CN110060102A - Retail shop where user based on inclined label study positions big data prediction technique - Google Patents
Retail shop where user based on inclined label study positions big data prediction technique Download PDFInfo
- Publication number
- CN110060102A CN110060102A CN201910313789.9A CN201910313789A CN110060102A CN 110060102 A CN110060102 A CN 110060102A CN 201910313789 A CN201910313789 A CN 201910313789A CN 110060102 A CN110060102 A CN 110060102A
- Authority
- CN
- China
- Prior art keywords
- sample
- retail shop
- probability
- user
- candidate
- 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.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computing arrangements based on specific mathematical models
- G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
-
- 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
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0203—Market surveys; Market polls
-
- 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
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0241—Advertisements
- G06Q30/0251—Targeted advertisements
- G06Q30/0261—Targeted advertisements based on user location
Abstract
A kind of user place retail shop positioning big data prediction technique based on inclined label study is claimed in the present invention, comprising: the shopping status data of 101 couples of users carry out pretreatment operation；The 102 candidate quotient paved sets according to corresponding to each sample, which are closed, constructs inclined flag data collection；103 pairs of inclined flag data collection carry out feature extraction operation；104 construct similarity graph according to feature space；105 carry out probability propagation according to similarity graph；106 by propagating a convergent probability, and the retail shop that user will have behavior to interact in future is predicted from the conjunction of the candidate quotient paved set of inclined flag data collection.The present invention is mainly by pre-processing user's history data, extract feature, convert inclined flag data collection, establish label learning model partially, according to the inclined flag data collection of the position behavior of user, the retail shop that user will have behavior to interact in future is predicted from the conjunction of candidate quotient paved set corresponding to each user, is allowed users to obtain more accurately Individualized Notification Service, is improved the shopping experience of user.
Description
Technical field
The invention belongs to mark study, big data processing technology field partially, it is particularly based on where probability propagation model user
Retail shop positions big data prediction.
Background technique
Label study is a kind of output space Weakly supervised study associated with one group of candidate's tag set, candidate label partially
Only having one in set is authentic signature, and remaining label is considered as interference noise label.During inclined label training, each
The true tag of training sample is submerged in candidate tag set, thus can not be similar to strong supervised learning it is such, directly from
The input space is obtained in data set to the learning algorithm for exporting space.However, in real life, believing with accurate unique tags
The data set of breath is more difficult to more obtain.Therefore we have in face of how from do not have unicity and definition data set middle school
The serious problem of habit.Recently, it marks study to provide many effective methods partially to go to solve problems, and widely uses
In many practical applications, especially there is very big breakthrough where user in retail shop's orientation problem.
With the rapid proliferation of internet mobile payment, we enjoy life brought by more and more intelligent positionings just
Benefit.Such as when client enters into certain dining room in market, the discount coupon in the mobile phone meeting automatic spring dining room；When client enters into market
When clothes shop, mobile phone can automatically recommend the clothes that you like in this family shop；When client passes by one jeweler's shop in market, mobile phone
It can think that a diamond ring for a long time is available in stock with automatic prompt client；When leaving market parking lot, mobile phone is permitted client's
Can under can hand over parking fee automatically.The intimate service that these clients are enjoyed all be unable to do without the excavation of behind big data and machine learning
Support.Retail shop's positioning analysis where client is while implicitly bringing the artificial intelligent body of client and testing, so that user is easier
Retail shop's information interested to oneself is understood, to improve the purchasing power of client indirectly.How in correct time, correct
Place give user most effective service, be big data era intelligence expand new challenge.
Summary of the invention
Present invention seek to address that the above problem of the prior art.One kind is proposed to allow users to obtain more accurately
Individualized Notification Service, retail shop's positioning big data prediction where improving the user based on inclined label study of the shopping experience of user
Method.Technical scheme is as follows:
A kind of user place retail shop positioning big data prediction technique based on inclined label study comprising following steps:
101. the position behavioral data of couple user carries out including in exceptional sample cleans, missing Wi-Fi information is filled in
Pretreatment operation；
102. being closed according to candidate quotient paved set corresponding to each sample, each sample in data set is some user couple
A kind of shopping status answered, the different shopping status of each user correspond to different candidate quotient paved sets and close, the time of each sample
Select retail shop's set according to certain Rule, for each sample, this rule may be summarized to be three steps: 1, according to distance
Find 10 retail shops nearest from this user's current shopping status；2, according to the convex quadratic programming problem of one innovation of optimization, come
This 10 shops are solved for the importance of this user's current shopping status；3, according to importance, importance is selected to be greater than threshold value
0.4 retail shop closes as candidate quotient paved set, constructs inclined flag data collection；
103. a pair flag data collection partially carries out feature extraction operation, feature group of the Wi-Fi apart from strength characteristic vector is extracted
At feature space, this feature vector is similar to ONE-HOT feature vector, and feature vector is represented as what data set occurred per one-dimensional
Each Wi-Fi under user's current shopping status apart from intensity value；
104. constructing similarity graph according to feature space, specifically include:
For each of data set sample xi, repeatedly do identical operation: 1, by xiAs the one of similarity graph
A node；2, by xiRegard central point as, according to xiWi-Fi is apart from strength characteristic vector between other samples in data set
Euclidean distance is xiThe smallest 10 samples of Euclidean distance are chosen, then according to xiWith this 10 samples of selection, xiIt can see
Work is the central sample point of this 10 samples, and the node that it is corresponding in figure is connected with side in similar diagram；
105. carrying out probability propagation according to similarity graph；For each of data set sample xi, repeatedly do phase
Same operation: 1, it initializes: optimized parameter is calculated according to likelihood function (formula (6)), to calculate xiCorresponding candidate
The probability that the candidate retail shop of each of retail shop's set may interact, using this probability distribution as xiCorresponding candidate quotient paved set is closed
Each of candidate retail shop initialization probability distribution；2, for the t times iteration of probability propagation algorithm: according to based on similar diagram
Formula obtain the t times iteration xiThe probability distribution of corresponding candidate retail shop realizes the probability propagation of the t times iteration, meter
The process for calculating this formula is exactly the process of a probability propagation, this communication process can only be realized in similar diagram corresponding to each edge
Two nodes between propagation, due to may result in not during propagation in xiWhat corresponding candidate quotient paved set was closed
The interaction probability of retail shop is not 0, therefore will be to all retail shops relative to xiInteraction probability carry out disambiguation normalization, a, for non-
The interaction probability for the retail shop that candidate quotient paved set is closed is set to 0；B, the interaction probability for the retail shop closed for candidate quotient paved set carries out maximum
Minimum normalization.
