CN110163404A - A kind of diffusion of information prediction technique, device and server, storage medium - Google Patents
A kind of diffusion of information prediction technique, device and server, storage medium Download PDFInfo
- Publication number
- CN110163404A CN110163404A CN201810606271.XA CN201810606271A CN110163404A CN 110163404 A CN110163404 A CN 110163404A CN 201810606271 A CN201810606271 A CN 201810606271A CN 110163404 A CN110163404 A CN 110163404A
- Authority
- CN
- China
- Prior art keywords
- user
- training
- vector
- message
- target topic
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000009792 diffusion process Methods 0.000 title claims abstract description 81
- 238000000034 method Methods 0.000 title claims abstract description 46
- 239000013598 vector Substances 0.000 claims abstract description 266
- 230000000644 propagated effect Effects 0.000 claims abstract description 20
- 230000001902 propagating effect Effects 0.000 claims abstract description 11
- 238000012549 training Methods 0.000 claims description 197
- 230000006870 function Effects 0.000 claims description 92
- 238000013507 mapping Methods 0.000 claims description 86
- 238000004590 computer program Methods 0.000 claims description 8
- 230000006854 communication Effects 0.000 claims description 7
- 238000010586 diagram Methods 0.000 description 14
- 238000004891 communication Methods 0.000 description 3
- 235000013399 edible fruits Nutrition 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000006399 behavior Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000000149 penetrating effect Effects 0.000 description 1
- 238000005295 random walk Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/01—Social networking
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Computing Systems (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Software Systems (AREA)
- Tourism & Hospitality (AREA)
- Entrepreneurship & Innovation (AREA)
- Artificial Intelligence (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Quality & Reliability (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Development Economics (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The embodiment of the invention discloses a kind of diffusion of information prediction techniques, device and server, storage medium, wherein method comprises determining that the target message under target topic, it obtains for indicating the first eigenvector of the first user and the second feature vector of second user in user's set under the target topic, obtain the theme feature vector for indicating the target topic, it calls and propagates estimation model to the first eigenvector, the second feature vector and the theme feature vector carry out that calculated result is calculated, wherein, the estimation model of propagating is pre-set for calculating the predictive information for propagating related news under the target topic in user's set between user, the DIFFUSION PREDICTION information of the target message is determined according to the calculated result, path when message is propagated in user's set can be achieved to predict.
Description
Technical field
The present invention relates to machine learning techniques field more particularly to a kind of diffusion of information prediction technique, device and server,
Storage medium.
Background technique
Currently universal with the continuous development of Smart-Its and smart machine, user from network or can pass through
Other users obtain a large amount of message source, various message can also be transmitted to other users.Such as: user may be by oneself
The news messages on periphery are sent to other users, it is also possible to forward the interested news messages in web browsing, and to disappear
It ceases and is spread in user's circle where user, influenced so as to form certain public opinion.
Diffusion analysis for message such as news, advertisements is typically all specified at one according to the message at present
Start to propagate in user's set (such as set that the correspondence user of each account is constituted in some social network-i i-platform), and passes through
After a period of time, according to propagation condition of the message in user set, determine that the message collects in the user
The spread conditions such as spread scope, propagation time in conjunction.And how to be diffused prediction to message in advance becomes current research
Hot spot.
Summary of the invention
The embodiment of the invention provides a kind of diffusion of information prediction technique, device and server, storage mediums, it can be achieved that right
The prediction of event information spread condition in user gathers.
On the one hand, the embodiment of the invention provides a kind of diffusion of information prediction techniques, comprising:
Target message under determining target topic in user's set；
It obtains for indicating that the first eigenvector of the first user and second is used in user's set under the target topic
The second feature vector at family；
Obtain the theme feature vector for indicating the target topic；
Call propagate estimation model to the first eigenvector, the second feature vector and the theme feature to
Amount carries out that calculated result is calculated, wherein the propagation estimation model is pre-set for calculating user's set
The predictive information of related news under the target topic is propagated between middle user；
The DIFFUSION PREDICTION information of the target message is determined according to the calculated result.
On the other hand, the embodiment of the invention provides a kind of diffusion of information prediction meanss, comprising:
Determination unit, for determining the target message under target topic in user gathers；
Acquiring unit, for obtaining the fisrt feature for indicating the first user in user's set under the target topic
The second feature vector of vector sum second user；
The acquiring unit is also used to obtain the theme feature vector for indicating the target topic；
Computing unit, for call propagate estimation model to the first eigenvector, the second feature vector and
The theme feature vector carries out that calculated result is calculated, wherein the propagation estimation model is pre-set based on
Calculate the predictive information for propagating related news under the target topic in user's set between user；
The determination unit is also used to determine the DIFFUSION PREDICTION information of the target message according to the calculated result.
In another aspect, the embodiment of the invention provides a kind of server, including processor and memory, the processor and
The memory is connected with each other, wherein for storing computer program instructions, the processor is configured for the memory
Described program instruction is executed, realizes above-mentioned diffusion of information prediction technique.
In another aspect, the embodiment of the invention provides a kind of computer readable storage medium, the computer-readable storage
Media storage has computer program, and the computer program includes program instruction, and described program instructs when being executed by a processor
The method for making the processor execute above-mentioned first aspect.
In embodiments of the present invention, it determines the target message under target topic, propagation estimation model can be called to acquisition
For indicating that the theme of the feature vector of the first user and second user and the target topic is special in user's set under target topic
Sign vector carries out that calculated result is calculated, then the DIFFUSION PREDICTION letter of the target message can be determined according to the calculated result
Breath, to realize the prediction of the spread condition to target message in user's set.
Detailed description of the invention
Technical solution in order to illustrate the embodiments of the present invention more clearly, below will be to needed in embodiment description
Attached drawing is briefly described, it should be apparent that, drawings in the following description are some embodiments of the invention, general for this field
For logical technical staff, without creative efforts, it is also possible to obtain other drawings based on these drawings.
