequence index of each intent information is obtained, is specifically included:
It is pre- according to each corresponding historical behavior data of intent information of the user and intention index trained in advance
Model is surveyed, predicts the intention index of each intent information.
Still optionally further, in method as described above, the sequence index of each intent information is obtained, is specifically included:
According to ad play history data gathered in advance, obtain the corresponding advertisement broadcasting time of each intent information and
Advertisement value；
According to each corresponding advertisement broadcasting time of intent information and the advertisement value, each intention is obtained
The corresponding value index nember of information.
Still optionally further, in method as described above, the sequence index of each intent information is obtained, is specifically included:
Obtain the corresponding intention index of each intent information and value index nember；
According to each corresponding intention index of intent information and the value index nember, each intent information is obtained
Sequence index.
Still optionally further, in method as described above, according to the historical behavior data of user, the number of the user is excavated
A intent information, specifically includes:
Intention trained according to the historical behavior data of the user and in advance extracts model, excavates the user's
Several intent informations.
Still optionally further, in method as described above, according to the historical behavior data of the user and preparatory instruction
Experienced intention extracts model, excavates several intent informations of the user, specifically includes:
Obtain the corresponding text feature information of the historical behavior data of the user；
Model is extracted according to the corresponding text feature information of the historical behavior data of the user and the intention, in advance
Survey several intent informations of the user.
Still optionally further, in method as described above, the corresponding text of the historical behavior data of the user is obtained
Eigen information, specifically includes:
Obtain the title and crucial content of text of the corresponding browsing pages of the historical behavior data；
The title of the browsing pages is segmented, multiple title participles are obtained；
Model is extracted using content keyword trained in advance, and multiple contents keys are extracted from the crucial content of text
Word；
By the multiple title participle and the combination of the multiple content keyword, the text of the corresponding browsing pages is formed
Eigen information.
The present invention provides a kind of excavating gear that user is intended to, and described device includes:
It excavates module and excavates several intent informations of the user for the historical behavior data according to user；
Module is obtained, for obtaining the sequence index of each intent information；
Extraction module extracts N number of for the sequence index according to each intent information from several intent informations
Target intention information, to carry out advertisement recommendation.
Still optionally further, in device as described above, the acquisition module, specifically for each institute according to the user
The corresponding historical behavior data of intent information and intention Index Prediction Model trained in advance are stated, predict each intent information
It is intended to index.
Still optionally further, in device as described above, the acquisition module is specifically used for:
According to ad play history data gathered in advance, obtain the corresponding advertisement broadcasting time of each intent information and
Advertisement value；
According to each corresponding advertisement broadcasting time of intent information and the advertisement value, each intention is obtained
The corresponding value index nember of information.
Still optionally further, in device as described above, the acquisition module is specifically used for:
Obtain the corresponding intention index of each intent information and value index nember；
According to each corresponding intention index of intent information and the value index nember, each intent information is obtained
Sequence index.
Still optionally further, in device as described above, the excavation module, specifically for according to the user
Historical behavior data and intention trained in advance extract model, excavate several intent informations of the user.
Still optionally further, in device as described above, the excavation module is specifically used for:
Obtain the corresponding text feature information of the historical behavior data of the user；
Model is extracted according to the corresponding text feature information of the historical behavior data of the user and the intention, in advance
Survey several intent informations of the user.
Still optionally further, in device as described above, the excavation module is specifically used for:
Obtain the title and crucial content of text of the corresponding browsing pages of the historical behavior data；
The title of the browsing pages is segmented, multiple title participles are obtained；
Model is extracted using content keyword trained in advance, and multiple contents keys are extracted from the crucial content of text
Word；
By the multiple title participle and the combination of the multiple content keyword, the text of the corresponding browsing pages is formed
Eigen information.
The present invention also provides a kind of computer equipment, the equipment includes:
One or more processors；
Memory, for storing one or more programs；
When one or more of programs are executed by one or more of processors, so that one or more of processing
Device realizes the method for digging that user as described above is intended to.
The present invention also provides a kind of computer-readable mediums, are stored thereon with computer program, which is held by processor
The method for digging that user as described above is intended to is realized when row.
The method for digging and device, computer equipment and readable medium that user of the invention is intended to, by according to user's
Historical behavior data excavate several intent informations of user；Obtain the sequence index of each intent information；According to each intent information
Sort index, extracts N number of target intention information, from several intent informations to carry out advertisement recommendation.Technical solution of the present invention,
It can make up for it the deficiencies in the prior art, accurately and efficiently excavate the intent information of user, in order to information according to the user's intention
It carries out effectively advertisement to recommend, recommends efficiency so as to effectively improve advertisement.
[Detailed description of the invention]
Fig. 1 is the flow chart for the method for digging embodiment one that user of the invention is intended to.