106. pre- from the conjunction of the candidate quotient paved set of inclined flag data collection by the convergent probability of step 105 probability propagation institute
Measure the retail shop that user will have behavior to interact in future.
Further, the step 101 carries out pretreatment operation specific steps to the shopping status data of user are as follows:
The cleaning of 1011. exceptional samples: the cleaning of exceptional sample passes through the longitude and latitude in original data set and currently shopping first
The Wi-Fi strength information of state, according to formula
The abnormal confidence level of each sample is calculated, wherein λi,τiThe longitude of user corresponding to respectively i-th of sample,
The Wi-Fi intensity of latitude and current state, m indicates data set sample size, if the abnormal confidence level c of certain sampleiLower than 0.15
Or be higher than 0.85, then the sample is determined as exceptional sample, and it is filtered away from original data set；
1012. missing Wi-Fi information filling: find first from Wi-Fi strength information missing sample longitude and latitude the most
Similar 10 samples, and this corresponding Wi-Fi strength information of 10 samples is Given information, the phase between two sample
Like property according to formula
It is calculated, wherein λa,λb,User corresponding to the longitude and latitude and sample b of user corresponding to respectively sample a
Longitude and latitude,The respectively variance of longitude and latitude in entire data set, then by this 10 samples according to public affairs
Formula
It goes to fill the Wi-Fi strength information that the sample lacks, wherein sample a is sample to be filled, ai(i=1,2 ...,
10) 10 neighbour's samples for being sample a,For sample aiCorresponding Wi-Fi strength information.
Further, the step 102 candidate quotient paved set according to corresponding to each sample, which is closed, constructs inclined flag data collection
Specific steps are as follows:
For each sample in former data, following operation is repeated to construct inclined flag data collection: (1) according to former number
According to user's longitude and latitude and retail shop's longitude and latitude is concentrated, the distance between sample and each retail shop are calculatedWherein λA,Respectively
Indicate the longitude and latitude of shop A, λa,Respectively indicate sample a longitude and latitude；(2) according to calculated distance d, selection and sample
10 nearest retail shops of distance；(3) longitude and latitude of 10 nearest retail shops of the distance according to corresponding to this sample, to following secondary
Planning equation optimizes:
10 retail shops corresponding to the sample are solved with respect to the weighted value of this sample, wherein λa,Respectively indicate sample a institute
Corresponding user's longitude and latitude, ωa,iThe retail shop i that (i=1,2 ..., 10) is respectively indicated in 10 nearest retail shops of distance sample a is opposite
In the weighted value of sample a,The longitude and latitude of 10 retail shops nearest corresponding to sample a is respectively indicated,
If weight corresponding to the retail shop calculated is greater than 0.4, which is added in the candidate quotient paved set conjunction of the sample.
Further, the step 103 carries out feature extraction operation to inclined flag data collection, specifically includes step:
Wi-Fi is apart from intensity: turning to 1000 dimensional feature vectors for Wi-Fi title is discrete first, characteristic value is Wi-Fi
Corresponding Wi-Fi intensity, then according to conversion formula:
The Wi-Fi strength characteristic vector of discretization is converted in order to which Wi-Fi is apart from strength characteristic vector, whereinIt is i-th
1000 dimension Wi-Fi of a sample apart from strength characteristic vector,Wi- corresponding to 1000 dimensions Wi-Fi for i-th of sample
Fi strength characteristic vector, | Yi| it is the size that the corresponding candidate quotient paved set of i-th of sample is closed,Respectively
Indicate the corresponding candidate retail shop A of the samplejLongitude and latitude, λa,It respectively indicates the sample and corresponds to user's longitude and latitude.
Further, the step 104 constructs the specific steps of similarity graph according to feature space are as follows:
In order to construct similarity graph < V based on feature space, E, ωe>, need to define node V, the phase of similar diagram respectively
Like the while weights omega in E and similar diagram of figuree；
The definition of the node of 1041. similar diagrams: each of inclined flag data collection sample is considered as in similarity graph
Node；
The definition on the side of 1042. similar diagrams: for every in each of inclined flag data collection sample, that is, similarity graph
One node, 10 samples in addition to itself for selecting Wi-Fi therewith nearest apart from intensity Euclidean distance as affiliated partner,
Two o'clock corresponding in similar diagram is attached, the side as similar diagram；
The definition of the side right weight of 1043. similar diagrams: the side according to the similar (a, b) in formula (2) as similar diagram
The weight of (a, b), wherein a, b are respectively two samples that two nodes are corresponding in inclined flag data concentration in similar diagram.
