CN109819304A - Barrage methods of exhibiting and device - Google Patents
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Abstract
This disclosure relates to barrage methods of exhibiting and device.This method comprises: determining the corresponding user vector of the target user according to the user data of target user；Determine the corresponding barrage vector of candidate's barrage；Determine the similarity of user vector barrage vector corresponding with candidate's barrage；According to the similarity of user vector barrage vector corresponding with candidate's barrage, the recommendation barrage for being directed to the target user is determined from the candidate barrage；In the case where barrage function is opened, the recommendation barrage is shown.The disclosure determines the corresponding user vector of target user according to the user data of target user, the recommendation barrage for being directed to target user is determined according to the similarity of user vector and barrage vector, and it is shown in the case where barrage discloses unlatching and recommends barrage, thus, it is possible to only show the interested barrage of target user, the case where barrage all over the screen influences viewing is avoided the occurrence of.
Description
Technical field
This disclosure relates to barrage technical field more particularly to a kind of barrage methods of exhibiting and device.
Background technique
Currently, barrage technology is widely used by each video website, show that barrage can be given during playing video
User brings interactive experience.However, in the video (such as popular TV play or the variety show of hot topic etc.) of some hot topics
In, since the quantity of barrage is larger, the phenomenon that being easy to appear barrage all over the screen.In a large amount of barrage, it is likely that there are many use
The uninterested barrage in family, causes the decline of viewing experience.
Summary of the invention
In view of this, the present disclosure proposes a kind of barrage methods of exhibiting and devices.
According to the one side of the disclosure, a kind of barrage methods of exhibiting is provided, comprising:
According to the user data of target user, the corresponding user vector of the target user is determined；
Determine the corresponding barrage vector of candidate's barrage；
Determine the similarity of user vector barrage vector corresponding with candidate's barrage；
According to the similarity of user vector barrage vector corresponding with candidate's barrage, from the candidate barrage
Determine the recommendation barrage for being directed to the target user；
In the case where barrage function is opened, the recommendation barrage is shown.
In one possible implementation, the user data includes in user behavior data and static user data
It is one or two kinds of.
In one possible implementation, according to the user data of target user, determine that the target user is corresponding
User vector, comprising:
According to the user data of the target user, the feature vector of the target user is determined；
According to the feature vector of the target user, the corresponding user vector of the target user is determined.
In one possible implementation, according to the feature vector of the target user, the target user couple is determined
The user vector answered, comprising:
It will be in the feature vector input prediction model of the target user, wherein the prediction model is described for predicting
The corresponding next user behavior data of target user；
The output vector of the layer second from the bottom of the prediction model is determined as the corresponding user vector of the target user.
In one possible implementation, the method also includes:
Training dataset is obtained, the training dataset includes in the user data and the multiple user of multiple users
The corresponding next user behavior data of each user；
According to the user data of the multiple user, the feature vector of each user in the multiple user is determined respectively；
It, will be described more respectively using the feature vector of user each in the multiple user as the input of the prediction model
Output of the corresponding next user behavior data of each user as the prediction model in a user, the training prediction mould
Type.
In one possible implementation, the recommendation barrage is shown, comprising:
According to the similarity for recommending barrage and the user vector, the exhibition method for recommending barrage is determined.
According to another aspect of the present disclosure, a kind of barrage displaying device is provided, comprising:
User vector determining module determines the corresponding use of the target user for the user data according to target user
Family vector；
Barrage vector determining module, for determining the corresponding barrage vector of candidate barrage；
Similarity determining module, for determining the similar of user vector barrage vector corresponding with candidate's barrage
Degree；
Recommend barrage determining module, for the phase according to user vector barrage vector corresponding with candidate's barrage
Like degree, the recommendation barrage for being directed to the target user is determined from the candidate barrage；
Recommend barrage display module, for showing the recommendation barrage in the case where barrage function is opened.
In one possible implementation, the user data includes in user behavior data and static user data
It is one or two kinds of.
In one possible implementation, the user vector determining module includes:
Feature vector determines submodule, for the user data according to the target user, determines the target user's
Feature vector；
User vector determines submodule, for the feature vector according to the target user, determines the target user couple
The user vector answered.
In one possible implementation, the user vector determines that submodule is used for:
It will be in the feature vector input prediction model of the target user, wherein the prediction model is described for predicting
The corresponding next user behavior data of target user；
The output vector of the layer second from the bottom of the prediction model is determined as the corresponding user vector of the target user.