Fig. 2 is the flow chart for the method for digging embodiment two that user of the invention is intended to.
Fig. 3 is the flow chart for the method for digging embodiment three that user of the invention is intended to.
Fig. 4 is the flow chart for the method for digging example IV that user of the invention is intended to.
Fig. 5 is the structure chart of information processing unit embodiment two of the invention.
Fig. 6 is the structure chart of computer equipment embodiment of the invention.
Fig. 7 is a kind of exemplary diagram of computer equipment provided by the invention.
[specific embodiment]
To make the objectives, technical solutions, and advantages of the present invention clearer, right in the following with reference to the drawings and specific embodiments
The present invention is described in detail.
Fig. 1 is the flow chart for the method for digging embodiment one that user of the invention is intended to.As shown in Figure 1, the present embodiment
The method for digging that user is intended to, can specifically include following steps:
100, according to the historical behavior data of user, several intent informations of user are excavated；
101, the sequence index of each intent information is obtained；
102, according to the sequence index of each intent information, N number of target intention information is extracted from several intent informations, with into
Row advertisement is recommended.
The executing subject for the method for digging that the user of the present embodiment is intended to is user intention mining device, which is intended to dig
Pick device can be set in the server, can get all historical behavior data of serviced user, thus based on using
All historical behavior data at family, excavate the intent information effectively of user.
Specifically, when the excavation that the user of the present embodiment is intended to, the historical behavior data according to user are first had to, are excavated each
Several intent informations of user.For example, in the present embodiment, can be acquired each to obtain the real-time intent information of user
The historical time period of historical behavior data of the user within the historical time period of arest neighbors, arest neighbors can come according to demand
Setting, such as can be the other times cycle length of 8 hours, 24 hours, one week, one month or meet demand, herein not
It limits.In addition, the historical behavior data of the present embodiment may include search history behavioral data when user scans for, also
Contextual information when may include user's browsing pages.Search history behavioral data when wherein searching for can directly react use
The intent information at family.In contextual information when user's browsing pages, each, which clicks the browsing pages checked, to be implied
When having the tendency that user's browsing, to imply the intent information for having user.
In the present embodiment, according to the historical behavior data of user, when excavating several intent informations of user, can directly from
In each historical behavior data of user, semantic parsing is carried out, the intent information of user is obtained.For example, if historical behavior number
When according to for search history behavioral data, the intent information of user, which is excavated, to be very easy to, and is generally comprised in search term.And based on use
When intent information of the page to excavate user of family browsing, the content for needing to analyze is more, can just get the intention letter of user
Breath, cause the excavation of user intent information to take a long time, digging efficiency it is lower.
Based on this problem, in the present embodiment, can also provide when a kind of historical behavior data are the browsing pages of user
The excavation scheme of the intent information of user.In the present embodiment, an intention can be trained to extract model in advance, to be based on the meaning
Figure extracts model realization and excavates to the intention of the user in the corresponding browsing pages of historical behavior data.For example, at this time should
Step 100 is specifically as follows: intention trained according to the historical behavior data of user and in advance extracts model, excavates the number of user
A intent information.
Further, " intention trained according to the historical behavior data of user and in advance extracts model to the step, excavates and uses
Several intent informations at family ", can specifically include following steps:
(a1) the corresponding text feature information of historical behavior data of user is obtained；
For example, each historical behavior data of user can be indicated using a corresponding text feature information,
This text feature information can be identified using one group of word.For example, the step (a1) can specifically include following steps:
(a2) title and crucial content of text of the corresponding browsing pages of historical behavior data are obtained；
In the present embodiment, each historical behavior data of user correspond to a browsing pages, each browsing pages
It is corresponding there are a title, foundation of the title as user intention mining of the browsing pages is obtained in the present embodiment.Another party
Can also there be the crucial content of text that one section of content of text is the browsing pages in the browsing pages of face, it is clear can effectively to summarize this
Look at the content of the page.In practical application, if include abstract in browsing pages, the crucial content of text of the browsing pages can be
Abstract in browsing pages.If do not include abstract in browsing pages, the first segment based on browsing pages can generally also summarize
The content of the browsing pages is introduced, crucial content of text of the first segment as browsing pages of browsing pages can be chosen at this time.
Or other sections can also be selected according to the characteristic of browsing pages, the crucial content of text such as final stage as browsing pages.
(b2) title of browsing pages is segmented, obtains multiple title participles；
The participle of the present embodiment can use participle technique commonly used in the prior art, and the title of browsing pages is split as
Multiple title participles, can refer to related art, details are not described herein in detail.