Further, the step 105 carries out probability propagation, specific steps according to similarity graph are as follows:
1051. initialization probabilities: for each sample, assume initially that retail shop appears in the probability in the conjunction of its candidate quotient paved set
For the ratio that the retail shop in entire data set occurs, i.e., the probability that retail shop occurs in data set is appeared in into the sample as retail shop
This candidate quotient paved set closes the priori knowledge of probability, and assumes to wait under conditions of the Wi-Fi of i-th of sample occurs apart from intensity
Retail shop in selected works is the distribution of this base of a fruit of the probability logic of obligation of true tag, then according to existing inclined flag data collection, construction
Likelihood function out:
Wherein p (y ∈ Si|xi, θ) and it is the true tag under conditions of Wi-Fi of i-th of sample occurs apart from intensity vector
The probability being present in the candidate quotient paved set conjunction of the sample, nyIndicate the number that retail shop y occurs in entire data set, πi,yFor
Retail shop y appear in its candidate quotient paved set close in probability, p (y | xi, θ) and it is to go out in the Wi-Fi of i-th of sample apart from intensity vector
Under conditions of existing, retail shop y is the probability of true tag, this likelihood function has formalized each of entire data set sample
True tag be all present in candidate quotient paved set and close this known facts, and parameter value θ can be carried out with Maximum-likelihood estimation
It estimates, whereinAs in the Wi-Fi of this sample apart from strength characteristic vector
Under conditions of appearance, retail shop y it is following will the user corresponding to this sample the probability for interacting behavior, will be as general
The initialization probability that rate is propagated；
The propagation of 1052. probability: in the t wheel iteration of probability propagation, according to the probability matrix F of last round of iterationt-1With
Initialization probability matrix P=[p (yi=j | xi,θ)]m×q, the new probability by field sample propagation effect of a wheel can be obtained
Matrix Ft:
Wherein W ∈ Rm×mSimilarity matrix between sample and sample, probability propagation have the wheel of iteration 50 altogether, pass in probability
In each round broadcast, the interaction probability of retail shop corresponding to each sample is broadcast to corresponding to it according to the similarity between sample
Neighbour's example, each sample interact probability according to retail shop corresponding to its 10 neighbour's samples to update oneself to the mutual of this retail shop
Dynamic probability.
Further, in label problem concerning study partially, each round iteration needs to disambiguate updated probability matrix
Retail shop's interaction probability in each sample non-candidate retail shop set is set to 0 by operation, mutual to the retail shop in the conjunction of candidate quotient paved set
Dynamic probability is normalized:
Further, the step 106 is closed by propagating a convergent probability from the candidate quotient paved set of inclined flag data collection
In predict user have in future behavior interact retail shop specific steps are as follows:
The probability matrix F obtained according to convergence is propagated in 105 stepst, can be obtained each sample and correspond to user most has
The prediction retail shop that may be interacted:
It advantages of the present invention and has the beneficial effect that:
1, retail shop's positioning applies itself, and most common prediction technique is basic more sorting machine learning methods, and more points
Class method can consume a large amount of resource, and the possible label of each sample should be the subset of all labels, i.e., each sample
True tag possibly only be present in certain several label, rather than more classification methods all labels are regarded as it is possible true
Label, the precision that will lead to more classification methods in this way is insufficient.Therefore this patent has innovatively regarded retail shop's positioning application partially as
Label learning method predicted, can make full use of the label information of that several retail shop of the only possible interaction of each sample into
Row prediction, greatly improves the precision of model；
2, in exceptional sample cleaning step, it is contemplated that the fact that sample standard deviation in data set is in the same commercial circle,
This patent innovatively creates relevant different to the Wi-Fi intensity of the longitude and latitude of user corresponding to sample and current shopping status
Normal confidence level will deviate from the sample that the average confidence level in data set is too high or itself confidence level is too low and clear out.
3, more similar with according to corresponding user's longitude and latitude between different samples in retail shop's positioning application, the shopping locating for them
State just should more similar principle, this patent innovatively creates the similarity formula based on this principle, to indicate different
Similarity degree between sample, there are two effects in this patent for this similarity: (1) according to the sample with Wi-Fi loss of learning
10 minimum samples of similarity remove the missing information of the sample of filling Wi-Fi loss of learning；(2) similarity can be used as phase
Like in figure, side right is great small between sample.
4, during constructing inclined flag data collection, conventional building method corresponds to user only by the sample is found
10 nearest retail shops of distance, however excessive noise figure can be brought to inclined flag data collection in this way, therefore we also need
10 retail shops for adjusting the distance nearest are screened, this patent innovatively create with corresponding to retail shop's longitude and latitude and sample
The relevant quadratic programming equation of the longitude and latitude of user, interaction weight of this quadratic programming equation by each retail shop relative to the sample
As variable is solved, according to optimal solution variable corresponding to optimization quadratic programming equation, it will be able to filter out as much as possible
User's current shopping status relative distance (relative to other 9 shops) immediate retail shop, can substantially reduce inclined reference numerals
According to the too big brought noise figure of the candidate tally set size of collection.
5, during feature extraction operation, this patent caught in retail shop's positioning application user corresponding to each sample with
Candidate quotient paved set close in each retail shop's distance average value, can by each sample candidate retail shop and non-candidate retail shop very
The characteristic distinguished well, and simultaneously in view of average distance can not distinguish asking for the retail shop in the conjunction of candidate quotient paved set well
Topic, Wi-Fi intensity corresponding to each sample is combined with average distance, innovatively proposes Wi-Fi apart from intensity
Vector characteristics ensure that the area between the retail shop in the conjunction of candidate quotient paved set while distinguishing candidate retail shop and non-candidate retail shop
Indexing.
