CN106484777A - A kind of multimedia data processing method and device - Google Patents
A kind of multimedia data processing method and device Download PDFInfo
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Abstract
The embodiment of the invention discloses a kind of multimedia data processing method and device, wherein method include：According to operation behavior of the historic user group to the multiple multi-medium datas in default multimedia database, multi-medium data operation behavior matrix is generated；Based on sparse own coding neutral net, and the corresponding user characteristics vector of each historic user difference of corresponding hidden feature vector sum is distinguished according to each multi-medium data of multi-medium data operation behavior matrix computations；When receiving recommendation request corresponding with targeted customer, and historic user group comprising targeted customer when, obtain the multiple multi-medium datas in the personal operation behavior information of targeted customer, and corresponding hidden feature is vectorial respectively recommendation process is carried out to the multiple multi-medium datas in personal operation behavior information according to each multi-medium data in the vectorial and personal operation behavior information of the corresponding user characteristics of targeted customer.Using the present invention, it is ensured that the song that is recommended is the song liked by user, to improve recommendation effect.
Description
Technical field
The present invention relates to Internet technical field, more particularly to a kind of multimedia data processing method and device.
Background technology
With the development of Internet technology, application miscellaneous emerges in an endless stream, and for example, instant messaging application, game should
With, multi-medium data application etc..By taking multi-medium data application as an example, user can be listened to various by multi-medium data application
The song of various kinds, and the song that can also be liked by supposition user, respective songs are recommended user.At present, push away
The mode for surveying the song liked by user can include：The song that user is collected (or download) regards as what user was liked
Song, therefore, it can to speculate that the song liked of user includes the song similar with the song of being collected (or download), and then
To the song that user recommends these similar.When user does not collect (or download) song, the song for completely playing is recognized
It is set to the song liked by user, and then carries out the recommendation of similar songs.But it is to use that the song for completely playing is not represented
Family is in the song (if user is temporarily away from computer, and the music player in computer continues to play) that listens, and then cannot also represent
It is song that user is liked, if so directly the song for completely playing is regarded as the song liked by user, cannot
Ensure that recommended song is the song liked by user, cause recommendation effect not good.
Content of the invention
The embodiment of the present invention provides one kind for multimedia data processing method and device, it is ensured that the song that is recommended
It is song that user is liked, to improve recommendation effect.
A kind of multimedia data processing method is embodiments provided, including：
According to operation behavior of the historic user group to the multiple multi-medium datas in default multimedia database, generate many
Media data operation behavior matrix；
Based on sparse own coding neutral net, and according to each multimedia number of the multi-medium data operation behavior matrix computations
Corresponding user characteristics vector is distinguished according to each historic user of the corresponding hidden feature vector sum of difference；One hidden feature vector table
Levy fancy grade information of the historic user group to a multi-medium data；One user characteristics vector characterizes a history and uses
Fancy grade information of the family to the plurality of multi-medium data；
When receiving recommendation request corresponding with targeted customer, and the historic user group comprising the targeted customer when,
The multiple multi-medium datas in the personal operation behavior information of the targeted customer are obtained, and corresponding according to the targeted customer
In the vectorial and individual's operation behavior information of user characteristics, each multi-medium data distinguishes corresponding hidden feature vector to institute
The multiple multi-medium datas that states in personal operation behavior information carry out recommendation process.
Correspondingly, the embodiment of the present invention additionally provides a kind of apparatus for processing multimedia data, including：
Matrix generation module, for according to historic user group to the multiple multi-medium datas in default multimedia database
Operation behavior, generate multi-medium data operation behavior matrix；
Feature calculation module, for being based on sparse own coding neutral net, and according to the multi-medium data operation behavior
The each multi-medium data of matrix computations is distinguished each historic user of corresponding hidden feature vector sum and distinguishes corresponding user characteristics vector；
One hidden feature vector characterizes fancy grade information of the historic user group to a multi-medium data；One user characteristics
Vector characterizes fancy grade information of the historic user to the plurality of multi-medium data；
Recommending module, receives recommendation request corresponding with targeted customer for working as, and the historic user group includes institute
When targeted customer is stated, the multiple multi-medium datas in the personal operation behavior information of the targeted customer are obtained, and according to described
In the vectorial and individual's operation behavior information of the corresponding user characteristics of targeted customer, each multi-medium data difference is corresponding hidden
Recommendation process is carried out to the multiple multi-medium datas in individual's operation behavior information containing characteristic vector.
The embodiment of the present invention passes through according to historic user group to the multiple multi-medium datas in default multimedia database
Operation behavior, generate multi-medium data operation behavior matrix, and be based on sparse own coding neutral net, and according to multimedia number
The each historic user of corresponding hidden feature vector sum is distinguished according to each multi-medium data of operation behavior matrix computations distinguish corresponding use
Family characteristic vector, it can be seen that, the hobby that hidden feature vector can be with accurate characterization historic user group to a multi-medium data
Degree information, and the fancy grade letter that user characteristics vector can be with one historic user of accurate characterization to multiple multi-medium datas
Breath, so can realize accurate personalized recommendation by hidden feature vector sum user characteristics vector to targeted customer, you can
Ensure that recommended song is the song liked by targeted customer, to improve recommendation effect.
Description of the drawings
In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
Accompanying drawing to be used needed for technology description is had to be briefly described, it should be apparent that, drawings in the following description are only this
Some embodiments of invention, for those of ordinary skill in the art, on the premise of not paying creative work, acceptable
Other accompanying drawings are obtained according to these accompanying drawings.
Fig. 1 is a kind of schematic diagram of network architecture provided in an embodiment of the present invention；
Fig. 2 is a kind of schematic flow sheet of multimedia data processing method provided in an embodiment of the present invention；
Fig. 3 is the schematic flow sheet of another kind multimedia data processing method provided in an embodiment of the present invention；
Fig. 4 is a kind of structural representation of apparatus for processing multimedia data provided in an embodiment of the present invention；
Fig. 5 is a kind of structural representation of feature calculation module provided in an embodiment of the present invention；
Fig. 6 is a kind of structural representation of recommending module provided in an embodiment of the present invention；
Fig. 7 is a kind of structural representation of hidden feature signal generating unit provided in an embodiment of the present invention；
Fig. 8 is a kind of structural representation of user characteristics signal generating unit provided in an embodiment of the present invention；
Fig. 9 is the structural representation of another kind apparatus for processing multimedia data provided in an embodiment of the present invention.
Specific embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation is described, it is clear that described embodiment is only a part of embodiment of the present invention, rather than whole embodiments.It is based on
Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under the premise of creative work is not made
Embodiment, belongs to the scope of protection of the invention.
Fig. 1 is referred to, is a kind of schematic diagram of network architecture provided in an embodiment of the present invention.The network architecture can be wrapped
Include background server and multiple client, each client all can be connected with the background server by network, described after
Platform server can corresponding user be defined as historic user group respectively by each client, and the background server can also be received
(old song form of listening as certain client is to specifically include the corresponding use of the client to collect each corresponding operation behavior of client difference
The song listened to by family).Therefore, the background server can be according to historic user group in default multimedia database
Multiple multi-medium datas operation behavior, generate multi-medium data operation behavior matrix, and be based on sparse own coding nerve net
Network, and it is each to distinguish corresponding hidden feature vector sum according to each multi-medium data of the multi-medium data operation behavior matrix computations
Historic user distinguishes corresponding user characteristics vector；One hidden feature vector characterizes the historic user group to a multimedia
The fancy grade information of data；One user characteristics vector characterizes hobby of the historic user to the plurality of multi-medium data
Degree information.When certain client sends recommendation request to the background server, the background server can obtain many
Multiple multi-medium datas in the personal operation behavior information of individual targeted customer corresponding with the client, and according to the target
In the vectorial and individual's operation behavior information of the corresponding user characteristics of user, each multi-medium data distinguishes corresponding implicit spy
Levying vector carries out recommendation process to the multiple multi-medium datas in individual's operation behavior information.