In one possible implementation, described device further include:
Training dataset obtains module, and for obtaining training dataset, the training dataset includes the use of multiple users
The corresponding next user behavior data of each user in user data and the multiple user；
Feature vector determining module determines the multiple user for the user data according to the multiple user respectively
In each user feature vector；
Prediction model training module, for respectively using the feature vector of user each in the multiple user as described pre-
The input for surveying model, using the corresponding next user behavior data of user each in the multiple user as the prediction model
Output, the training prediction model.
In one possible implementation, the recommendation barrage display module is used for:
According to the similarity for recommending barrage and the user vector, the exhibition method for recommending barrage is determined.
According to another aspect of the present disclosure, a kind of barrage displaying device is provided, comprising: processor；It is handled for storage
The memory of device executable instruction；Wherein, the processor is configured to executing the above method.
According to another aspect of the present disclosure, a kind of non-volatile computer readable storage medium storing program for executing is provided, is stored thereon with
Computer program instructions, wherein the computer program instructions realize the above method when being executed by processor.
The barrage methods of exhibiting and device of various aspects of the present disclosure determine target user couple according to the user data of target user
The user vector answered determines the recommendation barrage for being directed to target user according to the similarity of user vector and barrage vector, and in bullet
It is shown in the case where the open unlatching of curtain and recommends barrage, thus, it is possible to only show the interested barrage of target user, avoided the occurrence of full
Shield the case where barrage influences viewing.
According to below with reference to the accompanying drawings to detailed description of illustrative embodiments, the other feature and aspect of the disclosure will become
It is clear.
Detailed description of the invention
Comprising in the description and constituting the attached drawing of part of specification and specification together illustrates the disclosure
Exemplary embodiment, feature and aspect, and for explaining the principles of this disclosure.
Fig. 1 shows the flow chart of the barrage methods of exhibiting according to one embodiment of the disclosure.
Fig. 2 shows the illustrative flow charts according to the barrage methods of exhibiting step S11 of one embodiment of the disclosure.
Fig. 3 shows an illustrative flow chart of the barrage methods of exhibiting step S112 according to one embodiment of the disclosure.
Fig. 4 shows an illustrative flow chart of the barrage methods of exhibiting according to one embodiment of the disclosure.
Fig. 5 shows the block diagram that device is shown according to the barrage of one embodiment of the disclosure.
Fig. 6 shows the illustrative block diagram that device is shown according to the barrage of one embodiment of the disclosure.
Fig. 7 is a kind of block diagram of device 800 shown for barrage shown according to an exemplary embodiment.
Specific embodiment
Various exemplary embodiments, feature and the aspect of the disclosure are described in detail below with reference to attached drawing.It is identical in attached drawing
Appended drawing reference indicate element functionally identical or similar.Although the various aspects of embodiment are shown in the attached drawings, remove
It non-specifically points out, it is not necessary to attached drawing drawn to scale.
Dedicated word " exemplary " means " being used as example, embodiment or illustrative " herein.Here as " exemplary "
Illustrated any embodiment should not necessarily be construed as preferred or advantageous over other embodiments.
In addition, giving numerous details in specific embodiment below to better illustrate the disclosure.
It will be appreciated by those skilled in the art that without certain details, the disclosure equally be can be implemented.In some instances, for
Method, means, element and circuit well known to those skilled in the art are not described in detail, in order to highlight the purport of the disclosure.
Fig. 1 shows the flow chart of the barrage methods of exhibiting according to one embodiment of the disclosure.This method can be applied to terminal
In equipment.Wherein, terminal device can for mobile phone, tablet computer, VR (Virtual Reality, virtual reality) head it is aobvious, VR
Mirror, AR (Augmented Reality, augmented reality) head are aobvious, AR glasses, MR (Mixed Reality, mixed display) head are aobvious,
MR glasses, HUD (Head Up Display, head-up display) or smart television etc., are not limited thereto.As shown in Figure 1,
The method comprising the steps of S11 to step S15.
In step s 11, according to the user data of target user, the corresponding user vector of target user is determined.
In the present embodiment, the corresponding user vector of target user can be used for characterizing the feature of target user.
In one possible implementation, the user data of target user may include the user behavior number of target user
According to.The user behavior data of target user may include the data, the data for searching for video, comment view that target user watches video
The data of frequency, the data for delivering barrage, point step on one in data of the data of video, the data of collection video and sharing video frequency etc.
Kind is a variety of.
In alternatively possible implementation, the user data of target user may include the static subscriber of target user
Data.The static user data of target user may include one in age, gender, occupation and affiliated area of target user etc.
Item is multinomial.
In alternatively possible implementation, the user data of target user may include the user behavior of target user
Data and static user data.
As an example of the implementation, can according to one or more user behavior datas of target user, with
And one or more static user datas of target user, determine the corresponding user vector of target user.For example, can be according to mesh
It marks user to watch the data of video, comment on the data of video and gender, age and the occupation of target user, determines target user
Corresponding user vector.