Fig. 1 is a kind of schematic flow diagram of diffusion of information prediction technique provided in an embodiment of the present invention；
Fig. 2 is the schematic flow diagram of the acquisition methods of the user characteristics vector of the embodiment of the present invention；
Fig. 3 is the schematic flow diagram of the training method of the associated vector of the embodiment of the present invention；
Fig. 4 a is a kind of diffusion of information network diagram provided in an embodiment of the present invention；
Fig. 4 b is that a diffusion of information path of diffusion of information network as shown in fig. 4 a provided in an embodiment of the present invention shows
It is intended to；
Fig. 4 c is another diffusion of information path of diffusion of information network as shown in fig. 4 a provided in an embodiment of the present invention
Schematic diagram；
Fig. 5 is a kind of diffusion of information path schematic diagram provided in an embodiment of the present invention；
Fig. 6 is a kind of schematic block diagram of diffusion of information prediction meanss provided in an embodiment of the present invention；
Fig. 7 is a kind of schematic block diagram of server provided in an embodiment of the present invention.
Specific embodiment
In embodiments of the present invention, the various types of message such as news messages, advertisement information needs propagated,
Can these message start propagate before or propagate complete before, or even propagate complete after, as needed to message
Spread condition is predicted, in embodiments of the present invention, when the expansion for needing a certain target message under the target topic to propagation
When scattered situation is predicted, can by under target topic indicate user set in the feature of the first user and second user to
The theme feature vector of amount and the target topic calls and propagates estimation model and be calculated one for being predicted
Calculated result, and then determine the target message in the user gathers between two users according to the calculated result
Predictive information, the predictive information are mainly used to predict diffusion feelings of the target message between the first user and second user
Condition, such as the predictive information may include indicating that the target message is broadcast to the letter such as probability of second user from the first user
Breath.Each in user's set with can be used as the first user or second user per family, user's collection available in this way
The a large amount of predictive information about target message propagation condition closed are based on a large amount of predictive information, can predict to obtain the mesh
Situations such as marking spread condition of the message in user set, such as can determining the number scale of diffusion, diffusion distance.
In one embodiment, indicated under target topic the first user first eigenvector and second user second
Feature vector presets to obtain, and the feature vector of the first user under the target topic for uniquely indicating described the
One user, and the feature vector of second user is then used to uniquely indicate the second user under the target topic.Difference is main
Topic can predefine unique theme feature vector, for example, pre-set for uniquely indicate the theme feature of current political news to
Amount, pre-sets the theme feature vector for uniquely indicating sports news.In one embodiment, the theme of target topic
The user characteristics vector of feature vector, different user under target topic is pre- to first pass through model to train generation.
In one embodiment, which specifically can be the propagation estimation model.Propagation estimation model is used to
Probability value or distance value (Euclidean distance value) are calculated based on vector, and user's set includes the first eigenvector, the
The feature vector of all users including two feature vectors, the theme feature vector of the target topic, is all based on the biography
Estimation model is broadcast come what is trained, specifically can be the N message based on target topic in user set after propagation,
Based on known propagation path, to the initial vector of the initial vector sum target topic of each user in user's set
It is trained, finally obtains the theme feature of feature vector and the target topic of each user under the target topic
Vector.
Estimate model come to message to based on feature vector and theme feature vector, propagation first in embodiments of the present invention
Propagation carry out prediction be illustrated, as shown in Figure 1, providing a kind of schematic flow diagram of diffusion of information prediction technique, the party
Method can complete to target message user set in spread condition predict, first in S101, determine target topic
Under target message, such as some news messages under current political news, and obtaining for indicating user under the target topic
The second feature vector of the first eigenvector of first user and second user in set.First eigenvector and second feature to
Amount can distinguish the different user under same target topic.It include a large amount of user in user's set, these users can be
The user of specified group, such as specified student group user, specified worker's group of subscribers or certain being manually specified
It may check a bit, the user of communication target message.First user and second user can be any two in user's set and use
Family.The user characteristics vector of each user equal existence anduniquess under the target topic in user's set.The institute of first user
It states first eigenvector and can refer to the related of subsequent embodiment to the specific acquisition modes of the second feature vector of second user and retouch
It states.
After obtaining the first eigenvector and second feature vector, obtain in S102 for indicating the target
The theme feature vector of theme.In one embodiment, the theme feature vector of different target theme is different, but same target master
It is identical for inscribing the theme feature vector of lower different target message, for example, the theme feature vector e under current political news theme1
It indicates, the theme feature vector under entertainment news theme can use e2It indicates.Equally, the theme feature vector of different themes is specific
Acquisition modes can refer to the description of subsequent embodiment.
It should be noted that the sequencing that the execution of step S101 and step S102 is not necessarily to, can be performed simultaneously step
Rapid S101 and step S102, can also first carry out step S101, then execute step S102, or first carry out step S102, then execute
Step S101.
The embodiment of the present invention is in the case where getting the target topic for indicating that first of the first user in user's set is special
Vector is levied, after the theme feature vector of the second feature vector sum target topic of second user, in S103, calls and propagates estimation
Model carries out the first eigenvector, the second feature vector and the theme feature vector calculating knot is calculated
Fruit, wherein propagation estimation model is pre-set to propagate the mesh between user for calculating in user's set
The predictive information of related news under theme is marked, which refers to any message under the target topic.