Fig. 2 is referred to, is a kind of schematic flow sheet of multimedia data processing method provided in an embodiment of the present invention, described
Method can include：
S101, according to operation behavior of the historic user group to the multiple multi-medium datas in default multimedia database,
Generate multi-medium data operation behavior matrix；
Specifically, the background server based on multi-medium data application can obtain historic user group to default multimedia
The operation behavior of the multiple multi-medium datas in database.The multimedia database can be music libraries, each multimedia
Data can all be the first song in music libraries, and therefore, the operation behavior can include respectively going through in the historic user group
History user listens to behavior to each multi-medium data, and the behavior of listening to includes to listen to behavior and do not listen to behavior, and
The background server can arrange different characteristic values for different behaviors of listening to, corresponding with the operation behavior to generate
Multi-medium data operation behavior matrix.Historic user group as shown in table 1 below is to multiple many in default multimedia database
The mark sheet of the operation behavior of media data：
| Song 1 | Song 2 | Song 3 | Song 4 | … | |
| User 1 | 1 | 0 | 0 | 1 | … |
| User 2 | 1 | 1 | 1 | 0 | … |
| User 3 | 0 | 0 | 1 | 0 | … |
| User 4 | 1 | 1 | 1 | 1 | … |
| … | … | … | … | … | … |
Table 1
Wherein, the characteristic value " 1 " in table 1 represents that the user listened to this song song, and characteristic value " 0 " represents that the user does not receive
Listened this song bent, such as the characteristic value between user 1 and song 1 is " 1 ", then explanation user 1 listened to song 1；And for example user 3
It is " 0 " with the characteristic value between song 2, then explanation user 3 did not listened to song 2.Therefore, according to all characteristic values in table 1
Multi-medium data operation behavior matrix corresponding with the operation behavior, i.e., described multi-medium data operation behavior square can be generated
Element P in battle arrayuiCan represent that user u listens to the corresponding characteristic value (P of behavior to song iui=1, illustrate that user u was listened to
Song i；Pui=0, illustrate that user u does not listen to song i).
S102, based on sparse own coding neutral net and each many according to the multi-medium data operation behavior matrix computations
Media data is distinguished each historic user of corresponding hidden feature vector sum and distinguishes corresponding user characteristics vector；One hidden feature
Vector characterizes fancy grade information of the historic user group to a multi-medium data；One user characteristics vector characterizes one
Fancy grade information of the historic user to the plurality of multi-medium data；
Specifically, the background server can by the multi-medium data operation behavior Input matrix to described sparse from
The input layer of the corresponding sparse self-encoding encoder of encoding nerve network, will be in the multi-medium data operation behavior matrix each
Listen to the corresponding characteristic value of behavior to be input in the input layer；The sparse self-encoding encoder include the input layer, hidden layer,
Target component between output layer and the hidden layer and the output layer；The hidden layer includes the hiding section of predetermined number
Point；The quantity of the concealed nodes can be retouched by balancing the computational efficiency of background server and the feature of user/song
Accuracy is stated, out (can be speculated based on experience value) with speculating.Wherein, the dimension of the dimension of the input layer and the output layer
Degree is identical.
The sparse self-encoding encoder can be according to parameter (the i.e. described multi-medium data operation behavior square in the input layer
Battle array) and the default object function for training the hiding parameter of the target component and the concealed nodes, to the mesh
The hiding parameter of mark parameter and the concealed nodes carries out partial derivative training；Wherein, the object function can be：
Wherein, x is the multi-medium data operation behavior matrix；A be by W1 (1)、W2 (1)、W3 (1)、……、Wk+1 (1)Composition
Matrix, W1 (1)、W2 (1)、W3 (1)、……、Wk+1 (1)Hiding parameter in respectively K+1 concealed nodes is (as W1 (1)Represent first
The hiding parameter of individual concealed nodes), and last concealed nodes in K+1 concealed nodes are intercept item, the intercept item
Hiding parameter is 1, can reconstruct the output layer by retaining the intercept item.S be by bN1、bN2、bN3、……、bNKComposition
Matrix, bN1、bN2、bN3、……、bNKIt is the hidden layer to the target component between the output layer, N is the dimension of input layer,
In the embodiment of the present invention, concealed nodes number K+1 is specified, sparse to hidden layer can require to relax, therefore will be near for 1 norm of s
It is seemingly 2 norms.Wherein, although as some users do not carry out listening to behavior to some songs, not representing these use
Family does not like these songs, even if likewise, some users listened some songs, can not directly represent that these users like this
A little songs, so the embodiment of the present invention passes through to increased a user interest factor item C in the object function, to improve
S matrix and user in the hidden layer for being trained out is to the incidence relation between the fancy grade of song；The user
Interest factor item C includes interest value c of each historic user respectively to each multi-medium data in the multimedia databaseui
(cuiRepresent interest value of the user u to song i)；One interest value is the behaviour based on a historic user to a multi-medium data
Make behavior type, number of operations and complete operation rate calculated.Wherein, to each in the user interest factor item C
Individual interest value cuiComputing formula can be：cui=1+ α log (1+ ε rui)；Wherein, directly collect as user u/download song i
When, rui=1；When user u is only (not collect/down operation) when listening to song i, rui=[min (nui, 5)/5] *
fui, wherein, nuiRefer to that user u listens to the number of times of song i, fuiIt is complete audience rating (i.e. described complete operation rate), fui=complete
Listen to the number/all tins of song numbers (i.e. described historic user group) of song i；When user u did not listen to song i, cui=
0.
The sparse self-encoding encoder can repeat to be alternately performed step S1 and step during partial derivative training is carried out
S2；Step S1 is：Fixing A, to s derivation, obtains optimal solution with least square method；Step S2 is：Fixing s, to A derivation, with most
Little square law obtains optimal solution.
Further, when the parameter phase in the parameter in the output layer of the sparse self-encoding encoder with the input layer
Closely (matching degree i.e. between the parameter of the parameter of output layer and the input layer reaches preset matching degree threshold value) when, determine described
The hiding parameter of target component and the concealed nodes meets the condition of convergence, and now described sparse self-encoding encoder stops execution step
S1 and step S2, and the hiding parameter group for meeting each concealed nodes of the condition of convergence is synthesized hidden feature matrix, i.e. institute
State the matrix A that hidden feature matrix is after training.In order to ensure the operating efficiency of background server, therefore, described sparse self-editing
The dimension of the hidden layer in code device is the dimension for being less than the input layer, so the hiding parameter in the hidden layer can also
The compressed format of the parameter being considered as in the input layer, i.e., described hidden feature matrix are the multi-medium data operation behavior square
The corresponding condensation matrix of battle array；Wherein, the hidden feature matrix includes that each multi-medium data distinguishes corresponding hidden feature vector,
For example, it is possible to the first row feature of the hidden feature matrix is defined as the hidden feature vector of song 1, by the implicit spy
The secondary series feature for levying matrix is defined as hidden feature vector of song 2 etc., and wherein, the hidden feature vector of song 1 is permissible
It is characterized as fancy grade information of the historic user group to song 1.
Further, as the quantity of the sparse self-encoding encoder is one, it is possible to from the sparse self-encoding encoder
It is corresponding that each historic user difference in the historic user group is extracted in the corresponding parameter matrix s of target component after middle training
User characteristics vector.The background server calculates each historic user in the historic user group and distinguishes corresponding user characteristics
After each multi-medium data distinguishes corresponding hidden feature vector in the vectorial and multimedia database, each use can be stored
Family characteristic vector and each hidden feature vector, carry out user in order to follow-up according to user characteristics vector sum hidden feature vector
Personalized song recommendations.