6, during probability propagation, classical label propagation algorithm is transformed by this patent.Classical label passes
It broadcasts algorithm to only account for the appearance of candidate retail shop and do not occur this case shell, and does not consider the potential general of candidate quotient paved set conjunction
Rate distribution, therefore classical label propagation algorithm is unable to reach satisfactory expressive force, this patent is utilized label and propagates calculation
The framework of method, the probability propagation algorithm that this patent proposes on this basis are estimated according to the maximum likelihood that this base of a fruit of logic-based is distributed
Meter goes to excavate the probability distribution of the conjunction of candidate quotient paved set corresponding to each sample, is then put into the probability distribution that estimation obtains
In the frame of label propagation algorithm, and innovatively propose disambiguate normalization (a, for non-candidate retail shop set retail shop it is mutual
Dynamic probability is set to 0；B, the interaction probability for the retail shop closed for candidate quotient paved set carries out minimax normalization) it optimizes and is propagated through
Not the problem of Cheng Zhong non-candidate retail shop probability is not 0.Essentially, probability propagation algorithm solve label propagation algorithm can only be in number
The shortcomings that carrying out data mining according to surface layer substantially increases the prediction result of label study partially.
Detailed description of the invention
Fig. 1 is that retail shop's positioning big data where the present invention provides a kind of user based on inclined label study of preferred embodiment is pre-
The flow chart of survey method.
It is pre- that retail shop where Fig. 2 provides a kind of user based on inclined label study of preferred embodiment for the present invention positions big data
Sample Similarity figure in survey method.
It is pre- that retail shop where Fig. 3 provides a kind of user based on inclined label study of preferred embodiment for the present invention positions big data
Learning model practical application general frame figure is marked in survey method partially.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, detailed
Carefully describe.Described embodiment is only a part of the embodiments of the present invention.
The technical solution that the present invention solves above-mentioned technical problem is:
With reference to Fig. 1, Fig. 1 is that retail shop's positioning where the embodiment of the present invention one provides a kind of user based on inclined label study is big
The flow chart of data predication method, specifically includes:
101. the shopping status data of couple user carry out pretreatment operation, specific as follows: 1011. exceptional samples cleaning: different
The cleaning of normal sample passes through the Wi-Fi intensity letter of the longitude and latitude of user corresponding to sample in original data set and current state first
Breath calculates the abnormal confidence level of each sample according to formula (1), if the abnormal confidence level c of certain sampleiLower than 0.15 or it is higher than
0.85, then the sample is determined as exceptional sample by us, and it is filtered away from original data set；1012. missing Wi-Fi letters
The filling of breath: due to the factor of force majeure, the Wi-Fi strength information of certain samples can not be obtained accurately, according to longitude and latitude
Similar sample, Wi-Fi strength information also answer similar thought, and the sample institute found first from Wi-Fi strength information missing is right
Using the most similar 10 samples of family longitude and latitude, and this corresponding Wi-Fi strength information of 10 samples is Given information,
Similitude between two samples is calculated according to formula (2), then goes filling should according to formula (3) by this 10 samples
The Wi-Fi strength information of sample missing.
Inclined flag data collection is constructed 102. closing according to candidate quotient paved set corresponding to each user, it is specific as follows: for original
Each sample in data repeats following operation to construct inclined flag data collection: (1) according to user's longitude and latitude in original data set
Degree and retail shop's longitude and latitude calculate the distance between sample and each retail shop(wherein λA,Respectively
Indicate the longitude and latitude of shop A, λa,Respectively indicate user a longitude and latitude)；(2) with according to calculated distance d, selection and sample
10 nearest retail shops of distance；(3) longitude and latitude of 10 nearest retail shops of the distance according to corresponding to this sample, to quadratic programming
Equation (formula (4)) optimizes, and weighted value of 10 retail shops corresponding to the sample with respect to this sample is solved, if calculating
Retail shop corresponding to weight be greater than 0.4, then by the retail shop be added to the sample candidate quotient paved set close in.
103. carrying out feature extraction operation, tool to inclined flag data collection according to user's longitude and latitude and Wi-Fi strength information
Body is as follows: turning to 1000 dimensional feature vectors for Wi-Fi title is discrete first, characteristic value is Wi-Fi corresponding to Wi-Fi strong
Degree, then according to conversion formula (5) by the conversion of the Wi-Fi strength characteristic vector of discretization in order to Wi-Fi apart from strength characteristic to
Amount.
104. according to feature space construct similarity graph, it is specific as follows: in order to construct the similarity graph based on feature space <
V,E,ωe> (see Fig. 2) needs to define the node V of similar diagram, the while weights omega in E and similar diagram of similar diagram respectivelye。
The definition of the node of 1041. similar diagrams: each of inclined flag data collection sample is considered as in similarity graph
Node.
The definition on the side of 1042. similar diagrams: (each in similarity graph for each of inclined flag data collection sample
A node), select 10 samples (node) in addition to itself that Wi-Fi is nearest apart from intensity Euclidean distance therewith as association
Two o'clock corresponding in similar diagram is attached, the side as similar diagram by object.
The definition of the side right weight of 1043. similar diagrams: the side according to the similar (a, b) in formula (2) as similar diagram
The weight of (a, b), wherein a, b are respectively two samples that two nodes are corresponding in inclined flag data concentration in similar diagram.
105. probability propagation is carried out according to similarity graph, specific as follows:
1051. initialization probabilities: for each sample, assume initially that retail shop appears in the probability in the conjunction of its candidate quotient paved set
For the ratio that the retail shop in entire data set occurs, i.e., the probability that retail shop occurs in data set is appeared in into the sample as retail shop
This candidate quotient paved set closes the priori knowledge of probability, and it is further assumed that the condition occurred in the Wi-Fi of i-th of sample apart from intensity
Under, retail shop in Candidate Set is the distribution of this base of a fruit of the probability logic of obligation of true tag, then according to existing inclined flag data collection,
Having constructed likelihood function is formula (6), and a likelihood function has formalized the true mark of each of entire data set sample
Label are all present in candidate quotient paved set and close this known facts.And parameter value θ can be estimated with Maximum-likelihood estimation, whereinThe item as occurred in the Wi-Fi of this sample apart from strength characteristic vector
Under part, retail shop y it is following will the user corresponding to this sample the probability for interacting behavior, will be as probability propagation
Initialization probability.
The propagation of 1052. probability: in the t wheel iteration of probability propagation, according to the probability matrix F of last round of iterationt-1With
Initialization probability matrix P=[p (yi=j | xi,θ)]m×q, the new probability by field sample propagation effect of a wheel can be obtained
Matrix FtFor formula (7), probability propagation has the wheel of iteration 50 altogether.In each round of probability propagation, quotient corresponding to each sample
Paving interaction probability is broadcast to neighbour's example corresponding to it according to the similarity between sample, and each sample is according to its 10 neighbours
Retail shop corresponding to sample interacts probability to update oneself interaction probability to this retail shop.It is each in label problem concerning study partially
Wheel iteration needs to carry out updated probability matrix disambiguation operation, i.e., the retail shop in each sample non-candidate retail shop set is mutual
Dynamic probability is set to 0, and operation is normalized as shown in formula (8) to retail shop's interaction probability in the conjunction of candidate quotient paved set.
106. the convergent probability by propagating predicts user's future from the conjunction of the candidate quotient paved set of inclined flag data collection
The retail shop for thering is behavior to interact, it is specific as follows: to restrain obtained probability matrix F according to propagating in 105 stepst, can be obtained every
It is shown in formula (9) that a sample, which corresponds to the prediction retail shop that user most possibly interacts,.Made based on the probability propagation method marked partially
User can obtain more accurately Individualized Notification Service, improve the shopping experience of user, becoming solution, nowadays label obtains
Take the approach that can be effectively predicted under conditions of difficulty.Inclined label learning model based on big data retail shop where user positions
The general frame figure of practical application see Fig. 3.
The above embodiment is interpreted as being merely to illustrate the present invention rather than limit the scope of the invention.?
After the content for having read record of the invention, technical staff can be made various changes or modifications the present invention, these equivalent changes
Change and modification equally falls into the scope of the claims in the present invention.
Claims (8)
1. retail shop where a kind of user based on inclined label study positions big data prediction technique, which is characterized in that including following
Step:
101. the position behavioral data of couple user carries out including that exceptional sample cleans, missing Wi-Fi information is filled in interior pre- place
Reason operation；
102. being closed according to candidate quotient paved set corresponding to each sample, each sample in data set is that some user is corresponding
A kind of shopping status, the different shopping status of each user correspond to different candidate quotient paved sets and close, the candidate quotient of each sample
Paved set is closed according to certain Rule, and for each sample, this rule may be summarized to be three steps: 1, being found according to distance
10 retail shops nearest from this user's current shopping status；2, according to the convex quadratic programming problem of one innovation of optimization, to solve
Importance of this 10 shops for this user's current shopping status；3, according to importance, importance is selected to be greater than threshold value 0.4
Retail shop closes as candidate quotient paved set, constructs inclined flag data collection；
103. a pair flag data collection partially carries out feature extraction operation, extracts feature of the Wi-Fi apart from strength characteristic vector and form spy
Space is levied, this feature vector is similar to ONE-HOT feature vector, and feature vector is represented as each of data set appearance per one-dimensional
Kind of Wi-Fi under user's current shopping status apart from intensity value；
104. constructing similarity graph according to feature space, specifically include:
For each of data set sample xi, repeatedly do identical operation: 1, by xiAs a knot of similarity graph
Point；2, by xiRegard central point as, according to xiEuclidean of the Wi-Fi apart from strength characteristic vector between other samples in data set
Distance is xiThe smallest 10 samples of Euclidean distance are chosen, then according to xiWith this 10 samples of selection, xiIt can be regarded as
The central sample point of this 10 samples, the node that it is corresponding in figure is connected with side in similar diagram；
105. carrying out probability propagation according to similarity graph；For each of data set sample xi, repeatedly it is identical behaviour
Make: 1, initializing: optimized parameter is calculated according to likelihood function (formula (6)), to calculate xiCorresponding candidate quotient paved set
The probability that each of conjunction candidate retail shop may interact, using this probability distribution as xiIt is every in corresponding candidate quotient paved set conjunction
The initialization probability distribution of a candidate retail shop；2, for the t times iteration of probability propagation algorithm: according to the formula based on similar diagram
Obtain the x of the t times iterationiThe probability distribution of corresponding candidate retail shop realizes the probability propagation of the t times iteration, calculates this
The process of formula is exactly the process of a probability propagation, this communication process can only be realized in similar diagram two corresponding to each edge
Propagation between node, due to may result in not during propagation in xiThe retail shop that corresponding candidate quotient paved set is closed
Interacting probability is not 0, therefore will be to all retail shops relative to xiInteraction probability carry out disambiguation normalization, a, for non-candidate quotient
The interaction probability for the retail shop that paved set is closed is set to 0；B, the interaction probability progress minimax for the retail shop closed for candidate quotient paved set is returned
One changes.