In one possible implementation, the corresponding user vector of target user can be obtained by prediction model.Example
Such as, which can use three-layer neural network.
In step s 12, the corresponding barrage vector of candidate barrage is determined.
In one possible implementation, it determines the corresponding barrage vector of candidate barrage, may include: to candidate barrage
Word segmentation processing is carried out, the corresponding word segmentation result of candidate barrage is obtained；Determine the corresponding vector of each word in the word segmentation result；Root
According to the corresponding vector of each word in the word segmentation result, the corresponding vector of candidate's barrage is determined.
In this implementation, candidate barrage can be carried out at participle using any participle technique in the related technology
Reason, is not limited thereto.
As an example of the implementation, can be determined using word2vec the word in word segmentation result it is corresponding to
Amount.
As an example of the implementation, the corresponding vector of candidate barrage S can be determined using formula 6,
Wherein, Q indicates the word number in the corresponding word segmentation result of candidate barrage S, wiIndicate the corresponding participle knot of candidate barrage
I-th of word in fruit, f (wi) indicate wiCorresponding vector.
In step s 13, the similarity of user vector barrage vector corresponding with candidate barrage is determined.
It in the present embodiment, can be with table if the similarity of the corresponding barrage vector of candidate barrage and user vector is higher
Bright target user is higher to the interested possibility of candidate's barrage；If the corresponding barrage vector of candidate barrage and user vector
Similarity is lower, then may indicate that target user is lower to the interested possibility of candidate's barrage.
As an example of the present embodiment, user vector U barrage corresponding with candidate barrage can be determined using formula 7
The similarity s [U, f (S)] of vector f (S),
S [U, f (S)]=cos [U, f (S)] formula 7.
In step S14, according to the similarity of user vector barrage vector corresponding with candidate barrage, from candidate barrage
Middle determination is directed to the recommendation barrage of target user.
In one possible implementation, according to the similar of user vector barrage vector corresponding with candidate barrage
Degree determines the recommendation barrage for being directed to target user from candidate barrage, may include: to be greater than the similarity with the user vector
The candidate barrage of second threshold is determined as the recommendation barrage for target user.
In alternatively possible implementation, according to the similar of user vector barrage vector corresponding with candidate barrage
Degree determines the recommendation barrage for being directed to target user from candidate barrage, may include: that the similarity with the user vector is maximum
I candidate barrage be determined as the recommendation barrage for target user, wherein I is positive integer.
In step S15, in the case where barrage function is opened, shows and recommend barrage.
In the present embodiment, target user, which can choose, opens barrage function or closing barrage function.In barrage function
In the case where unlatching, recommendation barrage can be shown；In the case where barrage function is closed, barrage can not be shown.
The present embodiment determines the corresponding user vector of target user according to the user data of target user, according to user vector
The recommendation barrage for being directed to target user is determined with the similarity of barrage vector, and is shown and recommended in the case where barrage discloses unlatching
Barrage avoids the occurrence of the case where barrage all over the screen influences viewing thus, it is possible to only show the interested barrage of target user.
In one possible implementation, before step S11, this method can also include: to obtain target user
One or both of user behavior data and static user data.
Fig. 2 shows the illustrative flow charts according to the barrage methods of exhibiting step S11 of one embodiment of the disclosure.Such as figure
Shown in 2, step S11 may include step S111 and step S112.
In step S111, according to the user data of target user, the feature vector of target user is determined.
In one possible implementation, the data that video can be watched according to target user, determine target user's
Watch feature vector；The data that video can be commented on according to target user, determine the comment feature vector of target user；It can root
According to the static user data of target user, the static nature vector of target user is determined.In this example, the feature of target user
Vector may include the viewing feature vector, comment feature vector and static nature vector of target user.
As an example of the implementation, the data of video each of video can be watched target user
Title is segmented, and determines the corresponding vector of each word of title, and determine video according to the corresponding vector of each word of title
Corresponding vector.For example, can determine video v using formula 1jCorresponding vector g (vj),
Wherein, N indicates video vjTitle in total word number, wiIndicate video vjTitle in i-th of word, f (wi) table
Show wiCorresponding vector, f (wi) it can be T dimensional vector, T is positive integer.
According to the corresponding vector of video each in the data of target user's viewing video, the viewing of target user can be determined
Feature vector.For example, the viewing feature vector h (V) of target user can be determined using formula 2,
Wherein, M indicates the video sum that target user watches in the data of video, g (vj) indicate that target user watches view
The corresponding vector of j-th of video in the data of frequency.