S103, when receiving recommendation request corresponding with targeted customer, and the historic user group is used comprising the target
During family, the multiple multi-medium datas in the personal operation behavior information of the targeted customer are obtained, and according to the targeted customer
In the vectorial and individual's operation behavior information of corresponding user characteristics each multi-medium data distinguish corresponding hidden feature to
Measuring carries out recommendation process to the multiple multi-medium datas in individual's operation behavior information；
Specifically, when receiving recommendation request corresponding with targeted customer, and the historic user group includes the target
During user, whether can detect in the corresponding individual's operation behavior information of targeted customer comprising the multi-medium data that has collected；If
It is detected as comprising the multi-medium data that has collected, the then multi-medium data that has been collected described in acquisition (recommending source) corresponding first
Similar multi-medium data, and using the described first similar multi-medium data as the targeted customer recommending data；If being detected as
Not comprising the multi-medium data that has collected, then whether determine whether in individual's operation behavior information comprising complete operation
Multi-medium data (song that completely listens to).When comprising complete in the corresponding individual's operation behavior information of the targeted customer
During the multi-medium data of whole operation, by vectorial for corresponding for the targeted customer user characteristics multimedia with the complete operation
The corresponding hidden feature vector of data carries out point multiplication operation, obtains individualized feature value, and when the individualized feature value is more than
During default eigenvalue threshold, multi-medium data (recommending source) the corresponding second similar multimedia of complete operation described in acquisition
Data, and using the described second similar multi-medium data as the targeted customer recommending data.
Optionally, the multimedia number when the corresponding individual's operation behavior information of the targeted customer not comprising complete operation
According to when, obtain multiple candidate's multi-medium datas, and by corresponding for mark user user characteristics vector respectively with many matchmakers of each candidate
The corresponding hidden feature vector of volume data carries out point multiplication operation, obtains each candidate's multi-medium data and distinguishes corresponding personalization
Characteristic value；The plurality of candidate's multi-medium data is ranked up according to each personalization characteristic value order from big to small, and root
According to ranking results using candidate's multi-medium data of default recommended amount as the targeted customer recommending data.For example, if using
The personal operation behavior information of family A is not comprising the song that has completely listened to, and candidate song includes song A, song B and song
C, then hidden feature vector that can be by the user characteristics of user A vector with song A carry out dot product, obtain individualized feature value a；
The user characteristics vector of user A and the hidden feature vector of song B are carried out dot product, obtains individualized feature value b；By user A
User characteristics vector carry out dot product with the hidden feature vector of song C, obtain individualized feature value c；To individualized feature value
After being ranked up, b is obtained>c>A, if it is 2 to preset recommended amount, can be by corresponding for individualized feature value b song B and individual character
Change corresponding song C of characteristic value c and recommend user A.
Optionally, when the individualized feature value is calculated, dominant character value can also be referred to, i.e. user u is to song i
Individualized feature value=user u to the hidden feature of the user characteristics of the dominant character value+user u of song i vector and song i to
The dot product result of amount.The display characteristic value can be calculated according to the matching degree between the label of song and the label of user
Arrive；The label of song can include the corresponding style of song type of the song, languages type, rhythm type etc.；The label of user can
With liked including the user style of song type, languages type, rhythm type etc..By increasing the dominant character value, can
So that the individualized feature value can more accurately describe fancy grade of the user u to song i.
Optionally, when receiving recommendation request corresponding with targeted customer, and the historic user group does not include the mesh
During mark user, illustrate that the targeted customer did not also listen to song on the multi-medium data platform provided by background server,
Then can be according to the vector value of the corresponding hidden feature vector of each candidate's multi-medium data order from big to small to described
Multiple candidate's multi-medium datas are ranked up, and according to ranking results using candidate's multi-medium data of default recommended amount as institute
State the recommending data of targeted customer.
Optionally, the background server periodically can calculate and update each historic user according to new operation behavior
The corresponding user characteristics vector of difference, and the corresponding hidden feature vector of each multi-medium data difference so that target is being used
When family is recommended, it is ensured that the accessed hidden feature vector sum user characteristics vector related to the targeted customer
It is the interests change of laminating user all the time, you can to ensure the accuracy of calculated individualized feature value.Wherein, the mesh
, per a song is listened, the recommendation source of all renewable once described targeted customer, in order to follow-up to target use for mark user
When song is recommended at family, can be with the direct access multi-medium data similar to the recommendation source of recent renewal, by similar multimedia
Data recommendation to user, to improve recommendation efficiency；And can show that in the user interface of client " song B is tried according to you
Song A that listens is recommended ", wherein, song A is recommendation source, and song B is the multi-medium data similar to source is recommended.
The embodiment of the present invention passes through according to historic user group to the multiple multi-medium datas in default multimedia database
Operation behavior, generate multi-medium data operation behavior matrix, and be based on sparse own coding neutral net, and according to multimedia number
The each historic user of corresponding hidden feature vector sum is distinguished according to each multi-medium data of operation behavior matrix computations distinguish corresponding use
Family characteristic vector, it can be seen that, the hobby that hidden feature vector can be with accurate characterization historic user group to a multi-medium data
Degree information, and the fancy grade letter that user characteristics vector can be with one historic user of accurate characterization to multiple multi-medium datas
Breath, so can realize accurate personalized recommendation by hidden feature vector sum user characteristics vector to targeted customer, you can
Ensure that recommended song is the song liked by targeted customer, to improve recommendation effect.
Refer to Fig. 3 again, be the schematic flow sheet of another kind multimedia data processing method provided in an embodiment of the present invention,
Methods described can include：
S201, according to operation behavior of the historic user group to the multiple multi-medium datas in default multimedia database,
Generate multi-medium data operation behavior matrix；
Specifically, the specific implementation of S201 step may refer to the S101 that above-mentioned Fig. 2 is corresponded in embodiment, here not
Repeated again.
S202 is corresponding dilute to the sparse own coding neutral net by the multi-medium data operation behavior Input matrix
The input layer of thin self-encoding encoder；The sparse self-encoding encoder includes the input layer, hidden layer, output layer and the hidden layer
With the target component between the output layer；The hidden layer includes the concealed nodes of predetermined number；
Specifically, the background server can by the multi-medium data operation behavior Input matrix to described sparse from
The input layer of the corresponding sparse self-encoding encoder of encoding nerve network, will be in the multi-medium data operation behavior matrix each
Listen to the corresponding characteristic value of behavior to be input in the input layer；The sparse self-encoding encoder include the input layer, hidden layer,
Target component between output layer and the hidden layer and the output layer；The hidden layer includes the hiding section of predetermined number
Point；The quantity of the concealed nodes can be retouched by balancing the computational efficiency of background server and the feature of user/song
Accuracy is stated, out (can be speculated based on experience value) with speculating.Wherein, the dimension of the dimension of the input layer and the output layer
Degree is identical.
S203, the sparse self-encoding encoder is according to the parameter in the input layer and default for training the target
The object function of the hiding parameter of parameter and the concealed nodes, to the target component and the hiding parameter of the concealed nodes
Carry out partial derivative training；
The sparse self-encoding encoder can be according to parameter (the i.e. described multi-medium data operation behavior square in the input layer
Battle array) and the default object function for training the hiding parameter of the target component and the concealed nodes, to the mesh
The hiding parameter of mark parameter and the concealed nodes carries out partial derivative training；Wherein, the object function can be：
Wherein, x is the multi-medium data operation behavior matrix；A be by W1 (1)、W2 (1)、W3 (1)、……、Wk+1 (1)Composition
Matrix, W1 (1)、W2 (1)、W3 (1)、……、Wk+1 (1)Hiding parameter in respectively K+1 concealed nodes is (as W1 (1)Represent first
The hiding parameter of individual concealed nodes), and last concealed nodes in K+1 concealed nodes are intercept item, the intercept item
Hiding parameter is 1, can reconstruct the output layer by retaining the intercept item.S be by bN1、bN2、bN3、……、bNKComposition
Matrix, bN1、bN2、bN3、……、bNKIt is the hidden layer to the target component between the output layer, N is the dimension of input layer,
In the embodiment of the present invention, concealed nodes number K+1 is specified, sparse to hidden layer can require to relax, therefore will be near for 1 norm of s
It is seemingly 2 norms.Wherein, although as some users do not carry out listening to behavior to some songs, not representing these use
Family does not like these songs, even if likewise, some users listened some songs, can not directly represent that these users like this
A little songs, so the embodiment of the present invention passes through to increased a user interest factor item C in the object function, to improve
S matrix and user in the hidden layer for being trained out is to the incidence relation between the fancy grade of song；The user
Interest factor item C includes interest value c of each historic user respectively to each multi-medium data in the multimedia databaseui
(cuiRepresent interest value of the user u to song i)；One interest value is the behaviour based on a historic user to a multi-medium data
Make behavior type, number of operations and complete operation rate calculated.Wherein, to each in the user interest factor item C
Individual interest value cuiComputing formula can be：cui=1+ α log (1+ ε rui)；Wherein, directly collect as user u/download song i
When, rui=1；When user u is only (not collect/down operation) when listening to song i, rui=[min (nui, 5)/5] *
fui, wherein, nuiRefer to that user u listens to the number of times of song i, fuiIt is complete audience rating (i.e. described complete operation rate), fui=complete
Listen to the number/all tins of song numbers (i.e. described historic user group) of song i；When user u did not listen to song i, cui=
0.