In one embodiment, the calculated result (calculated result i.e. the predictive information for propagating estimation model)
It can be the information an of probability or Euclidean distance etc, and propagation estimation model then can be accordingly for based on probability
It calculates or the objective function apart from calculating is come the model constructed in advance, propagation estimation model is for above-mentioned first referred to
The model that feature vector, second feature vector and theme feature vector are calculated.In one embodiment, it is believed that first
User is sender user, and second user is recipient user, and the first eigenvector can use Y1It indicates, described the
Two feature vectors use Y2It indicates, the theme feature vector of the target topic uses e1Indicate, propagate estimation model be according to
Predictive information is calculated in the objective function for calculating distance, such as the objective function may is that Loss=| | Y1+e1-Y2
||2, a distance value can be calculated based on the propagation estimation model that the objective function constructs, the distance value can be made
For predictive information, to estimate whether the target message can travel to second user from the first user.In one embodiment, such as
Fruit is smaller (being less than preset distance threshold) based on above-mentioned LOSS function calculated distance value, it may be considered that first
User is very likely broadcast to second user, and prediction result can be determined as to the first user to be broadcast to second for target message
User, and if LOSS function calculated distance value is larger (being greater than preset distance threshold), it may be considered that first
It is smaller that user is broadcast to a possibility that second user, will not even be broadcast to second user, then prediction result can be determined
Target message will not be broadcast to second user for the first user.
Each in user set with can be used as sender user i.e. the first user, and each user per family
It can also be used as recipient user i.e. second user.By above-mentioned step, any two user in user's set is calculated
Between predictive information.The DIFFUSION PREDICTION information for determining the target message according to the calculated result in S104, that is, exist
Global analysis is carried out based on a large amount of predictive information in S104, is determined in user gathers, the target message can be at which
It is propagated between user, the DIFFUSION PREDICTIONs information such as range size (such as quantity of the user of propagation) of propagation.
In embodiments of the present invention, determine the target message under target topic, can call propagation estimation model to one or
Possible communication target message is predicted between each user in user's set of the multiple desired dispensings of person, it may be determined that about target topic
Target message DIFFUSION PREDICTION information, grasp accurate the analysis of public opinion data, be conducive to the accurate dispensing of certain message, no
Message can be launched to the user group that may not propagate certain message.
Fig. 2 is referred to again, is first eigenvector and the second spy of second user of the first user of the embodiment of the present invention
The schematic flow diagram of the acquisition methods of vector is levied, the acquisition methods can correspond to the S101 in above-described embodiment, firstly,
In S201, obtain for indicate the second basis of the first foundation vector sum second user of the first user in user set to
Amount.In embodiments of the present invention, be different from the above-mentioned first eigenvector referred to and second feature vector, the first foundation to
Amount is for indicating first user in user gathers, and second basis vector is for indicating institute in user gathers
Second user is stated, the different representation methods that different user uses in user set ands, which is realized, gathers the user
The differentiation of middle user, wherein whether second basis vector of first foundation vector sum is propagated with the first user and second user
Theme corresponding to some message is unrelated.
Obtain for indicate user set in the first user first foundation vector sum second user second basis to
After amount, the mapping function under target topic can be obtained, the mapping function includes sender's mapping function and reception in S202
Square mapping function, and the first foundation vector is mapped according to the mapping function in S203 to obtain first spy
Vector is levied, and second basis vector is mapped to obtain the second feature vector.That is, if described first
User is as sender user, and the second user is as recipient user, i.e., according to described sender mapping function to described
First foundation vector is mapped to obtain the first eigenvector, and according to recipient's mapping function to second base
Plinth vector is mapped to obtain second feature vector.It in one embodiment, can be by the first of sender user (the first user)
Basis vector is denoted as u1, the second basis vector of recipient user's (second user) is denoted as u2, and can will be under the target topic
Sender's mapping function is denoted as w1, recipient's mapping function is denoted as w2, then according to described sender mapping function w1By described first
Basis vector u1It is mapped to obtain the first eigenvector w of first user1u1, and according to recipient's mapping function
w2By the second basis vector u2It is mapped to obtain the second feature vector w of the second user2u2。
In embodiments of the present invention, by obtaining the base for indicating the first user and second user in user's set
Mapping function under target topic described in plinth vector sum, can map to obtain the feature of first user and the second user to
Amount, and the user in user set can be traversed according to the mode and obtain in user's set each user in some theme
Under user characteristics vector, due to the difference that is indicated according to the basis vector of user of feature vector of user set, and
Mapping function can flexibly be changed based on different themes, so that the method is bigger about the scalability of different themes.
Fig. 3 is referred to again, is the schematic flow diagram of the training method of the associated vector of the embodiment of the present invention.Based on the instruction
Practicing method can train to obtain in user's set under the associated vector of multiple users, the theme feature vector of various themes and theme
The mapping function being related to.
When being trained to associated vector, it is necessary first to determine that training supervision message obtains the target in S301
Diffusion path of the training message in communication process under theme, is determined according to the diffusion path for indicating the training
Message first user from user set propagates to the training supervision message of the second user.Based on known diffusion road
Diameter come to training exercise supervision, the user A in the diffusion path is bound to be broadcast to user B, that is to say, that user A and use
The probability propagated between the B of family or apart from the vector for coming training user A, user B and theme feature as training supervision message.
The trained message is the training message under the target topic, and the trained message can be gathered in user
In user between be diffused, spread result related data be used as train supervision message.In one embodiment, may be used
Training message is propagated in above-mentioned user set, is determined in user's set further according to the searching mark of the trained message
The user identifier of the trained message has been propagated, and has been established according to the user identifier and the trained transmission of news direction
The diffusion path of the trained message, wherein the searching mark includes the keyword and/or label of the trained message, because
This, as long as any one user's click has been read about the content of the trained message or in the instruction in user set
Practice to make comments under message and content or as comment content issue or the training disappears using the trained message
Breath has been sent to other users as forwarding content, all can keyword based on the trained message and/or label determine institute
Whether the user stated in user's set has received the training message, or whether has sent the training message, and then can determine that
The user identifier of the trained message is propagated in user's set and the user gathers the direction for propagating the trained message,
Obtain a plurality of propagation path.