As an example of the implementation, target user can be commented on each in the data of video comment on into
Row participle determines the corresponding vector of each word in comment, and determines comment pair according to the corresponding vector of each word in comment
The vector answered.For example, can determine comment c using formula 3lCorresponding vector g (cl)；
Wherein, K indicates total word number in comment, wkIndicate comment ckIn the word, f (wk) indicate wkCorresponding vector.
Each item in the data of video is commented on according to target user and comments on corresponding vector, can determine the comment of target user
Feature vector.For example, can determine the comment feature vector h (C) of target user using formula 4；
Wherein, L indicates that target user comments on the general comment number in the data of video, clIndicate that target user comments on video
The corresponding vector of the l articles comment in data.
In step S112, according to the feature vector of target user, the corresponding user vector of target user is determined.
In one possible implementation, can according to the viewing feature vector of target user, comment feature vector and
Static nature vector determines the corresponding user vector of target user.
It should be noted that although with the viewing feature vector of target user, comment feature vector and static nature vector
The feature vector for describing target user is as above, it is understood by one of ordinary skill in the art that the disclosure answer it is without being limited thereto.This field
Technical staff can select more or fewer feature vectors to be used for really according to practical application scene demand and/or personal preference
The corresponding user vector of the user that sets the goal.
Fig. 3 shows an illustrative flow chart of the barrage methods of exhibiting step S112 according to one embodiment of the disclosure.Such as
Shown in Fig. 3, step S112 may include step S1121 and step S1122.
It, will be in the feature vector input prediction model of target user in step S1121, wherein prediction model is for pre-
Survey the corresponding next user behavior data of target user.
Wherein, the corresponding next user behavior data of target user can be the number of the next viewing video of target user
According to or it is next comment video data etc., be not limited thereto.For example, the corresponding next user behavior number of target user
According to the corresponding vector of video that can be the next viewing of target user, or can be corresponding for lower comment of target user
Vector.
It, can be by p (x in formula 5 as an example of the present embodimentr) maximum xrIt is determined as the next of target user
The corresponding vector of a user behavior data,
Wherein, u can be determined according to the feature vector of target user.For example, can by the viewing feature of target user to
The mean value of the static nature vector of h (V), the comment feature vector h (C) of target user and target user is measured as u, alternatively, can
With by the static nature of comment feature vector h (C) and target user of the viewing feature vector h (V) of target user, target user
The sum of vector is used as u, is not limited thereto.Indicate xdThe corresponding vector of d-th of user behavior data of target user, D are indicated
The sum of the user behavior data of target user.Wherein, xdThe data or comment video of video can be watched for target user
The corresponding vector such as data.
In step S1122, the output vector of the layer second from the bottom of prediction model is determined as the corresponding use of target user
Family vector.
It, can be by the viewing feature vector of target user, comment feature vector and quiet as an example of the present embodiment
In state feature vector input prediction model, and the output vector of the layer second from the bottom of prediction model can be determined as target user
Corresponding user vector.
Fig. 4 shows an illustrative flow chart of the barrage methods of exhibiting according to one embodiment of the disclosure.As shown in figure 4,
This method may include step S401 to step S410.
In step S401, training dataset is obtained, which includes the user data of multiple users and more
The corresponding next user behavior data of each user in a user.
Wherein, it may include the user behavior data of each user and quiet that training data, which concentrates the user data of each user,
State user data.
In step S402, according to the user data of multiple users, the feature of each user in multiple users is determined respectively
Vector.
Wherein it is determined that the method for the feature vector of each user and the feature for above determining target user in multiple users
The method of vector is similar, and details are not described herein.
It, will respectively using the feature vector of user each in multiple users as the input of prediction model in step S403
Output of the corresponding next user behavior data of each user as prediction model in multiple users, training prediction model.
In step s 404, according to the user data of target user, the feature vector of target user is determined.
Wherein, the description to step S111 is seen above to step S404.
It, will be in the feature vector input prediction model of target user in step S405, wherein prediction model is for predicting
The corresponding next user behavior data of target user.
Wherein, the description to step S1121 is seen above to step S405.
In step S406, the output vector of the layer second from the bottom of prediction model is determined as the corresponding user of target user
Vector.
Wherein, the description to step S1122 is seen above to step S406.
In step S 407, the corresponding barrage vector of candidate barrage is determined.
Wherein, the description to step S12 is seen above to step S407.
In step S408, the similarity of user vector barrage vector corresponding with candidate barrage is determined.
Wherein, the description to step S13 is seen above to step S408.
In step S409, according to the similarity of user vector barrage vector corresponding with candidate barrage, from candidate bullet
The recommendation barrage for being directed to target user is determined in curtain.
Wherein, the description to step S14 is seen above to step S409.
In step S410, in the case where barrage function is opened, shows and recommend barrage.