The sparse self-encoding encoder can repeat to be alternately performed step S1 and step during partial derivative training is carried out
S2；Step S1 is：Fixing A, to s derivation, obtains optimal solution with least square method；Step S2 is：Fixing s, to A derivation, with most
Little square law obtains optimal solution.
S204, the parameter similar in the parameter in the output layer of the sparse self-encoding encoder with the input layer
When, determine that the target component and the hiding parameter of the concealed nodes meet the condition of convergence, and the condition of convergence will be met
Each concealed nodes hiding parameter determination be target input source；
Specifically, the background server can also preset at least two sparse self-encoding encoders, when first sparse self-editing
During parameter similar in parameter and the input layer in the output layer of code device, determine the target component and described hide
The hiding parameter of node meets the sparse self-encoding encoder of the condition of convergence, i.e., first and completes training, at this point it is possible to sparse by first
The hiding parameter determination for meeting each concealed nodes of the condition of convergence in self-encoding encoder is target input source.
S205, according to the sparse self-encoding encoder of predetermined number, the target input source is input to next sparse self-editing
The input layer of code device, the next sparse self-encoding encoder train the target input source corresponding hidden according to the object function
Parameter is hidden, and using the hiding parameter after training in the next sparse self-encoding encoder as target input source, repeats this
Step, until last sparse self-encoding encoder trains hiding parameter；
Specifically, the background server can be further according to the sparse self-encoding encoder of predetermined number, by the target
Input source is input to the input layer of next sparse self-encoding encoder, and the next sparse self-encoding encoder is according to the object function
Train the corresponding hiding parameter of the target input source, and by the hiding parameter after training in the next sparse self-encoding encoder
As target input source, this step is repeated, until last sparse self-encoding encoder trains hiding parameter.Wherein,
The hiding parameter trained by one sparse self-encoding encoder is properly termed as single order characteristic parameter, and second sparse self-encoding encoder is instructed
The hiding parameter that practises is properly termed as second order characteristic parameter, by that analogy, the hiding ginseng trained by n-th sparse self-encoding encoder
Number is properly termed as n rank characteristic parameter, and n value is bigger, then n rank characteristic parameter more accurate can describe user to multi-medium data
Fancy grade.Wherein, the object function in first sparse self-encoding encoder can include the user interest factor item C,
And the object function in other the sparse self-encoding encoders in addition to first sparse self-encoding encoder can not include described
User interest factor item C.Wherein, the condition of convergence of each sparse self-encoding encoder is identical, i.e., the condition of convergence be all for institute
State the parameter similar in parameter in output layer and the input layer.
S206, the hiding parameter combination that last sparse self-encoding encoder described is trained are hidden feature matrix；
S207, when the quantity of the sparse self-encoding encoder is at least two, obtains corresponding of each historic user difference
People's operation behavior information, respectively by corresponding for the multi-medium data for having been operated in each individual's operation behavior information hidden feature vector
Enter row vector average computation, and using each average vector for calculating as each historic user distinguish corresponding user characteristics to
Amount；
Specifically, when the quantity of the sparse self-encoding encoder is at least two, each historic user can be obtained right respectively
The personal operation behavior information that answers, respectively by corresponding for the multi-medium data for having been operated in each individual's operation behavior information implicit spy
Levy vector and enter row vector average computation, and each average vector for calculating is distinguished corresponding user as each historic user
Characteristic vector.For example, the corresponding individual's song that can be listened to including user A of operation behavior information of historic user A, by with
The song that family A was listened to can include song 1, song 2, song 3, then the corresponding user characteristics vector of historic user A can be
The average vector of the hidden feature vector of the hidden feature vector of song 1, the hidden feature vector of song 2 and song 3.
S208, when receiving recommendation request corresponding with targeted customer, and the historic user group is used comprising the target
During family, the multiple multi-medium datas in the personal operation behavior information of the targeted customer are obtained, and according to the targeted customer
In the vectorial and individual's operation behavior information of corresponding user characteristics each multi-medium data distinguish corresponding hidden feature to
Measuring carries out recommendation process to the multiple multi-medium datas in individual's operation behavior information；
Wherein, the specific implementation of S208 step may refer to the S101 that above-mentioned Fig. 2 is corresponded in embodiment, here no longer
Repeated.
The embodiment of the present invention passes through according to historic user group to the multiple multi-medium datas in default multimedia database
Operation behavior, generate multi-medium data operation behavior matrix, and be based on sparse own coding neutral net, and according to multimedia number
The each historic user of corresponding hidden feature vector sum is distinguished according to each multi-medium data of operation behavior matrix computations distinguish corresponding use
Family characteristic vector, it can be seen that, the hobby that hidden feature vector can be with accurate characterization historic user group to a multi-medium data
Degree information, and the fancy grade letter that user characteristics vector can be with one historic user of accurate characterization to multiple multi-medium datas
Breath, so can realize accurate personalized recommendation by hidden feature vector sum user characteristics vector to targeted customer, you can
Ensure that recommended song is the song liked by targeted customer, to improve recommendation effect.
Fig. 4 is referred to, is a kind of structural representation of apparatus for processing multimedia data provided in an embodiment of the present invention.Described
Apparatus for processing multimedia data 1 is can apply in background server, and the apparatus for processing multimedia data 1 can include：Square
Battle array generation module 10, feature calculation module 20, recommending module 30；
The matrix generation module 10, for according to historic user group to the multiple many matchmakers in default multimedia database
The operation behavior of volume data, generates multi-medium data operation behavior matrix；
Specifically, the matrix generation module 10 can obtain historic user group to many in default multimedia database
The operation behavior of individual multi-medium data.The multimedia database can be music libraries, and each multi-medium data can be all
A first song in music libraries, therefore, the operation behavior can be including each historic user in the historic user group to each
Multi-medium data listen to behavior, the behavior of listening to includes to listen to behavior and does not listen to behavior, the matrix generation module
10 can arrange different characteristic values for different behaviors of listening to, to generate multi-medium data behaviour corresponding with the operation behavior
Make behavioural matrix.Again by taking the table 1 that above-mentioned Fig. 2 is corresponded in embodiment as an example, the characteristic value " 1 " in table 1 represents that the user listened to
This song is bent, and characteristic value " 0 " represents that the user did not listened to this song song, and the such as characteristic value between user 1 and song 1 is " 1 ",
Then explanation user 1 listened to song 1；And for example the characteristic value between user 3 and song 2 is " 0 ", then explanation user 3 did not listened to
Song 2.Therefore, the matrix generation module 10 can be generated and the operation behavior according to all characteristic values in the table 1
Corresponding multi-medium data operation behavior matrix, i.e., the element P in described multi-medium data operation behavior matrixuiUse can be represented
Family u listens to the corresponding characteristic value (P of behavior to song iui=1, illustrate that user u listened to song i；Pui=0, user u is described
Song i) is not listened to.