106. being predicted from the conjunction of the candidate quotient paved set of inclined flag data collection by the convergent probability of step 105 probability propagation institute
The retail shop that user will have behavior to interact in future.
2. retail shop where the user according to claim 1 based on inclined label study positions big data prediction technique, special
Sign is that the step 101 carries out pretreatment operation specific steps to the shopping status data of user are as follows:
The cleaning of 1011. exceptional samples: the cleaning of exceptional sample passes through the longitude and latitude and current shopping status in original data set first
Wi-Fi strength information, according to formula
The abnormal confidence level of each sample is calculated, wherein λi,τiThe longitude of user corresponding to respectively i-th of sample, latitude and
The Wi-Fi intensity of current state, m indicates data set sample size, if the abnormal confidence level c of certain sampleiIt is lower than 0.15 or high
In 0.85, then the sample is determined as exceptional sample, and it is filtered away from original data set；
The filling of 1012. missing Wi-Fi information: the sample longitude and latitude found first from Wi-Fi strength information missing is the most similar
10 samples, and this corresponding Wi-Fi strength information of 10 samples is Given information, the similitude between two sample
According to formula
It is calculated, wherein λa,λb,The warp of user corresponding to the longitude and latitude and sample b of user corresponding to respectively sample a
Latitude,The respectively variance of longitude and latitude in entire data set, then by this 10 samples according to formula
It goes to fill the Wi-Fi strength information that the sample lacks, wherein sample a is sample to be filled, ai(i=1,2 ..., 10) it is sample
10 neighbour's samples of this,For sample aiCorresponding Wi-Fi strength information.
3. retail shop where the user according to claim 2 based on inclined label study positions big data prediction technique, special
Sign is that the step 102 candidate quotient paved set according to corresponding to each sample closes the specific steps for constructing inclined flag data collection
Are as follows:
For each sample in former data, following operation is repeated to construct inclined flag data collection: (1) according to original data set
Middle user's longitude and latitude and retail shop's longitude and latitude calculate the distance between sample and each retail shopWherein λA,Respectively
Indicate the longitude and latitude of shop A, λa,Respectively indicate sample a longitude and latitude；(2) according to calculated distance d, selection and sample
10 nearest retail shops of distance；(3) longitude and latitude of 10 nearest retail shops of the distance according to corresponding to this sample, to following secondary
Planning equation optimizes:
10 retail shops corresponding to the sample are solved with respect to the weighted value of this sample, wherein λa,It respectively indicates corresponding to sample a
User's longitude and latitude, ωa,i(i=1,2 ..., 10) respectively indicates the retail shop i in 10 nearest retail shops of distance sample a relative to sample
The weighted value of this,The longitude and latitude of 10 retail shops nearest corresponding to sample a is respectively indicated, if meter
Weight corresponding to the retail shop calculated is greater than 0.4, then the retail shop is added in the candidate quotient paved set conjunction of the sample.
4. retail shop where the user according to claim 3 based on inclined label study positions big data prediction technique, special
Sign is that the step 103 carries out feature extraction operation to inclined flag data collection, specifically includes step:
Wi-Fi is apart from intensity: turning to 1000 dimensional feature vectors for Wi-Fi title is discrete first, characteristic value is that Wi-Fi institutes are right
The Wi-Fi intensity answered, then according to conversion formula:
The Wi-Fi strength characteristic vector of discretization is converted in order to which Wi-Fi is apart from strength characteristic vector, whereinFor i-th of sample
1000 dimension Wi-Fi apart from strength characteristic vector,Wi-Fi intensity corresponding to 1000 dimensions Wi-Fi for i-th of sample is special
Vector is levied, | Yi| it is the size that the corresponding candidate quotient paved set of i-th of sample is closed,Respectively indicate the sample
This correspondence candidate retail shop AjLongitude and latitude, λa,It respectively indicates the sample and corresponds to user's longitude and latitude.
5. retail shop where the user according to claim 4 based on inclined label study positions big data prediction technique, special
Sign is that the step 104 constructs the specific steps of similarity graph according to feature space are as follows:
In order to construct similarity graph < V based on feature space, E, ωe>, need to define the node V of similar diagram, similar diagram respectively
In the while weights omega of E and similar diagrame；
The definition of the node of 1041. similar diagrams: each of inclined flag data collection sample is considered as the node in similarity graph；
The definition on the side of 1042. similar diagrams: for each of each of inclined flag data collection sample, that is, similarity graph
Node selects 10 samples in addition to itself that Wi-Fi is nearest apart from intensity Euclidean distance therewith as affiliated partner, i.e., will
Corresponding two o'clock is attached in similar diagram, the side as similar diagram；
The definition of the side right weight of 1043. similar diagrams: the side (a, b) according to the similar (a, b) in formula (2) as similar diagram
Weight, wherein a, b be respectively in similar diagram two nodes two corresponding samples are concentrated in inclined flag data.