Wherein, the description to step S15 is seen above to step S410.
In one possible implementation, it shows and recommends barrage, may include: according to recommendation barrage and user vector
Similarity determines the exhibition method for recommending barrage.
As an example of the implementation, recommend the font size of barrage can be with the recommendation barrage and user vector
Similarity be positively correlated.Recommend the similarity of barrage and user vector higher, then recommends the font of barrage bigger；Recommend barrage with
The similarity of user vector is lower, then recommends the font of barrage smaller.
As another example of the implementation, if the similarity of barrage and user vector is recommended to be greater than first threshold,
The recommendation barrage can be then shown with the first font size；If the similarity of barrage and user vector is recommended to be less than or equal to first
Threshold value can show the recommendation barrage then with the second font size.Wherein, the first font size is greater than the second font size.
It should be noted that being determined although being described with above example according to the similarity of recommendation barrage and the user vector
Recommend barrage exhibition method it is as above, it is understood by one of ordinary skill in the art that the disclosure answer it is without being limited thereto.Those skilled in the art
Member can be similar to the user vector according to recommendation barrage according to practical application scene demand and/or personal preference flexible setting
Degree determines the concrete mode for recommending the exhibition method of barrage.For example, can the exhibition in a manner of color outstanding or luminescence display etc.
Show and the higher recommendation barrage of the similarity of user vector.
Fig. 5 shows the block diagram that device is shown according to the barrage of one embodiment of the disclosure.As shown in figure 5, the device includes: use
Family vector determining module 51 determines the corresponding user vector of the target user for the user data according to target user；Bullet
Curtain vector determining module 52, for determining the corresponding barrage vector of candidate barrage；Similarity determining module 53, described in determining
The similarity of user vector barrage vector corresponding with candidate's barrage；Recommend barrage determining module 54, for according to
The similarity of user vector barrage vector corresponding with candidate's barrage, determines from the candidate barrage and is directed to the target
The recommendation barrage of user；Recommend barrage display module 55, for showing the recommendation bullet in the case where barrage function is opened
Curtain.
In one possible implementation, the user data includes in user behavior data and static user data
It is one or two kinds of.
Fig. 6 shows the illustrative block diagram that device is shown according to the barrage of one embodiment of the disclosure.It is as shown in Figure 6:
In one possible implementation, the user vector determining module 51 includes: that feature vector determines submodule
511, for the user data according to the target user, determine the feature vector of the target user；User vector determines son
Module 512 determines the corresponding user vector of the target user for the feature vector according to the target user.
In one possible implementation, the user vector determines that submodule 512 is used for: by the target user's
In feature vector input prediction model, wherein the prediction model is for predicting the corresponding next user of the target user
Behavioral data；By the output vector of the layer second from the bottom of the prediction model be determined as the corresponding user of the target user to
Amount.
In one possible implementation, described device further include: training dataset obtains module 56, for obtaining instruction
Practice data set, the training dataset include in the user data and the multiple user of multiple users each user it is corresponding
Next user behavior data；Feature vector determining module 57 determines respectively for the user data according to the multiple user
The feature vector of each user in the multiple user；Prediction model training module 58, being used for respectively will be in the multiple user
Input of the feature vector of each user as the prediction model, user each in the multiple user is corresponding next
Output of the user behavior data as the prediction model, the training prediction model.
In one possible implementation, the recommendation barrage display module 55 is used for: according to the recommendation barrage with
The similarity of the user vector determines the exhibition method for recommending barrage.
The present embodiment determines the corresponding user vector of target user according to the user data of target user, according to user vector
The recommendation barrage for being directed to target user is determined with the similarity of barrage vector, and is shown and recommended in the case where barrage discloses unlatching
Barrage avoids the occurrence of the case where barrage all over the screen influences viewing thus, it is possible to only show the interested barrage of target user.
Fig. 7 is a kind of block diagram of device 800 shown for barrage shown according to an exemplary embodiment.For example, dress
Setting 800 can be mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical treatment
Equipment, body-building equipment, personal digital assistant etc..
Referring to Fig. 7, device 800 may include following one or more components: processing component 802, memory 804, power supply
Component 806, multimedia component 808, audio component 810, the interface 812 of input/output (I/O), sensor module 814, and
Communication component 816.
The integrated operation of the usual control device 800 of processing component 802, such as with display, telephone call, data communication, phase
Machine operation and record operate associated operation.Processing component 802 may include that one or more processors 820 refer to execute
It enables, to perform all or part of the steps of the methods described above.In addition, processing component 802 may include one or more modules, just
Interaction between processing component 802 and other assemblies.For example, processing component 802 may include multi-media module, it is more to facilitate
Interaction between media component 808 and processing component 802.
Memory 804 is configured as storing various types of data to support the operation in device 800.These data are shown
Example includes the instruction of any application or method for operating on device 800, contact data, and telephone book data disappears
Breath, picture, video etc..Memory 804 can be by any kind of volatibility or non-volatile memory device or their group
It closes and realizes, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM) is erasable to compile
Journey read-only memory (EPROM), programmable read only memory (PROM), read-only memory (ROM), magnetic memory, flash
Device, disk or CD.
Power supply module 806 provides electric power for the various assemblies of device 800.Power supply module 806 may include power management system
System, one or more power supplys and other with for device 800 generate, manage, and distribute the associated component of electric power.
Multimedia component 808 includes the screen of one output interface of offer between described device 800 and user.One
In a little embodiments, screen may include liquid crystal display (LCD) and touch panel (TP).If screen includes touch panel, screen
Curtain may be implemented as touch screen, to receive input signal from the user.Touch panel includes one or more touch sensings
Device is to sense the gesture on touch, slide, and touch panel.The touch sensor can not only sense touch or sliding action
Boundary, but also detect duration and pressure associated with the touch or slide operation.In some embodiments, more matchmakers
Body component 808 includes a front camera and/or rear camera.When device 800 is in operation mode, such as screening-mode or
When video mode, front camera and/or rear camera can receive external multi-medium data.Each front camera and
Rear camera can be a fixed optical lens system or have focusing and optical zoom capabilities.
Audio component 810 is configured as output and/or input audio signal.For example, audio component 810 includes a Mike
Wind (MIC), when device 800 is in operation mode, when such as call mode, recording mode, and voice recognition mode, microphone is matched
It is set to reception external audio signal.The received audio signal can be further stored in memory 804 or via communication set
Part 816 is sent.In some embodiments, audio component 810 further includes a loudspeaker, is used for output audio signal.
I/O interface 812 provides interface between processing component 802 and peripheral interface module, and above-mentioned peripheral interface module can
To be keyboard, click wheel, button etc..These buttons may include, but are not limited to: home button, volume button, start button and lock
Determine button.
Sensor module 814 includes one or more sensors, and the state for providing various aspects for device 800 is commented
Estimate.For example, sensor module 814 can detecte the state that opens/closes of device 800, and the relative positioning of component, for example, it is described
Component is the display and keypad of device 800, and sensor module 814 can be with 800 1 components of detection device 800 or device
Position change, the existence or non-existence that user contacts with device 800,800 orientation of device or acceleration/deceleration and device 800
Temperature change.Sensor module 814 may include proximity sensor, be configured to detect without any physical contact
Presence of nearby objects.Sensor module 814 can also include optical sensor, such as CMOS or ccd image sensor, at
As being used in application.In some embodiments, which can also include acceleration transducer, gyro sensors
Device, Magnetic Sensor, pressure sensor or temperature sensor.
Communication component 816 is configured to facilitate the communication of wired or wireless way between device 800 and other equipment.Device
800 can access the wireless network based on communication standard, such as WiFi, 2G or 3G or their combination.In an exemplary implementation
In example, communication component 816 receives broadcast singal or broadcast related information from external broadcasting management system via broadcast channel.
In one exemplary embodiment, the communication component 816 further includes near-field communication (NFC) module, to promote short range communication.Example
Such as, NFC module can be based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra wide band (UWB) technology,
Bluetooth (BT) technology and other technologies are realized.
In the exemplary embodiment, device 800 can be believed by one or more application specific integrated circuit (ASIC), number
Number processor (DSP), digital signal processing appts (DSPD), programmable logic device (PLD), field programmable gate array
(FPGA), controller, microcontroller, microprocessor or other electronic components are realized, for executing the above method.
In the exemplary embodiment, a kind of non-volatile computer readable storage medium storing program for executing is additionally provided, for example including calculating
The memory 804 of machine program instruction, above-mentioned computer program instructions can be executed above-mentioned to complete by the processor 820 of device 800
Method.
The disclosure can be system, method and/or computer program product.Computer program product may include computer
Readable storage medium storing program for executing, containing for making processor realize the computer-readable program instructions of various aspects of the disclosure.