The feature calculation module 20, for being based on sparse own coding neutral net, and grasps according to the multi-medium data
Make behavioural matrix and calculate the corresponding user spy of each historic user difference of the corresponding hidden feature vector sum of each multi-medium data difference
Levy vector；One hidden feature vector characterizes fancy grade information of the historic user group to a multi-medium data；One
User characteristics vector characterizes fancy grade information of the historic user to the plurality of multi-medium data；
Specifically, please also refer to Fig. 5, it is the structural representation of the feature calculation module 20, the feature calculation mould
Block 20 can include：Input block 201, training unit 202, hidden feature signal generating unit 203, user characteristics signal generating unit 204；
The input block 201, for by the multi-medium data operation behavior Input matrix to the sparse own coding
The input layer of the corresponding sparse self-encoding encoder of neutral net；The sparse self-encoding encoder includes the input layer, hidden layer, output
Target component between layer and the hidden layer and the output layer；The hidden layer includes the concealed nodes of predetermined number；
The training unit 202, for controlling the sparse self-encoding encoder according to the parameter in the input layer and pre-
If the object function for training the hiding parameter of the target component and the concealed nodes, to the target component and institute
The hiding parameter for stating concealed nodes carries out partial derivative training；
The hidden feature signal generating unit 203, for when the sparse self-encoding encoder the output layer in parameter with
During parameter similar in the input layer, determine that the target component and the hiding parameter of the concealed nodes meet convergence bar
Part, and the hiding parameter group for meeting each concealed nodes of the condition of convergence is synthesized hidden feature matrix；The hidden feature
Matrix includes each multimedia number for the corresponding condensation matrix of the multi-medium data operation behavior matrix, the hidden feature matrix
According to the corresponding hidden feature vector of difference；
The user characteristics signal generating unit 204, based on according to the target component after training or the hidden feature matrix
Calculate each historic user in the historic user group and distinguish corresponding user characteristics vector；
Wherein, when the quantity of the sparse self-encoding encoder is one, the hiding parameter that is calculated is single order feature
Parameter, i.e., described hidden feature matrix include the single order characteristic parameter.The input block 201, the training unit 202,
The hidden feature signal generating unit 203, the specific implementation of the user characteristics signal generating unit 204 may refer to above-mentioned Fig. 2
S102 in corresponding embodiment, is not discussed here.
The recommending module 30, receives recommendation request corresponding with targeted customer, and the historic user group for working as
During comprising the targeted customer, the multiple multi-medium datas in the personal operation behavior information of the targeted customer, and root is obtained
According to the corresponding user characteristics of the targeted customer vectorial and described individual operation behavior information in each multi-medium data right respectively
The hidden feature vector that answers carries out recommendation process to the multiple multi-medium datas in individual's operation behavior information；
Specifically, please also refer to Fig. 6, it is the structural representation of the recommending module 30, the recommending module 30 is permissible
Including：Detector unit 301, similar recommendation unit 302, acquisition judging unit 303, point multiplication operation unit 304, sort recommendations unit
305；
The detector unit 301, receives recommendation request corresponding with targeted customer, and the historic user group for working as
During comprising the targeted customer, whether detect in the corresponding individual's operation behavior information of targeted customer comprising the multimedia that has collected
Data；
The similar recommendation unit 302, if be detected as comprising the multimedia number that has collected for the detector unit 301
According to, then obtain described in the corresponding first similar multi-medium data of the multi-medium data collected, and by described first similar many matchmakers
Volume data is used as the recommending data of the targeted customer；
The acquisition judging unit 303, if be detected as not comprising the multimedia number that has collected for the detector unit 301
According to then determining whether in individual's operation behavior information the whether multi-medium data comprising complete operation；
The point multiplication operation unit 304, for as many matchmakers comprising complete operation in individual's operation behavior information
During volume data, will be vectorial for corresponding for targeted customer user characteristics corresponding hidden with the multi-medium data of the complete operation
Point multiplication operation is carried out containing characteristic vector, obtain individualized feature value；
The similar recommendation unit 302, is additionally operable to, when the individualized feature value is more than eigenvalue threshold is preset, obtain
The corresponding second similar multi-medium data of the multi-medium data of the complete operation, and by the described second similar multi-medium data
Recommending data as the targeted customer；
The point multiplication operation unit 304, is additionally operable to when individual's operation behavior information is not comprising many of complete operation
During media data, obtain multiple candidate's multi-medium datas, by corresponding for mark user user characteristics vector respectively with each candidate
The corresponding hidden feature vector of multi-medium data carries out point multiplication operation, obtains corresponding of each candidate's multi-medium data difference
Property characteristic value；
The sort recommendations unit 305, for entering to the plurality of candidate's multi-medium data according to each personalization characteristic value
Row sequence, and according to ranking results using candidate's multi-medium data of default recommended amount as the targeted customer recommendation number
According to；
Wherein, the detector unit 301, the similar recommendation unit 302, the acquisition judging unit 303, the dot product
Arithmetic element 304, the specific implementation of the sort recommendations unit 305 may refer to above-mentioned Fig. 2 and correspond in embodiment
S103, is not discussed here.
Optionally, the recommending module 30 can also refer to dominant character value, i.e., when the individualized feature value is calculated
User characteristics vectorial and song of the user u to the individualized feature value=user u of song i to the dominant character value+user u of song i
The dot product result of the hidden feature vector of bent i.The display characteristic value can be according between the label of song and the label of user
Matching degree be calculated；The label of song can include the corresponding style of song type of the song, languages type, rhythm type etc.
Deng；Style of song type that the label of user can be liked including the user, languages type, rhythm type etc..By increasing institute
Dominant character value is stated, the individualized feature value can be enable more accurately to describe fancy grade of the user u to song i.
Optionally, when receiving recommendation request corresponding with targeted customer, and the historic user group does not include the mesh
During mark user, illustrate that the targeted customer did not also listen to song on the multi-medium data platform provided by background server,
Then the recommending module 30 can according to the vector value of the corresponding hidden feature vector of each candidate's multi-medium data from big to
Little order is ranked up to the plurality of candidate's multi-medium data, and will be many for the candidate of default recommended amount according to ranking results
Media data is used as the recommending data of the targeted customer.
Optionally, the feature calculation module 20 periodically can calculate and update each history according to new operation behavior
User distinguishes corresponding user characteristics vector, and each multi-medium data distinguishes corresponding hidden feature vector so as to mesh
When mark user is recommended, it is ensured that the accessed hidden feature vector sum user characteristics related to the targeted customer
Vector is to fit the interests change of user all the time, you can to ensure individualized feature value that the recommending module 30 is calculated
Accuracy.Wherein, the targeted customer is per listening a song, all renewable once described targeted customer's of the recommending module 30
Recommendation source, in order to follow-up when song is recommended to the targeted customer, the recommending module 30 can with direct access with nearest
The multi-medium data for recommending source similar of renewal, similar multi-medium data is recommended user, to improve recommendation efficiency；And
" song B is recommended according to song A of your audition " can be shown in the user interface of client, wherein, song A is recommends
Source, song B are the multi-medium data similar to source is recommended.
Further, please also refer to Fig. 7, it is a kind of hidden feature signal generating unit 203 provided in an embodiment of the present invention
Structural representation, the hidden feature signal generating unit 203 include：Determination subelement 2031, deep learning subelement 2032, combination
Subelement 2033；
The determination subelement 2031, for when the sparse self-encoding encoder the output layer in parameter defeated with described
When entering the parameter similar in layer, determine that the target component and the hiding parameter of the concealed nodes meet the condition of convergence, and will
The hiding parameter determination for meeting each concealed nodes of the condition of convergence is target input source；
The deep learning subelement 2032, for the sparse self-encoding encoder according to predetermined number, the target is input into
Source is input to the input layer of next sparse self-encoding encoder, and the next sparse self-encoding encoder is trained according to the object function
The corresponding hiding parameter of the target input source, and using the hiding parameter after training in the next sparse self-encoding encoder as
Target input source, repeats this step, until last sparse self-encoding encoder trains hiding parameter；
The combination subelement 2033, for the hiding parameter combination for training last sparse self-encoding encoder described
For hidden feature matrix；
Wherein, the hiding parameter trained by first sparse self-encoding encoder is properly termed as single order characteristic parameter, second
The hiding parameter trained by sparse self-encoding encoder is properly termed as second order characteristic parameter, by that analogy, n-th sparse self-encoding encoder
The hiding parameter for being trained is properly termed as n rank characteristic parameter, and n value is bigger, then n rank characteristic parameter can more accurate description use
Fancy grade of the family to multi-medium data.The determination subelement 2031, the deep learning subelement 2032, described group of zygote
The specific implementation of unit 2033 may refer to the S204-S206 that above-mentioned Fig. 3 is corresponded in embodiment, no longer be gone to live in the household of one's in-laws on getting married here
State.