6. retail shop where the user according to claim 5 based on inclined label study positions big data prediction technique, special
Sign is that the step 105 carries out probability propagation, specific steps according to similarity graph are as follows:
1051. initialization probabilities: for each sample, it is whole for assuming initially that retail shop appears in the probability in the conjunction of its candidate quotient paved set
The probability that retail shop occurs in data set is appeared in the sample as retail shop and waited by the ratio that a data concentrate the retail shop to occur
The priori knowledge of retail shop's set probability is selected, and is assumed under conditions of the Wi-Fi of i-th of sample occurs apart from intensity, Candidate Set
In retail shop be this base of a fruit of the probability logic of obligation of true tag distribution, then according to existing inclined flag data collection, construct seemingly
Right function:
Wherein p (y ∈ Si|xi, θ) and it is under conditions of the Wi-Fi of i-th of sample occurs apart from intensity vector, true tag exists
Probability in the candidate quotient paved set of the sample is closed, nyIndicate the number that retail shop y occurs in entire data set, πi,yFor retail shop y
Appear in its candidate quotient paved set close in probability, p (y | xi, θ) and it is to occur in the Wi-Fi of i-th of sample apart from intensity vector
Under the conditions of, retail shop y is the probability of true tag, this likelihood function has formalized the true of each of entire data set sample
Real label is all present in candidate quotient paved set and closes this known facts, and parameter value θ can be estimated with Maximum-likelihood estimation,
WhereinAs occur in the Wi-Fi of this sample apart from strength characteristic vector
Under the conditions of, retail shop y it is following will the user corresponding to this sample the probability for interacting behavior, probability propagation will be used as
Initialization probability；
The propagation of 1052. probability: in the t wheel iteration of probability propagation, according to the probability matrix F of last round of iterationt-1With it is initial
Change probability matrix P=[p (yi=j | xi,θ)]m×q, the new probability matrix by field sample propagation effect of a wheel can be obtained
Ft:
Wherein W ∈ Rm×mSimilarity matrix between sample and sample, probability propagation has the wheel of iteration 50 altogether, in probability propagation
In each round, retail shop corresponding to each sample interacts probability and is broadcast to neighbour corresponding to it according to the similarity between sample
Example, each sample are general to the interaction of this retail shop to update oneself according to the interaction probability of retail shop corresponding to its 10 neighbour's samples
Rate.
7. retail shop where the user according to claim 6 based on inclined label study positions big data prediction technique, special
Sign is that in label problem concerning study partially, each round iteration needs to carry out disambiguation operation to updated probability matrix, i.e., will be every
Retail shop's interaction probability in a sample non-candidate retail shop set is set to 0, carries out to retail shop's interaction probability in the conjunction of candidate quotient paved set
Normalization:
8. retail shop where the user according to claim 6 or 7 based on inclined label study positions big data prediction technique,
It is characterized in that, the step 106 convergent probability by propagating is predicted from the conjunction of the candidate quotient paved set of inclined flag data collection
The specific steps for the retail shop that user will have behavior to interact in future are as follows:
The probability matrix F obtained according to convergence is propagated in 105 stepst, can be obtained each sample, to correspond to user most possibly mutual
Dynamic prediction retail shop:
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910313789.9A CN110060102B (en) | 2019-04-18 | 2019-04-18 | Bias label learning-based method for predicting positioning big data of shops where users are located |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910313789.9A CN110060102B (en) | 2019-04-18 | 2019-04-18 | Bias label learning-based method for predicting positioning big data of shops where users are located |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110060102A true CN110060102A (en) | 2019-07-26 |
| CN110060102B CN110060102B (en) | 2022-05-03 |
Family
ID=67319420
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910313789.9A Active CN110060102B (en) | 2019-04-18 | 2019-04-18 | Bias label learning-based method for predicting positioning big data of shops where users are located |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110060102B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111581467A (en) * | 2020-05-15 | 2020-08-25 | 北京交通大学 | Bias label learning method based on subspace representation and global disambiguation method |
| CN111581466A (en) * | 2020-05-15 | 2020-08-25 | 北京交通大学 | Multi-label learning method for characteristic information with noise |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8339316B1 (en) * | 2010-08-13 | 2012-12-25 | Google Inc. | Smart GPS use |
| CN105044662A (en) * | 2015-05-27 | 2015-11-11 | 南京邮电大学 | Fingerprint clustering multi-point joint indoor positioning method based on WIFI signal intensity |
| CN106525052A (en) * | 2016-12-14 | 2017-03-22 | 广东工业大学 | Graph model constrained indoor positioning method |
| CN107086935A (en) * | 2017-06-16 | 2017-08-22 | 重庆邮电大学 | Flow of the people distribution forecasting method based on WIFI AP |
| CN107845260A (en) * | 2017-10-26 | 2018-03-27 | 杭州东信北邮信息技术有限公司 | A kind of recognition methods of user's bus trip mode |
| US20180144209A1 (en) * | 2016-11-22 | 2018-05-24 | Lunit Inc. | Object recognition method and apparatus based on weakly supervised learning |
| CN108764292A (en) * | 2018-04-27 | 2018-11-06 | 北京大学 | Deep learning image object mapping based on Weakly supervised information and localization method |