It in one embodiment, can be by the keyword and/or Hashtag of the event information (training message) under different themes
Partly or entirely the click reading content of user, comment content and forwarding content carry out in (Hash label) and the user set
Matching similarly can extract multiple out to extract the diffusion of information path in user's set about the trained message
The diffusion of information path in the diffusion of information path of event information, the multiple event information may make up different event information (still
Belonging to target topic) diffusion of information network in user group (user's set) is as shown in fig. 4 a two trained message pair
The diffusion of information network answered includes that (its dispersal direction is respectively with diagram for two diffusion paths in the diffusion of information network
The instruction of the arrow of middle solid line and dotted line), wherein two diffusion paths are as shown in figures 4 b and 4 c.
Fig. 4 a is that the event X that will belong under target topic and event Y is sent to user group M (the user group M includes
User A, B, C, D, E) in be trained, by will be in the keyword and/or label of event X and event Y and the user group M
Click reading information, comment information and/or the forwarding information of user matches, and can extract event as shown in Figure 4 b respectively
The diffusion path of the diffusion path of X and event Y as illustrated in fig. 4 c, the diffusion of the diffusion path of the event X and the event Y
Path constitutes diffusion of information network of the X and Y event information in user group M.So as to based on spreading known to above-mentioned two
Path is special to train the theme of theme corresponding to the user characteristics vector sum event X of user A, B, C, D, E in determining user group M
Levy the theme feature vector of theme corresponding to vector, event Y.
It obtains training supervision message based on diffusion path, that is to say, that show in diffusion path adjacent in diffusion path
Two users between had propagated corresponding trained message, as shown in Figure 4 b, if using a certain Euclidean distance value as distance
Show to have propagated corresponding training message between two users when threshold value, then, the training supervision letter between user A and user B
Breath is the value less than or equal to distance threshold, and similarly, the training supervision message between user B and user C is to be less than or wait
Training supervision message between the value of distance threshold, user C and user E is the value less than or equal to distance threshold.Similarly
Training supervision message between the corresponding two vector users of available Fig. 4 c.
Further in S302, the theme training vector under the target topic is obtained, the theme training vector is made
E ' can be used for the initial vector of the target topic1It indicates.In one embodiment, the theme training vector under target topic can also
To be obtained by the skipgram method in word2vec, or obtained by way of generating at random, theme training to
Amount needs to carry out subsequent training as training data, to obtain can be ultimately utilized in the predictive information being calculated between two users
Theme feature vector.
After determining the theme training vector of the target topic, in S303, call the propagation estimation model to institute
State first training vector, the second user second instruction under the target topic of first user under the target topic
The theme training vector for practicing target topic described in vector sum carries out that trained calculated result is calculated.In one embodiment, institute
Before stating S303, can include determining that the first user the first training vector and second user the second training vector the step of,
It may particularly include: obtaining the training mapping function under the target topic；It will be described first according to the trained mapping function
The first training basis vector that user generates is mapped to obtain first training vector, and by the second of the second user
Training basis vector is mapped to obtain second training vector.First training basis vector is intended to indicate that described first uses
The initial vector at family, the second training basis vector are intended to indicate that the initial vector of the second user.
The trained mapping function includes sender's training mapping function and recipient's training mapping function, if described the
One user is as sender user, and the second user is as recipient user, then first training vector is according to
What sender's mapping function and the first training basis vector generated, second training vector is reflected according to the recipient
Penetrating function and described second trains basis vector to generate.
It is (every based on the path point in the diffusion path after the diffusion path for determining the training message under target topic
One path point indicates a user) carry out random walk, can formation sequence data, then be directed to the data of the serializing
Using skipgram method (a kind of generation in word2vec (a kind of vector field homoemorphism type being understood that for generating computer)
The method for the vector that computer is understood that), obtain each user in user set distributed low-dimensional vector indicate to get
The first training basis vector and the second training basis vector before to training, in order to further obtain the depth of user and event
Semantic feature indicates (i.e. the first training vector of the first user and the second training vector of second user).In one embodiment
In, the first training basis vector can be denoted as u '1, the second training basis vector is denoted as u '2, mapped for trained sender
Function is denoted as w '1, w ' is denoted as trained recipient's mapping function2, then the first training vector generated by mapping is denoted as
w′1u′1, the second training vector is denoted as w '2u′2。
In one embodiment, based on training message, if its propagation path is as shown in figure 5, can be obtained the propagation road
The first training foundation characteristic vector u ' of neighboring user A and B on diameter1With the second training foundation characteristic vector u '2, and in event master
Inscribe on relevant latent sheaf space based on training mapping function (including sender training mapping function w '1It is mapped with recipient's training
Function w '2) mapped, obtaining new user characteristics indicates (the first i.e. above-mentioned training vector w '1u′1With the second training vector
W '2U '2).In one embodiment, after the diffusion path for determining the trained message, the first training base of first user
Second training basis vector of plinth vector sum second user can also be randomly generated.First training basis vector and the second training
Basis vector is trained as the trained values of user characteristics, with obtain can be ultimately utilized in be calculated it is pre- between two users
The first eigenvector and second feature vector of measurement information.