Further, then please also refer to Fig. 8, it is a kind of user characteristics signal generating unit 204 provided in an embodiment of the present invention
Structural representation, the user characteristics signal generating unit 204 includes：Extract subelement 2041, average computation subelement 2042；
Described extraction subelement 2041, for when the sparse self-encoding encoder quantity be one when, from described sparse from
Each historic user difference in the historic user group is extracted in the corresponding parameter matrix of target component after training in encoder
Corresponding user characteristics vector；
The average computation subelement 2042, for when the quantity of the sparse self-encoding encoder is at least two, obtaining
The corresponding individual's operation behavior information of each historic user difference, the multimedia that will have been operated in each individual's operation behavior information respectively
The corresponding hidden feature vector of data enters row vector average computation, and each average vector for calculating is used as each history
Corresponding user characteristics vector is distinguished at family；
Specifically, when the quantity of the sparse self-encoding encoder is at least two, the average computation subelement 2042 can
To obtain the corresponding individual's operation behavior information of each historic user difference, will operate in each individual's operation behavior information respectively
The corresponding hidden feature vector of multi-medium data enters row vector average computation, and using each average vector for calculating as each
Historic user distinguishes corresponding user characteristics vector.For example, the corresponding individual's operation behavior information of historic user A can include to use
The song listened to by family A, the song that was listened to by user A can include song 1, song 2, song 3, then historic user A pair
The user characteristics vector that answers can be the implicit of the hidden feature of song 1 vector, the hidden feature vector of song 2 and song 3
The average vector of characteristic vector.
The embodiment of the present invention passes through according to historic user group to the multiple multi-medium datas in default multimedia database
Operation behavior, generate multi-medium data operation behavior matrix, and be based on sparse own coding neutral net, and according to multimedia number
The each historic user of corresponding hidden feature vector sum is distinguished according to each multi-medium data of operation behavior matrix computations distinguish corresponding use
Family characteristic vector, it can be seen that, the hobby that hidden feature vector can be with accurate characterization historic user group to a multi-medium data
Degree information, and the fancy grade letter that user characteristics vector can be with one historic user of accurate characterization to multiple multi-medium datas
Breath, so can realize accurate personalized recommendation by hidden feature vector sum user characteristics vector to targeted customer, you can
Ensure that recommended song is the song liked by targeted customer, to improve recommendation effect.
Fig. 9 is referred to, is the structural representation of another kind apparatus for processing multimedia data provided in an embodiment of the present invention.Such as
Shown in Fig. 9, the apparatus for processing multimedia data 1000 is can apply in background server, the multimedia-data procession dress
Putting 1000 can include：At least one processor 1001, such as CPU, at least one network interface 1004, user interface 1003,
Memory 1005, at least one communication bus 1002.Wherein, the connection that communication bus 1002 is used for realizing between these components is led to
Letter.Wherein, user interface 1003 can include display screen (Display), keyboard (Keyboard), and optional user interface 1003 is also
Wireline interface, the wave point of standard can be included.Network interface 1004 can optionally include the wireline interface of standard, wireless
Interface (as WI-FI interface).Memory 1005 can be high-speed RAM memory, or non-labile memory (non-
Volatile memory), for example, at least one magnetic disc store.Memory 1005 optionally can also be that at least one is located at
Storage device away from aforementioned processor 1001.As shown in figure 9, as can in a kind of memory 1005 of computer-readable storage medium
To include operating system, network communication module, Subscriber Interface Module SIM and equipment control application program.
In the apparatus for processing multimedia data 1000 shown in Fig. 9, network interface 1004 is mainly used in connecting client, with
Recommend multi-medium data to client；And user interface 1003 is mainly used in the interface of input is provided the user, user is obtained defeated
The data for going out；And processor 1001 can be used for calling the equipment control application program stored in memory 1005, to realize
According to operation behavior of the historic user group to the multiple multi-medium datas in default multimedia database, generate many
Media data operation behavior matrix；
Based on sparse own coding neutral net, and according to each multimedia number of the multi-medium data operation behavior matrix computations
Corresponding user characteristics vector is distinguished according to each historic user of the corresponding hidden feature vector sum of difference；One hidden feature vector table
Levy fancy grade information of the historic user group to a multi-medium data；One user characteristics vector characterizes a history and uses
Fancy grade information of the family to the plurality of multi-medium data；
When receiving recommendation request corresponding with targeted customer, and the historic user group comprising the targeted customer when,
The multiple multi-medium datas in the personal operation behavior information of the targeted customer are obtained, and corresponding according to the targeted customer
In the vectorial and individual's operation behavior information of user characteristics, each multi-medium data distinguishes corresponding hidden feature vector to institute
The multiple multi-medium datas that states in personal operation behavior information carry out recommendation process.
In one embodiment, the processor 1001 is based on sparse own coding neutral net in execution, and according to described
The each multi-medium data of multi-medium data operation behavior matrix computations distinguishes each historic user difference of corresponding hidden feature vector sum
During corresponding user characteristics vector, following steps are specifically executed：
By the multi-medium data operation behavior Input matrix to the sparse own coding neutral net corresponding sparse from
The input layer of encoder；The sparse self-encoding encoder includes the input layer, hidden layer, output layer and the hidden layer and institute
State the target component between output layer；The hidden layer includes the concealed nodes of predetermined number；
Control the sparse self-encoding encoder according to the parameter in the input layer and default for training the target
The object function of the hiding parameter of parameter and the concealed nodes, to the target component and the hiding parameter of the concealed nodes
Carry out partial derivative training；
When the parameter similar in the parameter in the output layer of the sparse self-encoding encoder with the input layer, determine
The hiding parameter of the target component and the concealed nodes meets the condition of convergence, and hides each of the condition of convergence is met
The hiding parameter group synthesis hidden feature matrix of node；The hidden feature matrix is the multi-medium data operation behavior matrix
Corresponding condensation matrix, the hidden feature matrix include that each multi-medium data distinguishes corresponding hidden feature vector；
According to each historic user in the target component after training or historic user group described in the hidden feature matrix computations
The corresponding user characteristics vector of difference.
In one embodiment, the processor 1001 is being executed when in the output layer of the sparse self-encoding encoder
During parameter similar in parameter and the input layer, determine that the hiding parameter of the target component and the concealed nodes meets and receive
Condition is held back, and when the hiding parameter group for meeting each concealed nodes of the condition of convergence is synthesized hidden feature matrix, is specifically held
Row following steps：
When the parameter similar in the parameter in the output layer of the sparse self-encoding encoder with the input layer, determine
The hiding parameter of the target component and the concealed nodes meets the condition of convergence, and hides each of the condition of convergence is met
The hiding parameter determination of node is target input source；
According to the sparse self-encoding encoder of predetermined number, the target input source is input to next sparse self-encoding encoder
Input layer, the next sparse self-encoding encoder train the corresponding hiding ginseng of the target input source according to the object function
Number, and using the hiding parameter after training in the next sparse self-encoding encoder as target input source, this step is repeated,
Until last sparse self-encoding encoder trains hiding parameter；
The hiding parameter combination that last sparse self-encoding encoder described is trained is hidden feature matrix.
In one embodiment, the object function includes default user interest factor item, the user interest factor
Item includes each historic user interest value respectively to each multi-medium data in the multimedia database；
One interest value be based on a historic user to the operation behavior type of a multi-medium data, number of operations with
And complete operation rate is calculated.
In one embodiment, the processor 1001 is being executed according to the target component after training or the hidden feature
When each historic user distinguishes corresponding user characteristics vector in historic user group described in matrix computations, following steps are specifically executed：
When the quantity of the sparse self-encoding encoder is one, the target component after training from the sparse self-encoding encoder
Each historic user in the historic user group is extracted in corresponding parameter matrix distinguishes corresponding user characteristics vector；
When the quantity of the sparse self-encoding encoder is at least two, the corresponding personal operation of each historic user difference is obtained
Behavioural information, respectively by the corresponding hidden feature vector of the multi-medium data that operated in each individual's operation behavior information carry out to
Amount average computation, and each average vector for calculating is distinguished corresponding user characteristics vector as each historic user.