Computer readable storage medium, which can be, can keep and store the tangible of the instruction used by instruction execution equipment
Equipment.Computer readable storage medium for example can be-- but it is not limited to-- storage device electric, magnetic storage apparatus, optical storage
Equipment, electric magnetic storage apparatus, semiconductor memory apparatus or above-mentioned any appropriate combination.Computer readable storage medium
More specific example (non exhaustive list) includes: portable computer diskette, hard disk, random access memory (RAM), read-only deposits
It is reservoir (ROM), erasable programmable read only memory (EPROM or flash memory), static random access memory (SRAM), portable
Compact disk read-only memory (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanical coding equipment, for example thereon
It is stored with punch card or groove internal projection structure and the above-mentioned any appropriate combination of instruction.Calculating used herein above
Machine readable storage medium storing program for executing is not interpreted that instantaneous signal itself, the electromagnetic wave of such as radio wave or other Free propagations lead to
It crosses the electromagnetic wave (for example, the light pulse for passing through fiber optic cables) of waveguide or the propagation of other transmission mediums or is transmitted by electric wire
Electric signal.
Computer-readable program instructions as described herein can be downloaded to from computer readable storage medium it is each calculate/
Processing equipment, or outer computer or outer is downloaded to by network, such as internet, local area network, wide area network and/or wireless network
Portion stores equipment.Network may include copper transmission cable, optical fiber transmission, wireless transmission, router, firewall, interchanger, gateway
Computer and/or Edge Server.Adapter or network interface in each calculating/processing equipment are received from network to be counted
Calculation machine readable program instructions, and the computer-readable program instructions are forwarded, for the meter being stored in each calculating/processing equipment
In calculation machine readable storage medium storing program for executing.
Computer program instructions for executing disclosure operation can be assembly instruction, instruction set architecture (ISA) instructs,
Machine instruction, machine-dependent instructions, microcode, firmware instructions, condition setup data or with one or more programming languages
The source code or object code that any combination is write, the programming language include the programming language-of object-oriented such as
Smalltalk, C++ etc., and conventional procedural programming languages-such as " C " language or similar programming language.Computer
Readable program instructions can be executed fully on the user computer, partly execute on the user computer, be only as one
Vertical software package executes, part executes on the remote computer or completely in remote computer on the user computer for part
Or it is executed on server.In situations involving remote computers, remote computer can pass through network-packet of any kind
It includes local area network (LAN) or wide area network (WAN)-is connected to subscriber computer, or, it may be connected to outer computer (such as benefit
It is connected with ISP by internet).In some embodiments, by utilizing computer-readable program instructions
Status information carry out personalized customization electronic circuit, such as programmable logic circuit, field programmable gate array (FPGA) or can
Programmed logic array (PLA) (PLA), the electronic circuit can execute computer-readable program instructions, to realize each side of the disclosure
Face.
Referring herein to according to the flow chart of the method, apparatus (system) of the embodiment of the present disclosure and computer program product and/
Or block diagram describes various aspects of the disclosure.It should be appreciated that flowchart and or block diagram each box and flow chart and/
Or in block diagram each box combination, can be realized by computer-readable program instructions.
These computer-readable program instructions can be supplied to general purpose computer, special purpose computer or other programmable datas
The processor of processing unit, so that a kind of machine is produced, so that these instructions are passing through computer or other programmable datas
When the processor of processing unit executes, function specified in one or more boxes in implementation flow chart and/or block diagram is produced
The device of energy/movement.These computer-readable program instructions can also be stored in a computer-readable storage medium, these refer to
It enables so that computer, programmable data processing unit and/or other equipment work in a specific way, thus, it is stored with instruction
Computer-readable medium then includes a manufacture comprising in one or more boxes in implementation flow chart and/or block diagram
The instruction of the various aspects of defined function action.
Computer-readable program instructions can also be loaded into computer, other programmable data processing units or other
In equipment, so that series of operation steps are executed in computer, other programmable data processing units or other equipment, to produce
Raw computer implemented process, so that executed in computer, other programmable data processing units or other equipment
Instruct function action specified in one or more boxes in implementation flow chart and/or block diagram.
The flow chart and block diagram in the drawings show system, method and the computer journeys according to multiple embodiments of the disclosure
The architecture, function and operation in the cards of sequence product.In this regard, each box in flowchart or block diagram can generation
One module of table, program segment or a part of instruction, the module, program segment or a part of instruction include one or more use
The executable instruction of the logic function as defined in realizing.In some implementations as replacements, function marked in the box
It can occur in a different order than that indicated in the drawings.For example, two continuous boxes can actually be held substantially in parallel
Row, they can also be executed in the opposite order sometimes, and this depends on the function involved.It is also noted that block diagram and/or
The combination of each box in flow chart and the box in block diagram and or flow chart, can the function as defined in executing or dynamic
The dedicated hardware based system made is realized, or can be realized using a combination of dedicated hardware and computer instructions.
The presently disclosed embodiments is described above, above description is exemplary, and non-exclusive, and
It is not limited to disclosed each embodiment.Without departing from the scope and spirit of illustrated each embodiment, for this skill
Many modifications and changes are obvious for the those of ordinary skill in art field.The selection of term used herein, purport
In the principle, practical application or technological improvement to the technology in market for best explaining each embodiment, or lead this technology
Other those of ordinary skill in domain can understand each embodiment disclosed herein.