The trained calculated result can be based on above-mentioned first training vector, the second training vector and theme training to
A trained probability being calculated or training Euclidean distance are measured, and propagation estimation model is then accordingly for based on upper
The model for stating the training objective function of trained probability calculation or training distance calculating to construct in advance.In one embodiment, such as
Above-mentioned, the expression of the first training vector is for w '1u′1, the second training vector is expressed as w '2u′2, theme training vector is expressed as e '1,
Described propagate estimates that model may is that Loss=according to the training objective function for calculating distance | | w '1u′1+e′1-w′2u′2|
|2。
In one embodiment, when the training calculated result and the training for calling the propagation estimation model to be calculated
When supervision message is consistent or closely similar, then illustrate the first training basis vector, the second training basis vector, transmission at this time
Fang Xunlian mapping function, recipient's training mapping function and theme training vector can effectively obtain correct calculated result,
That is the message of meeting communication target theme between two users, these vectors are effectively, can be directed to again other including described in
The diffusion path of first user and second user carrys out the above-mentioned calculating of further progress, so that u ' under each path1、w′1、u′2、
w′2And e '1It is all satisfied calculated result and the training supervision consistent condition of message, and then from u '1、w′1、u′2、w′2And e '1Instruction
Get final u1、w1、u2、w2And e1。
If the trained calculated result and the training supervision message are inconsistent, need in S304, according to described
Training supervision message and the trained calculated result, to the training of first training vector, the second training vector and the theme
Vector is updated, to obtain optimal vector.In one embodiment, when the training supervision message and the training calculate knot
When fruit is inconsistent, can to it is above-mentioned first training basis vector, second training basis vector, training mapping function and theme training to
One or more in amount carries out vector update, calls and propagates estimation model based on updated u '1、w′1、u′2、w′2And
e′1It is calculated, so that consistent with training supervision message according to the calculated result that estimation model is calculated is propagated.
In one embodiment, when first user is as sender user, the second user is recipient's use
Family includes: then to be updated to obtain sender to described sender training mapping function to the update of first training vector
Mapping function, and the first training basis vector is updated to obtain above-mentioned first foundation vector；To second training
It includes: to be updated to obtain recipient's mapping function to recipient training mapping function that vector, which is updated, and to described
Second training basis vector is updated to obtain above-mentioned second basis vector.
In embodiments of the present invention, by obtaining diffusion road of the training message under the target topic in communication process
Diameter, it may be determined that out for indicating that the trained message first user from user set propagates to the instruction of the second user
Practice supervision message, then can obtain the theme training vector under the target topic, and calls propagation estimation model to described first
Training vector, the second training vector and the theme training vector carry out that trained calculated result is calculated, when the training meter
Calculate result and when the inconsistent supervision message, it is trained to first training vector, the second training vector and the theme to
Amount is updated, so that it is consistent with the training supervision message using the result that updated vector is calculated, it realizes
To the continuous adjustment for propagating estimation model, so that the accuracy for being diffused prediction to the target message can be improved.
The embodiment of the invention also provides a kind of diffusion of information prediction meanss, the diffusion of information prediction meanss are for executing
The unit of aforementioned described in any item methods.It specifically, is that a kind of diffusion of information provided in an embodiment of the present invention is pre- referring to Fig. 6
Survey the schematic block diagram of device.The diffusion of information prediction meanss of the present embodiment comprise determining that unit 601, acquiring unit 602 and meter
Calculate unit 603.In embodiments of the present invention, the diffusion of information prediction meanss can be set needs to carry out message dissemination some
In the server of prediction or some dedicated pre- measurement equipments.
Determination unit 601, for determining the target message under target topic；Acquiring unit 602, for obtaining in institute
Stating indicates the first eigenvector of the first user and the second feature vector of second user in user's set under target topic；It is described
Acquiring unit 602 is also used to obtain the theme feature vector for indicating the target topic；Computing unit 603, for calling
It propagates estimation model the first eigenvector, the second feature vector and the theme feature vector calculate
To calculated result, wherein propagation estimation model is pre-set to pass between user for calculating in user's set
Broadcast the predictive information of related news under the target topic；The determination unit 601 is also used to be determined according to the calculated result
The DIFFUSION PREDICTION information of the target message.
In one embodiment, the acquiring unit 602, for obtaining for indicating user under the target topic
The first eigenvector of first user and when the second feature vector of second user in set, for obtaining for indicating the use
Second basis vector of the first foundation vector sum second user of the first user in the set of family；Obtain reflecting under the target topic
Penetrate function；Mapped to obtain the first eigenvector to the first foundation vector according to the mapping function, and by institute
The second basis vector is stated to be mapped to obtain the second feature vector.
In one embodiment, the mapping function includes sender's mapping function and recipient's mapping function；When described
First user is as sender user, and when the second user is as recipient user, the acquiring unit 602 is reflected according to described
It penetrates function the first foundation vector is mapped to obtain the first eigenvector, and second basis vector is carried out
Mapping obtains the concrete mode of the second feature vector are as follows: according to described sender mapping function to the first foundation vector
It is mapped to obtain the first eigenvector, and second basis vector is reflected according to recipient's mapping function
It penetrates to obtain the second feature vector.
In one embodiment, the diffusion of information prediction meanss further include: updating unit 604, the acquiring unit
602, it is also used to obtain diffusion path of the training message in communication process under the target topic, the determination unit 601
It is determined according to the diffusion path for indicating that it is described that the trained message first user from user set propagates to
The training supervision message of second user；The acquiring unit 602, be also used to obtain under the target topic theme training to
Amount；The computing unit 603 is also used to call the propagation estimation model to first user under the target topic
The theme of the second training vector and the target topic of first training vector, the second user under the target topic is instructed
Practice vector to carry out that trained calculated result is calculated；Updating unit 604, for according to the training supervision message and the training
Calculated result is updated first training vector, the second training vector and the theme training vector.
In one embodiment, the diffusion of information device further include: map unit 605, the acquiring unit 602, also
For obtaining the training mapping function under the target topic；Map unit 605, for that will be according to the trained mapping function
The first training basis vector that first user generates is mapped to obtain first training vector, and will be described second
The second training basis vector that user generates is mapped to obtain second training vector.
In one embodiment, the trained mapping function includes sender's training mapping letter under the target topic
Several and recipient training mapping function；When first user is as sender user, first training vector is basis
What described sender training mapping function and the first training basis vector generated, then the updating unit 604 is specifically used for:
Be updated to obtain sender's mapping function to described sender training mapping function, and to the first training basis vector into
Row, which updates, obtains first foundation vector；When the second user is as recipient user, then second training vector is root
It is generated according to recipient training mapping function and the second training basis vector, the updating unit 604 is specifically used for:
Be updated to obtain recipient's mapping function to recipient training mapping function, and to the second training basis vector into
Row, which updates, obtains the second basis vector.