In one embodiment, the processor 1001 ought receive recommendation request corresponding with targeted customer in execution,
And the historic user group comprising the targeted customer when, obtain multiple in the personal operation behavior information of the targeted customer
Multi-medium data, and according to the corresponding user characteristics of the targeted customer vectorial and described individual operation behavior information in each many
Corresponding hidden feature vector is pushed away media data to the multiple multi-medium datas in individual's operation behavior information respectively
When process is recommended, following steps are specifically executed：
When receiving recommendation request corresponding with targeted customer, and the historic user group comprising the targeted customer when,
Whether comprising the multi-medium data that has collected in the corresponding individual's operation behavior information of detection targeted customer；
If being detected as including the multi-medium data that has collected, the multi-medium data corresponding first that has been collected described in acquisition
Similar multi-medium data, and using the described first similar multi-medium data as the targeted customer recommending data；
If being detected as not comprising the multi-medium data collected, determine whether in individual's operation behavior information be
The no multi-medium data comprising complete operation；
When the multi-medium data comprising complete operation in individual's operation behavior information, by the targeted customer couple
The vectorial hidden feature vector corresponding with the multi-medium data of the complete operation of the user characteristics answered carries out point multiplication operation, obtains
To individualized feature value；
When the individualized feature value is more than eigenvalue threshold is preset, the multi-medium data of complete operation described in acquisition
Corresponding second similar multi-medium data, and using the described second similar multi-medium data as the targeted customer recommendation number
According to.
In one embodiment, the processor 1001 also executes following steps：
When individual's operation behavior information does not include the multi-medium data of complete operation, many matchmakers of multiple candidates are obtained
Volume data, by corresponding for mark user user characteristics vector, hidden feature corresponding with each candidate's multi-medium data is vectorial respectively
Point multiplication operation is carried out, is obtained each candidate's multi-medium data and distinguishes corresponding individualized feature value；
The plurality of candidate's multi-medium data is ranked up according to each personalization characteristic value, and will be pre- according to ranking results
If candidate's multi-medium data of recommended amount is used as the recommending data of the targeted customer.
The embodiment of the present invention passes through according to historic user group to the multiple multi-medium datas in default multimedia database
Operation behavior, generate multi-medium data operation behavior matrix, and be based on sparse own coding neutral net, and according to multimedia number
The each historic user of corresponding hidden feature vector sum is distinguished according to each multi-medium data of operation behavior matrix computations distinguish corresponding use
Family characteristic vector, it can be seen that, the hobby that hidden feature vector can be with accurate characterization historic user group to a multi-medium data
Degree information, and the fancy grade letter that user characteristics vector can be with one historic user of accurate characterization to multiple multi-medium datas
Breath, so can realize accurate personalized recommendation by hidden feature vector sum user characteristics vector to targeted customer, you can
Ensure that recommended song is the song liked by targeted customer, to improve recommendation effect.
One of ordinary skill in the art will appreciate that realizing all or part of flow process in above-described embodiment method, it is permissible
Instruct related hardware to complete by computer program, described program can be stored in a computer read/write memory medium
In, the program is upon execution, it may include as the flow process of the embodiment of above-mentioned each method.Wherein, described storage medium can be magnetic
Dish, CD, read-only memory (Read-Only Memory, ROM) or random access memory (Random Access
Memory, RAM) etc..
Above disclosed only present pre-ferred embodiments, can not limit the right model of the present invention certainly with this
Enclose, the equivalent variations that is therefore made according to the claims in the present invention, still belong to the scope covered by the present invention.
Claims (14)
1. a kind of multimedia data processing method, it is characterised in that include：
According to operation behavior of the historic user group to the multiple multi-medium datas in default multimedia database, multimedia is generated
Data manipulation behavioural matrix；
Based on sparse own coding neutral net, and divided according to each multi-medium data of the multi-medium data operation behavior matrix computations
The each historic user of not corresponding hidden feature vector sum distinguishes corresponding user characteristics vector；One hidden feature vector characterizes institute
State fancy grade information of the historic user group to a multi-medium data；One user characteristics vector characterizes a historic user pair
The fancy grade information of the plurality of multi-medium data；
When receiving recommendation request corresponding with targeted customer, and the historic user group comprising the targeted customer when, obtain
Multiple multi-medium datas in the personal operation behavior information of the targeted customer, and according to the corresponding user of the targeted customer
In characteristic vector and individual's operation behavior information, each multi-medium data distinguishes corresponding hidden feature vector to described
Multiple multi-medium datas in people's operation behavior information carry out recommendation process.
2. the method for claim 1, it is characterised in that described based on sparse own coding neutral net, and according to described
The each multi-medium data of multi-medium data operation behavior matrix computations distinguishes each historic user difference of corresponding hidden feature vector sum
Corresponding user characteristics vector, including：
By the multi-medium data operation behavior Input matrix to the corresponding sparse own coding of the sparse own coding neutral net
The input layer of device；The sparse self-encoding encoder includes that the input layer, hidden layer, output layer and the hidden layer are defeated with described
The target component gone out between layer；The hidden layer includes the concealed nodes of predetermined number；
The sparse self-encoding encoder is according to the parameter in the input layer and default for training the target component and institute
The object function of the hiding parameter of concealed nodes is stated, the hiding parameter to the target component and the concealed nodes carries out local derviation
Number training；
When the parameter similar in the parameter in the output layer of the sparse self-encoding encoder with the input layer, determine described
The hiding parameter of target component and the concealed nodes meets the condition of convergence, and will meet each concealed nodes of the condition of convergence
Hiding parameter group synthesis hidden feature matrix；The hidden feature matrix is corresponded to for the multi-medium data operation behavior matrix
Condensation matrix, the hidden feature matrix includes that each multi-medium data distinguishes corresponding hidden feature vector；
According to each historic user difference in the target component after training or historic user group described in the hidden feature matrix computations
Corresponding user characteristics vector.
3. method as claimed in claim 2, it is characterised in that described when in the output layer of the sparse self-encoding encoder
During parameter similar in parameter and the input layer, determine that the hiding parameter of the target component and the concealed nodes meets and receive
Condition is held back, and the hiding parameter group for meeting each concealed nodes of the condition of convergence is synthesized hidden feature matrix, including：
When the parameter similar in the parameter in the output layer of the sparse self-encoding encoder with the input layer, determine described
The hiding parameter of target component and the concealed nodes meets the condition of convergence, and will meet each concealed nodes of the condition of convergence
Hiding parameter determination be target input source；
According to the sparse self-encoding encoder of predetermined number, the target input source is input to the input of next sparse self-encoding encoder
Layer, the next sparse self-encoding encoder train the corresponding hiding parameter of the target input source according to the object function, and
Using the hiding parameter after training in the next sparse self-encoding encoder as target input source, this step is repeated, until
Last sparse self-encoding encoder trains hiding parameter；
The hiding parameter combination that last sparse self-encoding encoder described is trained is hidden feature matrix.
4. method as claimed in claim 2, it is characterised in that the object function includes default user interest factor item,
The user interest factor item include each historic user respectively in the multimedia database each multi-medium data emerging
Interest value；
One interest value be based on a historic user to the operation behavior type of a multi-medium data, number of operations and complete
Whole operating rate is calculated.
5. method as claimed in claim 3, it is characterised in that the target component according to after training or the hidden feature
In historic user group described in matrix computations, each historic user distinguishes corresponding user characteristics vector, including：
When the quantity of the sparse self-encoding encoder is one, the target component after training from the sparse self-encoding encoder is corresponded to
Parameter matrix in extract each historic user in the historic user group and distinguish corresponding user characteristics vector；
When the quantity of the sparse self-encoding encoder is at least two, the corresponding individual's operation behavior of each historic user difference is obtained
Corresponding for the multi-medium data for having been operated in each individual's operation behavior information hidden feature vector is entered row vector and puts down by information respectively
All calculate, and each average vector for calculating is distinguished corresponding user characteristics vector as each historic user.