Claims (14)
1. a kind of barrage methods of exhibiting characterized by comprising
According to the user data of target user, the corresponding user vector of the target user is determined；
Determine the corresponding barrage vector of candidate's barrage；
Determine the similarity of user vector barrage vector corresponding with candidate's barrage；
According to the similarity of user vector barrage vector corresponding with candidate's barrage, determined from the candidate barrage
For the recommendation barrage of the target user；
In the case where barrage function is opened, the recommendation barrage is shown.
2. the method according to claim 1, wherein the user data includes user behavior data and static use
One or both of user data.
3. method according to claim 1 or 2, which is characterized in that according to the user data of target user, determine the mesh
Mark the corresponding user vector of user, comprising:
According to the user data of the target user, the feature vector of the target user is determined；
According to the feature vector of the target user, the corresponding user vector of the target user is determined.
4. according to the method described in claim 3, it is characterized in that, according to the feature vector of the target user, described in determination
The corresponding user vector of target user, comprising:
It will be in the feature vector input prediction model of the target user, wherein the prediction model is for predicting the target
The corresponding next user behavior data of user；
The output vector of the layer second from the bottom of the prediction model is determined as the corresponding user vector of the target user.
5. according to the method described in claim 4, it is characterized in that, the method also includes:
Training dataset is obtained, the training dataset includes each in the user data and the multiple user of multiple users
The corresponding next user behavior data of user；
According to the user data of the multiple user, the feature vector of each user in the multiple user is determined respectively；
Respectively using the feature vector of user each in the multiple user as the input of the prediction model, by the multiple use
Output of the corresponding next user behavior data of each user as the prediction model in family, the training prediction model.
6. the method according to claim 1, wherein showing the recommendation barrage, comprising:
According to the similarity for recommending barrage and the user vector, the exhibition method for recommending barrage is determined.
7. a kind of barrage shows device characterized by comprising
User vector determining module, for the user data according to target user, determine the corresponding user of the target user to
Amount；
Barrage vector determining module, for determining the corresponding barrage vector of candidate barrage；
Similarity determining module, for determining the similarity of user vector barrage vector corresponding with candidate's barrage；
Recommend barrage determining module, for according to the similar of user vector barrage vector corresponding with candidate's barrage
Degree determines the recommendation barrage for being directed to the target user from the candidate barrage；
Recommend barrage display module, for showing the recommendation barrage in the case where barrage function is opened.
8. device according to claim 7, which is characterized in that the user data includes user behavior data and static use
One or both of user data.
9. device according to claim 7 or 8, which is characterized in that the user vector determining module includes:
Feature vector determines submodule, for the user data according to the target user, determines the feature of the target user
Vector；
User vector determines submodule, for the feature vector according to the target user, determines that the target user is corresponding
User vector.
10. device according to claim 9, which is characterized in that the user vector determines that submodule is used for:
It will be in the feature vector input prediction model of the target user, wherein the prediction model is for predicting the target
The corresponding next user behavior data of user；
The output vector of the layer second from the bottom of the prediction model is determined as the corresponding user vector of the target user.
11. device according to claim 10, which is characterized in that described device further include:
Training dataset obtains module, and for obtaining training dataset, the training dataset includes the number of users of multiple users
The corresponding next user behavior data of each user accordingly and in the multiple user；
Feature vector determining module determines each in the multiple user respectively for the user data according to the multiple user
The feature vector of a user；
Prediction model training module, for respectively using the feature vector of user each in the multiple user as the prediction mould
The input of type, using the corresponding next user behavior data of user each in the multiple user as the defeated of the prediction model
Out, the training prediction model.
12. device according to claim 7, which is characterized in that the recommendation barrage display module is used for:
According to the similarity for recommending barrage and the user vector, the exhibition method for recommending barrage is determined.
13. a kind of barrage shows device characterized by comprising
Processor；
Memory for storage processor executable instruction；
Wherein, the processor is configured to method described in any one of perform claim requirement 1 to 6.
14. a kind of non-volatile computer readable storage medium storing program for executing, is stored thereon with computer program instructions, which is characterized in that institute
It states and realizes method described in any one of claim 1 to 6 when computer program instructions are executed by processor.
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| CN201711174545.4A CN109819304A (en) | 2017-11-22 | 2017-11-22 | Barrage methods of exhibiting and device |
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| CN201711174545.4A CN109819304A (en) | 2017-11-22 | 2017-11-22 | Barrage methods of exhibiting and device |
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