(c2) model being extracted using content keyword trained in advance, multiple contents keys are extracted from crucial content of text
Word；
In the present embodiment, it is also necessary to train a content keyword to extract model in advance, it can be from one section of content of text
Extract the multiple content keywords that can be identified for that this section of content.Based on the principle, mould can be extracted using the content keyword
Type extracts multiple content keywords from crucial content of text.
When the content keyword of the present embodiment extracts model training, it can be carried out using the training principle of neural network model
Training.Preparatory acquisition training is needed it is anticipated that may include hundreds of thousands training data in the training expectation of such as the present embodiment, is instructed
The quantity for practicing data is more, and trained content keyword extracts the more accurate of model extraction.Each training number of the present embodiment
It may include one section of training text gathered in advance and multiple content keywords in.
When training, each training data is input to the content keyword and is extracted in model, obtained content keyword and mention
Take multiple content keywords of model extraction.Then judge multiple content keywords of the multiple content keyword languages extracted acquisition
Whether consistent, if inconsistent, modification content keyword extracts the parameter of model, so that the multiple content keyword languages acquisition extracted
Multiple content keywords it is consistent.According to above-mentioned training method, using all training datas to content keyword extraction model
It is trained, until multiple content keywords of extraction and multiple content keywords of acquisition are consistent, training is finished, and determines content
The parameter of keyword extraction model, so that it is determined that content keyword extracts model.
(d2) multiple titles participle and multiple content keywords are combined, forms the text feature letter of corresponding browsing pages
Breath.
That is, the text feature information of each historical behavior data includes that multiple title participles and multiple contents are closed
Keyword, these information all most possibly identify the content of the corresponding browsing pages of corresponding historical behavior data.
(b1) model is extracted according to the corresponding text feature information of the historical behavior data of user and intention, predicts user's
Several intent informations.
For each historical behavior data of user, by its corresponding text feature information input to meaning trained in advance
Figure extracts in model, it is intended that extracts the model prediction corresponding intent information of historical behavior data.In this way, based on same user's
A plurality of historical behavior data, several intent informations of the available user.
The training principle that the intention of the present embodiment extracts model is similar to above content keyword extraction model training, needs
Acquisition training corpus in advance, similarly may include hundreds of thousands training data in training corpus, can be in each training data
Corresponding intent information including training text characteristic information and acquisition.When then training, by the instruction in each training data
Practice text feature information input to the intention to extract in model, which extracts its intent information of model prediction, then will prediction
Intent information and the intent information of acquisition compare, whether consistent both judge, if inconsistent, adjustment is intended to extract model
Parameter so that the two is consistent.According to above-mentioned training method, instructed using all training datas to extraction model is intended to
Practice, until the intent information of prediction and the intent information of acquisition are consistent, training is finished, determine the parameter for being intended to extract model, from
And it determines intention and extracts model.
It still optionally further, will if all obtain more intent information in each historical behavior data of user
The quantity that will lead to the intent information of the user finally obtained is more.It at this time can be in each historical behavior for excavating user
After the corresponding intent information of data, each intent information text feature information corresponding with this historical behavior data is judged
Semantic similarity retains the corresponding intention letter of this historical behavior data if similarity is greater than default semantic similarity threshold value
Breath, otherwise, deletes the corresponding intent information of this historical behavior data.Intention corresponding for each historical behavior data
Information carries out after as above handling, and can effectively delete the inappropriate intent information in part of user, retains more appropriate, accurate
Intent information.
According to the mode of above-described embodiment, excavate to after several intent informations of user, it can be according to the number of user
A intent information carries out advertisement recommendation.If but several intent information quantity of user are more, also will cause the recommendation effect of advertisement
Rate is lower.Therefore, in the present embodiment, the sequence index of each intent information of user can also be obtained, and then believe according to each intention
The sequence index of breath extracts N number of target intention information from several intent informations, to carry out advertisement recommendation.It in this way can be into one
Step improves advertisement and recommends efficiency.
The sequence index of the intent information of the present embodiment may include that there are many modes, for example, can be according to user's history
Sequence sequence of the last moment of the corresponding page of every kind of intent information of upper browsing apart from current time from the near to the remote, or may be used also
Used in aforesaid way according to the descending sequence sequence of the corresponding advertisement number of every kind of intent information is clicked in user's history
Family browses the last moment of the corresponding page of every kind of intent information in history and user to click every kind of intent information corresponding wide
Number is accused, can be obtained from the historical behavior data of user.
In addition, the sequence index of the intent information of the present embodiment can also be according to the valence of the corresponding advertisement of every kind of intent information
Value, or the modes such as power can also be intended to according to the corresponding user of every kind of intent information to characterize.In practical application, may be used yet
To characterize sequence index using other specified parameters, no longer citing is repeated one by one herein.