In one embodiment, the acquiring unit 602 is being passed in the training message for obtaining under the target topic
When diffusion path during broadcasting, for propagating the training message under the target topic in user set；According to institute
The searching mark for stating trained message determines the user identifier that the trained message is propagated in user's set, and according to institute
State the diffusion path that user identifier establishes the trained message.
In one embodiment, the searching mark includes the keyword and/or label of the trained message, the determination
Unit 601 for the searching mark according to the trained message, is being determined to propagate the trained message in user's set
User identifier when, for the corresponding behaviour of user in gathering the keyword of the trained message and/or label with the user
Retrieval matching is carried out as content；It determines to propagate the trained message in user's set based on matched matching result is retrieved
User identifier, and determine the direction of propagation；The operation content that the user generates includes: the click reading content of user, comment
Inside perhaps forward at least one arrived in content.
In embodiments of the present invention, determination unit 601 determines the target message under the target topic, acquiring unit 602
The first eigenvector and second user for indicating the first user in user's set under the target topic can be obtained
Second feature vector, and obtain the theme feature vector for indicating the target topic, then computing unit 603 can call biography
It broadcasts estimation model theme feature vector described in the first eigenvector, the second feature vector sum be calculated
It calculates as a result, the DIFFUSION PREDICTION information for making the determination unit 601 that can determine the target message according to the calculated result, real
The prediction to the diffusion path of target message is showed.
It is a kind of schematic block diagram of server provided in an embodiment of the present invention referring to Fig. 7.In the present embodiment as shown in the figure
Server may include: power supply, shell, the structures such as various required interfaces, such as network interface, user interface etc.
Deng.The server further include: one or more processors 701 and storage device 702.Above-mentioned processor 701 and storage device
702 are connected, and in one embodiment, can be connected by bus 703 between processor 701 and storage device 702.
The server may include user interface, the user interface may include some physical buttons or touch by
The interface module of the compositions such as key can receive the operation of user, which can also can prompt the user with including some
The structures such as the display screen of the information such as the working condition of server.
The storage device 702 may include volatile memory (volatile memory), such as random access memory
(random-access memory, RAM)；Storage device 702 also may include nonvolatile memory (non-volatile
Memory), such as flash memory (flash memory), solid state hard disk (solid-state drive, SSD) etc.；Storage device
702 can also include the combination of the memory of mentioned kind.
The processor 701 can be central processing unit (central processing unit, CPU).The processor
701 can further include hardware chip.Above-mentioned hardware chip can be specific integrated circuit (application-
Specific integrated circuit, ASIC), programmable logic device (programmable logic device,
PLD) etc..The PLD can be field programmable gate array (field-programmable gate array, FPGA), lead to
With array logic (generic array logic, GAL) etc..The combination of the processor 701 or above structure.
In embodiments of the present invention, for the memory 702 for storing computer program, the computer program includes journey
Sequence instruction, processor 701 is used to execute the program instruction of the storage of memory 702, for realizing the respective party in above-described embodiment
Method step.
In one embodiment, the processor 701 is configured to call described program instruction, for determining under target topic
Target message；Obtain the first eigenvector and second for indicating the first user in user's set under the target topic
The second feature vector of user；Obtain the theme feature vector for indicating the target topic；It calls and propagates estimation model pair
The first eigenvector, the second feature vector and the theme feature vector carry out that calculated result is calculated,
In, propagation estimation model is pre-set to propagate the target topic between user for calculating in user's set
The predictive information of lower related news；The DIFFUSION PREDICTION information of the target message is determined according to the calculated result.
In one embodiment, the processor 701 is for obtaining for indicating user's collection under the target topic
The first eigenvector of first user and when the second feature vector of second user in conjunction, for obtaining for indicating the user
Second basis vector of the first foundation vector sum second user of the first user in set；Obtain the mapping under the target topic
Function；The first foundation vector is mapped according to the mapping function to obtain the first eigenvector, and will be described
Second basis vector is mapped to obtain the second feature vector.
In one embodiment, the mapping function includes sender's mapping function and recipient's mapping function, when described
First user is as sender user, and when the second user is as recipient user, the processor 701 is also used to according to institute
It states sender's mapping function the first foundation vector is mapped to obtain the first eigenvector, and according to the reception
Square mapping function maps second basis vector to obtain the second feature vector.
In one embodiment, the processor 701 is also used to obtain the training message under the target topic and is propagating
Diffusion path in the process is determined according to the diffusion path for indicating the trained message the from user set
One user propagates to the training supervision message of the second user；Call the propagation estimation model to first user in institute
State the second training vector and the mesh of the first training vector, the second user under the target topic under target topic
The theme training vector of mark theme carries out that trained calculated result is calculated；According to the training supervision message and the training meter
It calculates as a result, being updated to first training vector, the second training vector and the theme training vector.
In one embodiment, the processor 701 is also used to obtain the training mapping function under the target topic；Root
It is mapped the first training basis vector generated for first user to obtain described first according to the trained mapping function
Training vector, and by the second training basis vector generated for the second user mapped to obtain second training to
Amount.
In one embodiment, the trained mapping function includes sender's training mapping letter under the target topic
Several and recipient training mapping function, when first user is as sender user, first training vector is basis
What described sender training mapping function and the first training basis vector generated, the processor 701 is also used to described
Sender's training mapping function is updated to obtain sender's mapping function, and is updated to the first training basis vector
Obtain first foundation vector；When first user is as sender user, the second user is as recipient user, then
Second training vector is generated according to recipient training mapping function and the second training basis vector, described
Processor 701 is also used to be updated to obtain to the recipient training mapping function recipient's mapping function, and to described the
Two training basis vectors are updated to obtain the second basis vector.