| CN109089314A (en) * | 2018-09-30 | 2018-12-25 | 哈尔滨工业大学（深圳） | A kind of indoor orientation method of the wifi sequence assistant GPS based on proposed algorithm |
| CN109242552A (en) * | 2018-08-22 | 2019-01-18 | 重庆邮电大学 | A kind of retail shop's localization method based on big data |
-
2019
- 2019-04-18 CN CN201910313789.9A patent/CN110060102B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8339316B1 (en) * | 2010-08-13 | 2012-12-25 | Google Inc. | Smart GPS use |
| CN105044662A (en) * | 2015-05-27 | 2015-11-11 | 南京邮电大学 | Fingerprint clustering multi-point joint indoor positioning method based on WIFI signal intensity |
| US20180144209A1 (en) * | 2016-11-22 | 2018-05-24 | Lunit Inc. | Object recognition method and apparatus based on weakly supervised learning |
| CN106525052A (en) * | 2016-12-14 | 2017-03-22 | 广东工业大学 | Graph model constrained indoor positioning method |
| CN107086935A (en) * | 2017-06-16 | 2017-08-22 | 重庆邮电大学 | Flow of the people distribution forecasting method based on WIFI AP |
| CN107845260A (en) * | 2017-10-26 | 2018-03-27 | 杭州东信北邮信息技术有限公司 | A kind of recognition methods of user's bus trip mode |
| CN108764292A (en) * | 2018-04-27 | 2018-11-06 | 北京大学 | Deep learning image object mapping based on Weakly supervised information and localization method |
| CN109242552A (en) * | 2018-08-22 | 2019-01-18 | 重庆邮电大学 | A kind of retail shop's localization method based on big data |
| CN109089314A (en) * | 2018-09-30 | 2018-12-25 | 哈尔滨工业大学（深圳） | A kind of indoor orientation method of the wifi sequence assistant GPS based on proposed algorithm |
Non-Patent Citations (4)
| Title |
|---|
| RAMAZAN GOKBERK CINBIS,ETC: "Weakly Supervised Object Localization with Multi-Fold Multiple Instance Learning", 《IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE(VOLUME:39,ISSUE:1,JAN.1 2017)》 * |
| 张敏灵: "偏标记学习研究综述", 《数据采集与处理》 * |
| 杜成喜等: "基于XGBoost的用户定位与商铺推荐", 《无线互联科技》 * |
| 王进等: "基于MPI的近邻距离加权偏标记学习算法之并行实现", 《江苏大学学报(自然科学版)》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111581467A (en) * | 2020-05-15 | 2020-08-25 | 北京交通大学 | Bias label learning method based on subspace representation and global disambiguation method |
| CN111581466A (en) * | 2020-05-15 | 2020-08-25 | 北京交通大学 | Multi-label learning method for characteristic information with noise |
| CN111581466B (en) * | 2020-05-15 | 2024-02-27 | 北京交通大学 | Partial multi-mark learning method for characteristic information noise |
| CN111581467B (en) * | 2020-05-15 | 2024-04-02 | 北京交通大学 | Partial mark learning method based on subspace representation and global disambiguation method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110060102B (en) | 2022-05-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20230162071A1 (en) | Autonomous learning platform for novel feature discovery | |
| Shrivastava et al. | Failure prediction of Indian Banks using SMOTE, Lasso regression, bagging and boosting | |
| Liu et al. | Visualizing and exploring POI configurations of urban regions on POI-type semantic space | |
| CN110363282B (en) | Network node label active learning method and system based on graph convolution network | |
| Cheng et al. | A non-linear case-based reasoning approach for retrieval of similar cases and selection of target credits in LEED projects | |
| JP2009076042A (en) | Learning user's activity preference from gps trace and known nearby venue | |
| CN105532030A (en) | Apparatus, systems, and methods for analyzing movements of target entities | |
| CN107330734B (en) | Co-location mode and ontology-based business address selection method | |
| Majewska et al. | Cluster-mapping procedure for tourism regions based on geostatistics and fuzzy clustering: example of Polish districts | |
| CN110060102A (en) | Retail shop where user based on inclined label study positions big data prediction technique | |
| CN109993184A (en) | A kind of method and data fusion equipment of data fusion | |
| Rodrigues et al. | Automatic classification of points-of-interest for land-use analysis | |
| US11783436B2 (en) | Magellan: a context-aware itinerary recommendation system built only using card-transaction data | |
| CN113868537B (en) | Recommendation method based on multi-behavior session graph fusion | |
| Pramanik et al. | Deep learning based resource availability prediction for local mobile crowd computing | |
| Zhuang et al. | Integrating a deep forest algorithm with vector‐based cellular automata for urban land change simulation | |
| CN117217779A (en) | Training method and device of prediction model and information prediction method and device | |
| Hsieh et al. | Estimating potential customers anywhere and anytime based on location-based social networks | |
| Li et al. | Multi-modal representation learning for successive poi recommendation | |
| CN115935079A (en) | Graph collaborative filtering recommendation method based on clusters | |
| Manca et al. | Fuzzy analysis for modeling regional delineation and development: the case of the Sardinian Mining Geopark | |
| Bose | Data mining in tourism | |
| Tang et al. | Discovering urban functional zones from biased and sparse points of interests and sparse human activities | |
| Shin et al. | Hybrid model–based motion recognition for smartphone users | |
| Xiao et al. | A dynamic transfer ensemble model for customer churn prediction |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20230111 Address after: Room 801, 85 Kefeng Road, Huangpu District, Guangzhou City, Guangdong Province Patentee after: Yami Technology (Guangzhou) Co.,Ltd. Address before: 400065 Chongwen Road, Nanshan Street, Nanan District, Chongqing Patentee before: CHONGQING University OF POSTS AND TELECOMMUNICATIONS |
|
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20230804 Address after: Room 205-03, 2nd Floor, Building 2, No.1 and No.3, Qinglong Hutong A, Dongcheng District, Beijing, 100010 Patentee after: Beijing Xinsuo Consulting Co.,Ltd. Address before: Room 801, 85 Kefeng Road, Huangpu District, Guangzhou City, Guangdong Province Patentee before: Yami Technology (Guangzhou) Co.,Ltd. |