6. the method for claim 1, it is characterised in that described when receiving recommendation request corresponding with targeted customer,
And the historic user group comprising the targeted customer when, obtain multiple in the personal operation behavior information of the targeted customer
Multi-medium data, and according to the corresponding user characteristics of the targeted customer vectorial and described individual operation behavior information in each many
Corresponding hidden feature vector is pushed away media data to the multiple multi-medium datas in individual's operation behavior information respectively
Process is recommended, including：
When receiving recommendation request corresponding with targeted customer, and the historic user group comprising the targeted customer when, detection
Whether comprising the multi-medium data that has collected in the corresponding individual's operation behavior information of targeted customer；
If being detected as comprising the multi-medium data collected, obtain described in the multi-medium data collected corresponding first similar
Multi-medium data, and using the described first similar multi-medium data as the targeted customer recommending data；
If being detected as not comprising the multi-medium data that has collected, determine whether whether wrap in individual's operation behavior information
Multi-medium data containing complete operation；
When the multi-medium data comprising complete operation in individual's operation behavior information, will be corresponding for the targeted customer
The vectorial hidden feature vector corresponding with the multi-medium data of the complete operation of user characteristics carries out point multiplication operation, obtains individual
Property characteristic value；
When the individualized feature value is more than eigenvalue threshold is preset, described in acquisition, the multi-medium data of complete operation is corresponded to
The second similar multi-medium data, and using the described second similar multi-medium data as the targeted customer recommending data.
7. method as claimed in claim 6, it is characterised in that also include：
When individual's operation behavior information does not include the multi-medium data of complete operation, multiple candidate's multimedia numbers are obtained
According to corresponding with each candidate's multi-medium data respectively for the mark user corresponding user characteristics vector hidden feature vector is carried out
Point multiplication operation, obtains each candidate's multi-medium data and distinguishes corresponding individualized feature value；
The plurality of candidate's multi-medium data is ranked up according to each personalization characteristic value, and is pushed away default according to ranking results
Recommend the recommending data of candidate's multi-medium data as the targeted customer of quantity.
8. a kind of apparatus for processing multimedia data, it is characterised in that include：
Matrix generation module, for the behaviour according to historic user group to the multiple multi-medium datas in default multimedia database
Make behavior, generate multi-medium data operation behavior matrix；
Feature calculation module, for being based on sparse own coding neutral net, and according to the multi-medium data operation behavior matrix
Calculate each multi-medium data and distinguish the corresponding user characteristics vector of each historic user difference of corresponding hidden feature vector sum；One
Hidden feature vector characterizes fancy grade information of the historic user group to a multi-medium data；One user characteristics vector
Characterize fancy grade information of the historic user to the plurality of multi-medium data；
Recommending module, receives recommendation request corresponding with targeted customer for working as, and the historic user group includes the mesh
During mark user, the multiple multi-medium datas in the personal operation behavior information of the targeted customer are obtained, and according to the target
In the vectorial and individual's operation behavior information of the corresponding user characteristics of user, each multi-medium data distinguishes corresponding implicit spy
Levying vector carries out recommendation process to the multiple multi-medium datas in individual's operation behavior information.
9. device as claimed in claim 8, it is characterised in that the feature calculation module includes：
Input block, for corresponding to the multi-medium data operation behavior Input matrix to the sparse own coding neutral net
Sparse self-encoding encoder input layer；The sparse self-encoding encoder includes the input layer, hidden layer, output layer and described hidden
Hide the target component between layer and the output layer；The hidden layer includes the concealed nodes of predetermined number；
Training unit, for controlling the sparse self-encoding encoder according to the parameter in the input layer and default for training
The object function of the hiding parameter of the target component and the concealed nodes, to the target component and the concealed nodes
Hiding parameter carries out partial derivative training；
Hidden feature signal generating unit, for when in the parameter in the output layer of the sparse self-encoding encoder with the input layer
Parameter similar when, determine that the target component and the hiding parameter of the concealed nodes meet the condition of convergence, and institute will be met
State the hiding parameter group synthesis hidden feature matrix of each concealed nodes of the condition of convergence；The hidden feature matrix is many matchmakers
The corresponding condensation matrix of volume data operation behavior matrix, the hidden feature matrix include that each multi-medium data difference is corresponding hidden
Containing characteristic vector；
User characteristics signal generating unit, uses for history according to the target component after training or described in the hidden feature matrix computations
In the group of family, each historic user distinguishes corresponding user characteristics vector.
10. device as claimed in claim 9, it is characterised in that the hidden feature signal generating unit includes：
Determination subelement, for when the parameter in the parameter in the output layer of the sparse self-encoding encoder with the input layer
When close, determine that the target component and the hiding parameter of the concealed nodes meet the condition of convergence, and the convergence will be met
The hiding parameter determination of each concealed nodes of condition is target input source；
Deep learning subelement, for the sparse self-encoding encoder according to predetermined number, the target input source is input to next
The input layer of individual sparse self-encoding encoder, the next sparse self-encoding encoder train the target input according to the object function
The corresponding hiding parameter in source, and using the hiding parameter after training in the next sparse self-encoding encoder as target input source,
This step is repeated, until last sparse self-encoding encoder trains hiding parameter；
Combination subelement, the hiding parameter combination for training last sparse self-encoding encoder described are hidden feature square
Battle array.
11. devices as claimed in claim 9, it is characterised in that the object function includes default user interest factor item,
The user interest factor item include each historic user respectively in the multimedia database each multi-medium data emerging
Interest value；
One interest value be based on a historic user to the operation behavior type of a multi-medium data, number of operations and complete
Whole operating rate is calculated.
12. devices as claimed in claim 10, it is characterised in that the user characteristics signal generating unit includes：
Subelement is extracted, for when the quantity of the sparse self-encoding encoder is one, training from the sparse self-encoding encoder
Extracting each historic user in the historic user group in the corresponding parameter matrix of rear target component, to distinguish corresponding user special
Levy vector；
Average computation subelement, for when the quantity of the sparse self-encoding encoder is at least two, obtaining each historic user and dividing
Not corresponding individual's operation behavior information, respectively will be corresponding hidden for the multi-medium data for having been operated in each individual's operation behavior information
Enter row vector average computation containing characteristic vector, and each average vector for calculating is distinguished as each historic user corresponding
User characteristics vector.
13. devices as claimed in claim 8, it is characterised in that the recommending module includes：
Detector unit, receives recommendation request corresponding with targeted customer for working as, and the historic user group includes the mesh
During mark user, whether detect in the corresponding individual's operation behavior information of targeted customer comprising the multi-medium data that has collected；
Similar recommendation unit, if being detected as including the multi-medium data that has collected for the detector unit, described in acquisition
The corresponding first similar multi-medium data of the multi-medium data of collection, and using the described first similar multi-medium data as the mesh
The recommending data of mark user；
Judging unit is obtained, if being detected as, not comprising the multi-medium data that has collected, sentencing further for the detector unit
Whether the multi-medium data of complete operation is included in disconnected individual's operation behavior information；
Point multiplication operation unit, for when described individual operation behavior information in the multi-medium data comprising complete operation when, will
The vectorial hidden feature vector corresponding with the multi-medium data of the complete operation of the corresponding user characteristics of the targeted customer
Point multiplication operation is carried out, obtains individualized feature value；
The similar recommendation unit, is additionally operable to when the individualized feature value is more than eigenvalue threshold is preset, described in acquisition
The corresponding second similar multi-medium data of the multi-medium data of complete operation, and using the described second similar multi-medium data as institute
State the recommending data of targeted customer.
14. devices as claimed in claim 13, it is characterised in that the recommending module also includes：
The point multiplication operation unit, is additionally operable to the multi-medium data not comprising complete operation when individual's operation behavior information
When, obtain multiple candidate's multi-medium datas, by corresponding for mark user user characteristics vector respectively with each candidate's multimedia number
Point multiplication operation is carried out according to corresponding hidden feature vector, obtain each candidate's multi-medium data and distinguish corresponding individualized feature
Value；
Sort recommendations unit, for being ranked up to the plurality of candidate's multi-medium data according to each personalization characteristic value, and root
According to ranking results using candidate's multi-medium data of default recommended amount as the targeted customer recommending data.
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