In one embodiment, the processor 701 is being propagated in the training message for obtaining under the target topic
When diffusion path in the process, for propagating the training message under the target topic in user set；According to described
The searching mark of training message determines the user identifier that the trained message is propagated in user's set, and according to described
User identifier establishes the diffusion path of the trained message.
In one embodiment, the searching mark includes the keyword and/or label of the trained message, the processing
Device 701 for the searching mark according to the trained message, is being determined to propagate the trained message in user's set
When user identifier, for the keyword of the trained message and/or label operation content corresponding with the user to be examined
Rope matching；The user identifier that the trained message is propagated in user's set is determined based on matched matching result is retrieved,
And determine the direction of propagation；The operation content that the user generates includes: the click reading content of user, in comment perhaps in forwarding
At least one of arrived in appearance.
Those of ordinary skill in the art will appreciate that realizing all or part of the process in above-described embodiment method, being can be with
Relevant hardware is instructed to complete by computer program, the program can be stored in a computer-readable storage medium
In, the program is when being executed, it may include such as the process of the embodiment of above-mentioned each method.Wherein, the 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 is only section Example of the invention, cannot limit the right of the present invention with this certainly
Range, those skilled in the art can understand all or part of the processes for realizing the above embodiment, and according to right of the present invention
Equivalent variations made by it is required that, still belongs to the scope covered by the invention.
Claims (11)
1. a kind of diffusion of information prediction technique characterized by comprising
Determine the target message under target topic；
Obtain the first eigenvector and second user for indicating first user in user's set under the target topic
Second feature vector；
Obtain the theme feature vector for indicating the target topic；
Call propagate estimation model to the first eigenvector, the second feature vector and the theme feature vector into
Calculated result is calculated in row, wherein propagations estimation model be it is pre-set for calculate the user gather in use
The predictive information of related news under the target topic is propagated between family；
The DIFFUSION PREDICTION information of the target message is determined according to the calculated result.
2. the method according to claim 1, wherein the acquisition is for indicating user under the target topic
The second feature vector of the first eigenvector of first user and second user in set, comprising:
Obtain the second basis vector for indicating the first foundation vector sum second user of the first user in user's set；
Obtain the mapping function under the target topic；
Mapped to obtain the first eigenvector to the first foundation vector according to the mapping function, and by described
Two basis vectors are mapped to obtain the second feature vector.
3. according to the method described in claim 2, it is characterized in that, the mapping function includes sender's mapping function and reception
Square mapping function；
It is described to be reflected according to when the second user is as recipient user when first user is as sender user
It penetrates function the first foundation vector is mapped to obtain the first eigenvector, and second basis vector is carried out
Mapping obtains the second feature vector and includes:
The first foundation vector is mapped according to described sender mapping function to obtain the first eigenvector, and root
Second basis vector is mapped according to recipient's mapping function to obtain the second feature vector.
4. the method according to claim 1, wherein the method also includes:
Diffusion path of the training message in communication process under the target topic is obtained, is determined according to the diffusion path
For indicating that the trained message first user from user set propagates to the training supervision message of the second user；
Obtain the theme training vector under the target topic；
Call the estimation model of propagating to first training vector of first user under the target topic, described second
The theme training vector of second training vector and the target topic of the user under the target topic carries out that instruction is calculated
Practice calculated result；
According to the training supervision message and trained calculated result, to first training vector, second training to
Amount and the theme training vector are updated.
5. according to the method described in claim 4, it is characterized in that, the method also includes:
Obtain the training mapping function under the target topic；
It is mapped the first training basis vector generated for first user to obtain institute according to the trained mapping function
The first training vector is stated, and is mapped the second training basis vector generated for the second user to obtain second instruction
Practice vector.
6. according to the method described in claim 4, it is characterized in that, the trained mapping function is included under the target topic
Sender training mapping function and recipient training mapping function；
When first user is as sender user, first training vector is according to described sender training mapping letter
What the several and described first training basis vector generated, being updated to first training vector includes: to instruct to described sender
Practice mapping function to be updated to obtain sender's mapping function, and the first training basis vector is updated to obtain first
Basis vector；
When the second user is as recipient user, second training vector is according to recipient training mapping letter
What the several and described second training basis vector generated, being updated to second training vector includes: to instruct to the recipient
Practice mapping function to be updated to obtain recipient's mapping function, and the second training basis vector is updated to obtain second
Basis vector.
7. according to the method described in claim 4, it is characterized in that, the training message obtained under the target topic is passing
Diffusion path during broadcasting, comprising:
The training message under the target topic is propagated in user set；
According to the searching mark of the trained message, user's mark that the trained message is propagated in user's set is determined
Know, and establishes the diffusion path of the trained message according to the user identifier.
8. the method according to the description of claim 7 is characterized in that the searching mark includes the keyword of the trained message
And/or label, the searching mark according to the trained message, it determines to propagate the trained message in user's set
User identifier, comprising:
The operation content that the keyword of the trained message and/or label are generated with user in user set is retrieved
Matching；
The user identifier that the trained message is propagated in user's set is determined based on the matched matching result of retrieval,
And determine the direction of propagation；
The operation content that the user generates include: the click reading content of user, perhaps forward in content in comment arrive to
One item missing.
9. a kind of diffusion of information prediction meanss characterized by comprising
Determination unit, for determining the target message under target topic；
Acquiring unit, for obtaining the first eigenvector for indicating the first user in user's set under the target topic
With the second feature vector of second user；
The acquiring unit is also used to obtain the theme feature vector for indicating the target topic；
Computing unit propagates estimation model to the first eigenvector, the second feature vector and described for calling
Theme feature vector carries out that calculated result is calculated, wherein the propagation estimation model is pre-set for calculating institute
State the predictive information for propagating related news under the target topic in user's set between user；
The determination unit is also used to determine the DIFFUSION PREDICTION information of the target message according to the calculated result.