It, can be according to each intent information if the quantity of several intent informations of user is more in information flow scene
Sort index, N number of target intention information can be extracted from several intent informations according to the sequence of sequence index from big to small,
In this way, the N number of target intention information extracted, the specific aim for carrying out advertisement recommendation is stronger, wide so as to effectively improve
The accuracy recommended is accused, and then increases the clicking rate of advertisement, really realizes the commercial intention and commercial value of advertisement.
The method for digging that the user of the present embodiment is intended to excavates the number of user by the historical behavior data according to user
A intent information；Obtain the sequence index of each intent information；According to the sequence index of each intent information, from several intent informations
N number of target intention information is extracted, to carry out advertisement recommendation.The technical solution of the present embodiment, can make up for it the deficiencies in the prior art,
The intent information of user is accurately and efficiently excavated, in order to which information carries out effectively advertisement recommendation according to the user's intention, thus
Advertisement can be effectively improved and recommend efficiency.
Fig. 2 is the flow chart for the method for digging embodiment two that user of the invention is intended to.As shown in Fig. 2, the present embodiment
The method for digging that user is intended to, on the basis of the technical solution of above-mentioned embodiment illustrated in fig. 1, to sort, index is intended to index
For, to describe technical solution of the present invention.As shown in Fig. 2, the method for digging that the user of the present embodiment is intended to, specifically can wrap
Include following steps:
200, according to the historical behavior data of user, several intent informations of user are excavated；
201, the intention exponential forecasting mould trained according to the corresponding historical behavior data of each intent information of user and in advance
Type predicts the intention index of each intent information；
202, according to the intention index of each intent information, N number of target intention information is extracted from several intent informations, with into
Row advertisement is recommended.
In the present embodiment, step 200 and 202 specific implementation, can respectively refer to above-mentioned embodiment illustrated in fig. 1 the step of
100 and 102, details are not described herein.
In the present embodiment, intention Index Prediction Model trained according to the historical behavior data of user and in advance, prediction is respectively
It may include user's basic feature information in the historical behavior data of user, such as the year of user when the intention index of intent information
The features such as age, gender, identity occupation, hobby, these features can reflect intent information and user to a certain extent
Correlation degree, to embody the power that user browses the intention of the intent information related content.It can also include the intention of user
The item number of the item number of the corresponding historical behavior data of information, the corresponding historical behavior data of certain intent information of the user is got over
It is more, illustrate the user browse the intent information related content intention it is stronger；Vice versa.Furthermore it is also possible to include user
At the time of browsing the intent information corresponding page, if the moment is closer apart from current time, it is possible to understand that user browsing should
The intention of intent information related content is stronger；Vice versa.It, can be in the historical behavior data of user in practical application
User can be characterized including other and is intended to strong and weak information, and no longer citing repeats one by one herein.
Then, for each intent information, the corresponding historical behavior data of the intent information for the user that will acquire
It is input in intention Index Prediction Model trained in advance, which can predict the intention of the user
The intention index of information.Be intended to index it is stronger, indicate the user browse the relevant content of the intent information wish it is stronger, the meaning
The probability that the relevant advertisement of figure information is clicked by the user is bigger.
The intention Index Prediction Model of the present embodiment is in training, it is also desirable to which acquisition hundreds of thousands intent information is corresponding in advance
Historical behavior data and corresponding intention index.Then using in above-described embodiment content keyword extract model and
It is intended to extract the identical training method of training process of model, which is trained, it is no longer superfluous herein
It states.
In the present embodiment, gets and be intended to after index, according to the intention index of each intent information, from several intent informations
It is middle to extract N number of target intention information, to carry out advertisement recommendation.
The method for digging that the user of the present embodiment is intended to can make up for it the prior art by using above-mentioned technical proposal
Deficiency accurately and efficiently excavates the intent information of user, in order to which effectively advertisement pushes away for information progress according to the user's intention
It recommends, recommends efficiency so as to effectively improve advertisement.
Fig. 3 is the flow chart for the method for digging embodiment three that user of the invention is intended to.As shown in figure 3, the present embodiment
The method for digging that user is intended to is value index nember with the index that sorts on the basis of the technical solution of above-mentioned embodiment illustrated in fig. 1
For, to describe technical solution of the present invention.As shown in figure 3, the method for digging that the user of the present embodiment is intended to, specifically can wrap
Include following steps:
300, according to the historical behavior data of user, several intent informations of user are excavated；
In the present embodiment, the specific implementation of the step can refer to the step 100 of above-mentioned embodiment illustrated in fig. 1 in detail,
This is repeated no more.
301, according to ad play history data gathered in advance, obtain the corresponding advertisement broadcasting time of each intent information and
Advertisement value；
302, according to the corresponding advertisement broadcasting time of each intent information and advertisement value, the corresponding valence of each intent information is calculated
Value index number；
Step 301 and 302 is that the step 101 of above-mentioned embodiment illustrated in fig. 1 obtains the one of the sequence index of each intent information
Kind implementation.
In the present embodiment, each advertisement is all corresponded to there are at least one intent information, and such advertiser can be according to meaning
Figure information buys advertisement, when user has the intent information, pushes the corresponding advertisement of the intent information, realizes the intelligence of advertisement
Push.The advertisement that server is recommended plays record, can all store in ad play history data.In this way, by acquiring in advance
Ad play history data, then according to ad play history data, the corresponding advertisement of available to every kind intent information
Broadcasting time and advertisement value；Advertisement value is brought cash earnings in the advertisement playing process of the intent information.Advertisement
Broadcasting time is the number that the corresponding all advertisements of the intent information play in total.In the present embodiment, every kind of intention can be believed
The advertisement value of breath is corresponding to characterize the intent information as the value index nember of the intent information divided by advertisement broadcasting time
Advertisement is every to play primary, average bring value income.
303, according to the value index nember of each intent information, N number of target intention information is extracted from several intent informations, with into
Row advertisement is recommended.
In the present embodiment, after getting value index nember, according to the value index nember of each intent information, from several intent informations
It is middle to extract N number of target intention information, to carry out advertisement recommendation.
The method for digging that the user of the present embodiment is intended to can make up for it the prior art by using above-mentioned technical proposal
Deficiency accurately and efficiently excavates the intent information of user, in order to which effectively advertisement pushes away for information progress according to the user's intention
It recommends, recommends efficiency so as to effectively improve advertisement.
Fig. 4 is the flow chart for the method for digging example IV that user of the invention is intended to.As shown in figure 4, the present embodiment
User be intended to method for digging, on the basis of the technical solution of above-mentioned embodiment illustrated in fig. 1, with sort index and meanwhile include anticipate
For figure index and value index nember, to describe technical solution of the present invention.As shown in figure 4, the digging that the user of the present embodiment is intended to
Pick method, can specifically include following steps:
400, according to the historical behavior data of user, several intent informations of user are excavated；
In the present embodiment, the specific implementation of the step can refer to the step 100 of above-mentioned embodiment illustrated in fig. 1 in detail,
This is repeated no more.
401, the corresponding intention index of each intent information and value index nember are obtained；
Wherein obtain each intent information it is corresponding be intended to index can with reference to the step 210 in above-mentioned embodiment illustrated in fig. 2,
Details are not described herein.
Wherein obtaining the corresponding value index nember of each intent information can be with reference to the step 301 in above-mentioned embodiment illustrated in fig. 3
With 302, details are not described herein.
402, according to the corresponding intention index of each intent information and value index nember, the sequence index of each intent information is obtained；
For example, the result that the sequence index in the present embodiment can be multiplied for intention index with value index nember.Practical application
In, index can also be intended to and be added with value index nember, be either added or be multiplied according to different weights, no longer one at one stroke herein
Example repeats..
403, according to the sequence index of each intent information, N number of target intention information is extracted from several intent informations, with into
Row advertisement is recommended.
The method for digging that the user of the present embodiment is intended to can make up for it the prior art by using above-mentioned technical proposal
Deficiency accurately and efficiently excavates the intent information of user, in order to which effectively advertisement pushes away for information progress according to the user's intention
It recommends, recommends efficiency so as to effectively improve advertisement.
Fig. 5 is the structure chart for the excavating gear embodiment that user of the invention is intended to.As shown in figure 5, the use of the present embodiment
The excavating gear that family is intended to, can specifically include:
Module 10 is excavated for the historical behavior data according to user, excavates several intent informations of user；
Obtain the sequence index that module 11 is used to obtain each intent information；
Extraction module 12 is used to be dug according to the sequence index for obtaining each intent information that module 11 obtains from module 10 is excavated
N number of target intention information is extracted in several intent informations of pick, to carry out advertisement recommendation.
The excavating gear that the user of the present embodiment is intended to realizes the realization for the excavation that user is intended to by using above-mentioned module
Principle and technical effect are identical as the realization of above-mentioned related method embodiment, can refer to above-mentioned related method embodiment in detail
Record, details are not described herein.
Still optionally further, in the excavating gear that the user in above-mentioned embodiment illustrated in fig. 5 is intended to, it is specific to obtain module 11
Following any operation can be executed:
Module 11 is obtained to be specifically used for according to the corresponding historical behavior data of each intent information of user and trained in advance
It is intended to Index Prediction Model, predicts the intention index of each intent information.
Module 11 is obtained to be specifically used for that it is corresponding to obtain each intent information according to ad play history data gathered in advance
Advertisement broadcasting time and advertisement value；According to the corresponding advertisement broadcasting time of each intent information and advertisement value, each intention is obtained
The corresponding value index nember of information.
Module 11 is obtained to be specifically used for obtaining the corresponding intention index of each intent information and value index nember；Believed according to each intention
Corresponding intention index and value index nember are ceased, the sequence index of each intent information is obtained.
Still optionally further, in the excavating gear that the user in above-mentioned embodiment illustrated in fig. 5 is intended to, it is specific to excavate module 10
For extracting model according to the historical behavior data and intention trained in advance of user, several intent informations of user are excavated.
Still optionally further, in the excavating gear that the user in above-mentioned embodiment illustrated in fig. 5 is intended to, it is specific to excavate module 10
For:
Obtain the corresponding text feature information of historical behavior data of user；
According to the corresponding text feature information of the historical behavior data of user and it is intended to extract model, predicts that user's is several
Intent information.
Still optionally further, in the excavating gear that the user in above-mentioned embodiment illustrated in fig. 5 is intended to, it is specific to excavate module 10
For:
Obtain the title and crucial content of text of the corresponding browsing pages of historical behavior data；
The title of browsing pages is segmented, multiple title participles are obtained；
Model is extracted using content keyword trained in advance, and multiple content keywords are extracted from crucial content of text；
By multiple titles participle and the combination of multiple content keywords, the text feature information of corresponding browsing pages is formed.
The excavating gear that the user of the present embodiment is intended to realizes the realization for the excavation that user is intended to by using above-mentioned module
Principle and technical effect are identical as the realization of above-mentioned related method embodiment, can refer to above-mentioned related method embodiment in detail
Record, details are not described herein.
Fig. 6 is the structure chart of computer equipment embodiment of the invention.As shown in fig. 6, the computer equipment of the present embodiment,
It include: one or more processors 30 and memory 40, memory 40 works as memory for storing one or more programs
The one or more programs stored in 40 are executed by one or more processors 30, so that one or more processors 30 are realized such as
The method for digging that figure 1 above-embodiment illustrated in fig. 4 user is intended to.To include that multiple processors 30 are in embodiment illustrated in fig. 6
Example.
For example, Fig. 7 is a kind of exemplary diagram of computer equipment provided by the invention.Fig. 7, which is shown, to be suitable for being used to realizing this
The block diagram of the exemplary computer device 12a of invention embodiment.The computer equipment 12a that Fig. 7 is shown is only an example,
Should not function to the embodiment of the present invention and use scope bring any restrictions.
As shown in fig. 7, computer equipment 12a is showed in the form of universal computing device.The component of computer equipment 12a can
To include but is not limited to: one or more processor 16a, system storage 28a connect different system components (including system
Memory 28a and processor 16a) bus 18a.
Bus 18a indicates one of a few class bus structures or a variety of, including memory bus or Memory Controller,
Peripheral bus, graphics acceleration port, processor or the local bus using any bus structures in a variety of bus structures.It lifts
For example, these architectures include but is not limited to industry standard architecture (ISA) bus, microchannel architecture (MAC)
Bus, enhanced isa bus, Video Electronics Standards Association (VESA) local bus and peripheral component interconnection (PCI) bus.
Computer equipment 12a typically comprises a variety of computer system readable media.These media can be it is any can
The usable medium accessed by computer equipment 12a, including volatile and non-volatile media, moveable and immovable Jie
Matter.
System storage 28a may include the computer system readable media of form of volatile memory, such as deposit at random
Access to memory (RAM) 30a and/or cache memory 32a.Computer equipment 12a may further include it is other it is removable/
Immovable, volatile/non-volatile computer system storage medium.Only as an example, storage system 34a can be used for reading
Write immovable, non-volatile magnetic media (Fig. 7 do not show, commonly referred to as " hard disk drive ").Although being not shown in Fig. 7,
The disc driver for reading and writing to removable non-volatile magnetic disk (such as " floppy disk ") can be provided, and non-easy to moving
The CD drive that the property lost CD (such as CD-ROM, DVD-ROM or other optical mediums) is read and write.In these cases, each
Driver can be connected by one or more data media interfaces with bus 18a.System storage 28a may include at least
One program product, the program product have one group of (for example, at least one) program module, these program modules are configured to hold
The function of the above-mentioned each embodiment of Fig. 1-Fig. 5 of the row present invention.
Program with one group of (at least one) program module 42a/utility 40a, can store and deposit in such as system
In reservoir 28a, such program module 42a include --- but being not limited to --- operating system, one or more application program,
It may include the reality of network environment in other program modules and program data, each of these examples or certain combination
It is existing.Program module 42a usually executes the function and/or method in above-mentioned each embodiment of Fig. 1-Fig. 5 described in the invention.
Computer equipment 12a can also be with one or more external equipment 14a (such as keyboard, sensing equipment, display
24a etc.) communication, the equipment interacted with computer equipment 12a communication can be also enabled a user to one or more, and/or
(such as network interface card is adjusted with any equipment for enabling computer equipment 12a to be communicated with one or more of the other calculating equipment
Modulator-demodulator etc.) communication.This communication can be carried out by input/output (I/O) interface 22a.Also, computer equipment
12a can also by network adapter 20a and one or more network (such as local area network (LAN), wide area network (WAN) and/or
Public network, such as internet) communication.As shown, network adapter 20a passes through its of bus 18a and computer equipment 12a
The communication of its module.It should be understood that although not shown in the drawings, other hardware and/or software can be used in conjunction with computer equipment 12a
Module, including but not limited to: microcode, device driver, redundant processor, external disk drive array, RAID system, tape
Driver and data backup storage system etc..
Processor 16a by the program that is stored in system storage 28a of operation, thereby executing various function application and
Data processing, such as realize the method for digging that user shown in above-described embodiment is intended to.
The present invention also provides a kind of computer-readable mediums, are stored thereon with computer program, which is held by processor
The method for digging that the user as shown in above-described embodiment is intended to is realized when row.
The computer-readable medium of the present embodiment may include in the system storage 28a in above-mentioned embodiment illustrated in fig. 7
RAM30a, and/or cache memory 32a, and/or storage system 34a.
With the development of science and technology, the route of transmission of computer program is no longer limited by tangible medium, it can also be directly from net
Network downloading, or obtained using other modes.Therefore, the computer-readable medium in the present embodiment not only may include tangible
Medium can also include invisible medium.
The computer-readable medium of the present embodiment can be using any combination of one or more computer-readable media.
Computer-readable medium can be computer-readable signal media or computer readable storage medium.Computer-readable storage medium
Matter for example may be-but not limited to-system, device or the device of electricity, magnetic, optical, electromagnetic, infrared ray or semiconductor, or
Any above combination of person.The more specific example (non exhaustive list) of computer readable storage medium includes: with one
Or the electrical connections of multiple conducting wires, portable computer diskette, hard disk, random access memory (RAM), read-only memory (ROM),
Erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), light
Memory device, magnetic memory device or above-mentioned any appropriate combination.In this document, computer readable storage medium can
With to be any include or the tangible medium of storage program, the program can be commanded execution system, device or device use or
Person is in connection.
Computer-readable signal media may include in a base band or as carrier wave a part propagate data-signal,
Wherein carry computer-readable program code.The data-signal of this propagation can take various forms, including --- but
It is not limited to --- electromagnetic signal, optical signal or above-mentioned any appropriate combination.Computer-readable signal media can also be
Any computer-readable medium other than computer readable storage medium, which can send, propagate or
Transmission is for by the use of instruction execution system, device or device or program in connection.
The program code for including on computer-readable medium can transmit with any suitable medium, including --- but it is unlimited
In --- wireless, electric wire, optical cable, RF etc. or above-mentioned any appropriate combination.
The computer for executing operation of the present invention can be write with one or more programming languages or combinations thereof
Program code, described program design language include object oriented program language-such as Java, Smalltalk, C++,
Further include conventional procedural programming language-such as " C " language or similar programming language.Program code can be with
It fully executes, partly execute on the user computer on the user computer, being executed as an independent software package, portion
Divide and partially executes or executed on a remote computer or server completely on the remote computer on the user computer.?
Be related in the situation of remote computer, remote computer can pass through the network of any kind --- including local area network (LAN) or
Wide area network (WAN)-be connected to subscriber computer, or, it may be connected to outer computer (such as mentioned using Internet service
It is connected for quotient by internet).
In several embodiments provided by the present invention, it should be understood that disclosed system, device and method can be with
It realizes by another way.For example, the apparatus embodiments described above are merely exemplary, for example, the unit
It divides, only a kind of logical function partition, there may be another division manner in actual implementation.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme
's.
It, can also be in addition, the functional units in various embodiments of the present invention may be integrated into one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.Above-mentioned integrated list
Member both can take the form of hardware realization, can also realize in the form of hardware adds SFU software functional unit.
The above-mentioned integrated unit being realized in the form of SFU software functional unit can store and computer-readable deposit at one
In storage media.Above-mentioned SFU software functional unit is stored in a storage medium, including some instructions are used so that a computer
It is each that equipment (can be personal computer, server or the network equipment etc.) or processor (processor) execute the present invention
The part steps of embodiment the method.And storage medium above-mentioned includes: USB flash disk, mobile hard disk, read-only memory (Read-
Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic or disk etc. it is various
It can store the medium of program code.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention
Within mind and principle, any modification, equivalent substitution, improvement and etc. done be should be included within the scope of the present invention.
Claims (16)
1. the method for digging that a kind of user is intended to, which is characterized in that the described method includes:
According to the historical behavior data of user, several intent informations of the user are excavated；
Obtain the sequence index of each intent information；
According to the sequence index of each intent information, N number of target intention information is extracted from several intent informations, with into
Row advertisement is recommended.
2. specifically being wrapped the method according to claim 1, wherein obtaining the sequence index of each intent information
It includes:
According to each corresponding historical behavior data of intent information of the user and intention exponential forecasting mould trained in advance
Type predicts the intention index of each intent information.
3. specifically being wrapped the method according to claim 1, wherein obtaining the sequence index of each intent information
It includes:
According to ad play history data gathered in advance, the corresponding advertisement broadcasting time of each intent information and advertisement are obtained
Value；
According to each corresponding advertisement broadcasting time of intent information and the advertisement value, each intent information is obtained
Corresponding value index nember.
4. specifically being wrapped the method according to claim 1, wherein obtaining the sequence index of each intent information
It includes:
Obtain the corresponding intention index of each intent information and value index nember；
According to each corresponding intention index of intent information and the value index nember, the row of each intent information is obtained
Sequence index.
5. the method according to claim 1, wherein excavating the user according to the historical behavior data of user
Several intent informations, specifically include:
Intention trained according to the historical behavior data of the user and in advance extracts model, excavates the described of the user
Several intent informations.
6. according to the method described in claim 5, it is characterized in that, according to the historical behavior data of the user and in advance
Trained intention extracts model, excavates several intent informations of the user, specifically includes:
Obtain the corresponding text feature information of the historical behavior data of the user；
Model is extracted according to the corresponding text feature information of the historical behavior data of the user and the intention, predicts institute
State several intent informations of user.
7. according to the method described in claim 6, it is characterized in that, the historical behavior data for obtaining the user are corresponding
Text feature information, specifically includes:
Obtain the title and crucial content of text of the corresponding browsing pages of the historical behavior data；
The title of the browsing pages is segmented, multiple title participles are obtained；
Multiple content keywords are extracted from the crucial content of text using content keyword extraction model trained in advance；
By the multiple title participle and the combination of the multiple content keyword, the text for forming the corresponding browsing pages is special
Reference breath.
8. the excavating gear that a kind of user is intended to, which is characterized in that described device includes:
It excavates module and excavates several intent informations of the user for the historical behavior data according to user；
Module is obtained, for obtaining the sequence index of each intent information；
Extraction module extracts N number of target from several intent informations for the sequence index according to each intent information
Intent information, to carry out advertisement recommendation.
9. device according to claim 8, which is characterized in that the acquisition module, specifically for according to the user's
Each corresponding historical behavior data of the intent information and intention Index Prediction Model trained in advance predict each intention letter
The intention index of breath.
10. device according to claim 8, which is characterized in that the acquisition module is specifically used for:
According to ad play history data gathered in advance, the corresponding advertisement broadcasting time of each intent information and advertisement are obtained
Value；
According to each corresponding advertisement broadcasting time of intent information and the advertisement value, each intent information is obtained
Corresponding value index nember.
11. device according to claim 8, which is characterized in that the acquisition module is specifically used for:
Obtain the corresponding intention index of each intent information and value index nember；
According to each corresponding intention index of intent information and the value index nember, the row of each intent information is obtained
Sequence index.
12. device according to claim 8, which is characterized in that the excavation module, specifically for according to the user's
The historical behavior data and intention trained in advance extract model, excavate several intent informations of the user.
13. device according to claim 12, which is characterized in that the excavation module is specifically used for:
Obtain the corresponding text feature information of the historical behavior data of the user；
Model is extracted according to the corresponding text feature information of the historical behavior data of the user and the intention, predicts institute
State several intent informations of user.
14. device according to claim 13, which is characterized in that the excavation module is specifically used for:
Obtain the title and crucial content of text of the corresponding browsing pages of the historical behavior data；
The title of the browsing pages is segmented, multiple title participles are obtained；
Multiple content keywords are extracted from the crucial content of text using content keyword extraction model trained in advance；
By the multiple title participle and the combination of the multiple content keyword, the text for forming the corresponding browsing pages is special
Reference breath.
15. a kind of computer equipment, which is characterized in that the equipment includes:
One or more processors；
Memory, for storing one or more programs；
When one or more of programs are executed by one or more of processors, so that one or more of processors are real
The now method as described in any in claim 1-7.
16. a kind of computer-readable medium, is stored thereon with computer program, which is characterized in that the program is executed by processor
Method of the Shi Shixian as described in any in claim 1-7.
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