10. a kind of server, which is characterized in that including processor and memory, the processor is mutually interconnected with the memory
It connects, wherein the memory is configured for execution described program and refers to for storing computer program instructions, the processor
It enables, realizes the method according to claim 1.
11. a kind of computer readable storage medium, which is characterized in that the computer-readable recording medium storage has computer journey
Sequence, the computer program include program instruction, and described program instruction executes the processor such as
The described in any item methods of claim 1-8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810606271.XA CN110163404A (en) | 2018-06-12 | 2018-06-12 | A kind of diffusion of information prediction technique, device and server, storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810606271.XA CN110163404A (en) | 2018-06-12 | 2018-06-12 | A kind of diffusion of information prediction technique, device and server, storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110163404A true CN110163404A (en) | 2019-08-23 |
Family
ID=67644982
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810606271.XA Pending CN110163404A (en) | 2018-06-12 | 2018-06-12 | A kind of diffusion of information prediction technique, device and server, storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110163404A (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102831234A (en) * | 2012-08-31 | 2012-12-19 | 北京邮电大学 | Personalized news recommendation device and method based on news content and theme feature |
| CN104933622A (en) * | 2015-03-12 | 2015-09-23 | 中国科学院计算技术研究所 | Microblog popularity degree prediction method based on user and microblog theme and microblog popularity degree prediction system based on user and microblog theme |
| CN106372437A (en) * | 2016-09-07 | 2017-02-01 | 北京邮电大学 | Information diffusion prediction method and device |
| CN106600071A (en) * | 2016-12-20 | 2017-04-26 | 西南石油大学 | Topic-based microblog forwarding prediction method and system |
| CN107330562A (en) * | 2017-07-03 | 2017-11-07 | 扬州大学 | Information dissemination method based on individual consumer's feature |
| CN107908789A (en) * | 2017-12-12 | 2018-04-13 | 北京百度网讯科技有限公司 | Method and apparatus for generating information |
| CN107944635A (en) * | 2017-12-13 | 2018-04-20 | 福州大学 | A kind of information propagation forecast model and method for merging the topic factor |
-
2018
- 2018-06-12 CN CN201810606271.XA patent/CN110163404A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102831234A (en) * | 2012-08-31 | 2012-12-19 | 北京邮电大学 | Personalized news recommendation device and method based on news content and theme feature |
| CN104933622A (en) * | 2015-03-12 | 2015-09-23 | 中国科学院计算技术研究所 | Microblog popularity degree prediction method based on user and microblog theme and microblog popularity degree prediction system based on user and microblog theme |
| CN106372437A (en) * | 2016-09-07 | 2017-02-01 | 北京邮电大学 | Information diffusion prediction method and device |
| CN106600071A (en) * | 2016-12-20 | 2017-04-26 | 西南石油大学 | Topic-based microblog forwarding prediction method and system |
| CN107330562A (en) * | 2017-07-03 | 2017-11-07 | 扬州大学 | Information dissemination method based on individual consumer's feature |
| CN107908789A (en) * | 2017-12-12 | 2018-04-13 | 北京百度网讯科技有限公司 | Method and apparatus for generating information |
| CN107944635A (en) * | 2017-12-13 | 2018-04-20 | 福州大学 | A kind of information propagation forecast model and method for merging the topic factor |
Non-Patent Citations (2)
| Title |
|---|
| GAO SHENG 等: "A Novel Embedding Method for Information Diffusion Prediction in Social Network Big Data", IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, vol. 13, pages 2097 - 2105, XP011658216, DOI: 10.1109/TII.2017.2684160 * |
| 李志清;: "基于LDA主题特征的微博转发预测", 情报杂志, no. 09, pages 158 - 162 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US9252961B2 (en) | Identify experts and influencers in a social network | |
| CN103842983B (en) | For building and send the method and system of information flow | |
| CN103918001B (en) | Comprehension effects of communications propagated through social networking system | |
| ES2881768T3 (en) | Empirical determination of experts and system and procedure for sending questions | |
| Yin et al. | Spatio-temporal recommendation in social media | |
| AU2011292291B2 (en) | Dynamic place visibility in geo-social networking system | |
| CN103716338B (en) | A kind of information-pushing method and device | |
| CN106605418A (en) | Power management of mobile clients using location-based services | |
| JP2014194796A (en) | Methods and systems for determining geographic user profile to determine suitability of targeted content messages based on the profile | |
| US20150242447A1 (en) | Identifying effective crowdsource contributors and high quality contributions | |
| CN107925701A (en) | Notified for the event of application | |
| KR20110073282A (en) | Consolidating input messages for social activity summarization | |
| US20120226521A1 (en) | Utilize Experts and Influencers in a Social Network | |
| US10463975B2 (en) | Automatic fantasy sports data analysis method and apparatus | |
| CN104158748B (en) | A kind of topological detecting method towards system for cloud computing | |
| CN107545451A (en) | A kind of advertisement sending method and device | |
| CN107545444A (en) | A kind of card data recommendation method and device | |
| Barnwal et al. | Enhancing reliability of vehicular participatory sensing network: A Bayesian approach | |
| CN104111981A (en) | Method and device used for providing post messages | |
| Patten et al. | Biogeographical boundaries and Monmonier’s algorithm: a case study in the northern Neotropics | |
| WO2017206566A1 (en) | Message propagation method, and server and computer storage medium | |
| CN105879393A (en) | Webpage game service server and webpage game event reminding method and system | |
| CN107545453A (en) | A kind of information distribution method and device | |
| CN110297967A (en) | Point of interest determines method, apparatus, equipment and computer readable storage medium | |
| CN109299355A (en) | Recommended book list display method and device and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |