CN108073284A - Purchase system based on brain wave identification mood - Google Patents
Purchase system based on brain wave identification mood Download PDFInfo
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Abstract
The invention discloses the purchase systems based on brain wave identification mood, including acquiring brain waves module and its shopping commending system, brain wave sensor is worn on user's head, shopping commending system is deployed in electric business website backstage, brain wave sensor is used to detect the brain wave of user, and is analyzed and processed, identifies mood, when recognizing specific mood, commending system of doing shopping obtains related data, to the accurate Recommendations of user；The present invention is triggered when user browses commodity, effectively saves the time, improves shopping efficiency.
Description
Technical field
The present invention relates to the shopping commending systems based on brain wave identification mood, belong to technical field of intelligence.
Background technology
With the gradual expansion of e-commerce scale, the type and quantity of online commodity also constantly increase, consumer's shopping
The leeway of selection also greatly expands.However, consumer, which generally requires to consume the substantial amounts of time, can just find suitable commodity.It is this
Great inconvenience can undoubtedly be brought to consumer by browsing the process of a large amount of irrelevant informations and commodity.
The content of the invention
The present invention provides a kind of purchase system, is triggered when user browses commodity, effectively saves the plenty of time, puies forward shopping effect
Rate.
The technical solution adopted by the present invention to solve the technical problems is：
Based on brain wave identification mood shopping commending system, including acquiring brain waves module and its shopping commending system,
Acquiring brain waves mould brain wave sensor in the block is worn on user's head, and shopping commending system is deployed in electric business website backstage.
Brain wave sensor is used to detect the brain wave of user, after gathering eeg signal, obtains analog voltage signal, is divided into two afterwards
Road, the size of current that a-road-through over-current sensor obtains are input in ARM microcontrollers, and another way is directly inputted to ARM monolithics
Machine after treatment, obtains β ripples, θ ripple phase power, the absolute power ratio of β ripples and θ ripples is obtained afterwards, by machine learning side
Method --- Gradient Propulsion tree (GBDT) establishes emotion inference rule, identification actively, tranquil, passive three classes mood, when recognizing
During specific emotional, shopping commending system is enabled, provides a user Recommendations.
As present invention further optimization, the acquiring brain waves module further includes brain wave sensor, electric current passes
Three sensor, ARM microcontrollers parts；
As present invention further optimization, the brain wave sensor is used to measure corticocerebral EEG signals,
There are δ ripples (1-3Hz), θ ripples (4-7Hz), α ripples (8-13Hz), β ripples (14-30Hz) respectively, measurement uses dry electrode probe,
After amplifying by signal, filtering, using ThinkGearAM brain wave processing chips, the analog signal of β ripples and θ ripples is obtained；
As present invention further optimization, the current sensor uses Hall sensor, for by circuit
In current value be converted into voltage value according to certain linear relationship, so as to be input in ARM microcontrollers, and then mono- by ARM
The size of current in circuit is calculated in piece machine；
As present invention further optimization, the ARM microcontrollers directly transmit for handling from brain wave sensor
Voltage signal and the voltage signal that is transmitted via current sensor, after treatment, obtain the absolute power ratio of β ripples and θ ripples.
Pass through machine learning method --- Gradient Propulsion tree (GBDT) establishes emotion inference rule, identification actively, tranquil, passive three classes
Mood transfers data to computer backstage finally by serial communication；
As present invention further optimization, the shopping commending system further includes database module, data mining mould
Four block, recommendation process module, user's grading module parts；
As present invention further optimization, the database module be used for record and preserve user transaction data,
Commodity data, user information data carry out data preparation for the data mining of next stage；
As present invention further optimization, the data-mining module is used for the information and reality in database module
When Transaction Information integrate in recommended project.Data mining by statistics, online analysis and processing, information retrieval, machine learning, specially
Family's all multi-methods such as system and pattern-recognition, analyze data, make the reasoning of inductive, increasingly automatedly from substantial amounts of data
In excavate the information of potentially useful.The module finds the shopping history record and commodity of user from the data of database module
Then relation between characteristic gives result to recommendation process module；
As present invention further optimization, the recommendation process module is used to perform hybrid proposed algorithm, with reference to
Mood performs corresponding algorithm, judges Recommendations project.The module simultaneously provide include user with like commodity item including
Recommendation list；
As present invention further optimization, user's grading module is obtaining commercial product recommending list for user
Afterwards, scoring to Recommendations often completes once to recommend with regard to once being scored, which recommends business by user in browsing
During product, by system detectio to brain wave evaluated to identify mood.In the present system, the mood that will identify that changes into fixed
The number evaluation of amount, definition actively divide for full marks 2, and calmness is 1 point, and passiveness is 0 point.User's rating matrix can be expressed as R (m
× n), m represents the quantity of user, and n represents the quantity of project, the nonzero element r in rating matrixij∈R.Each project fvj∈T
Content representation be VSM feature vectors fvj=(w1j,w2j…wbj…wni), wherein wbjRepresent b-th of keyword for project tj
Weight, the weight is with tf/idf mode computations.The higher feature of selecting frequency forms high dimensional feature vector space.Widrow-
Hoff learning algorithms decline training pattern using gradient and carry out user modeling.User uiThe s times project scoring risAfterwards, the user
Feature vector is expressed as uvis=(v1is,v2is,…,vbis,…,vnis), wherein vnisRepresent after the scoring of the s times project b-th it is special
Keyword is levied to describing user uiThe importance of interest.As user uiScore to the s+1 times project ri(s+1)Afterwards, the feature of the user
Vector is expressed as vui(s+1), while update user uiTo the feature weight of j projects：
Appraisal result is fed back to recommendation process module by final system.
Advantageous effect
When browsing commodity, system by the brain wave detected is analyzed and processed and identifies use the user of the present invention
The mood at family enables commending system according to the mood recognized, recommends possible ace-high commodity to user, system is recommended in this shopping
System is more accurate compared with conventional recommendation systems, the effective plenty of time for saving user.
Description of the drawings
Fig. 1 is the front view of the preferred embodiment of the present invention；
Fig. 2 is acquiring brain waves function structure chart；
Fig. 3 is Gradient Propulsion tree (GBDT)；
Fig. 4 is the mood of the preferred embodiment of the present invention and goods matching figure；
Fig. 5 is the system flow chart of the preferred embodiment of the present invention；
In figure：1 is brain wave sensor, and 2 be using shopping commending system in computer.
Specific embodiment
In conjunction with the accompanying drawings, the present invention is further explained in detail.These attached drawings are simplified schematic diagram, only with
Illustration illustrates the basic structure of the present invention, therefore it only shows composition related to the present invention.
As shown in Fig. 1 front views, a kind of purchase system including acquiring brain waves module and its shopping commending system, is used
The equipment equipped with brain wave sensor is worn at family, after gathering eeg signal, is obtained voltage signal, is divided into two-way afterwards, all the way
The size of current obtained by current sensor is input in ARM microcontrollers, and another way is directly inputted to ARM microcontrollers, obtains
β ripples and θ wave powers calculate the absolute power ratio for obtaining β ripples and θ ripples, pass through machine learning method --- Gradient Propulsion tree
(GBDT) establish emotion inference rule, identify related emotional, when recognizing specific mood, shopping commending system obtains phase
Data are closed, provide a user Recommendations；
As shown in Fig. 2 acquiring brain waves function structure charts, acquiring brain waves module includes brain wave sensor, current sense
Device, ARM microcontrollers；
As present invention further optimization, the brain wave sensor is used to measure corticocerebral EEG signals,
There are δ ripples (1-3Hz), θ ripples (4-7Hz), α ripples (8-13Hz), β ripples (14-30Hz) respectively, measurement uses dry electrode probe,
After amplifying by signal, filtering, using ThinkGearAM brain wave processing chips, the analog signal of β ripples and θ ripples is obtained；
As present invention further optimization, the current sensor uses Hall sensor, for by circuit
In current value be converted into voltage value according to certain linear relationship, so as to be input in ARM microcontrollers, and then mono- by ARM
The size of current in circuit is calculated in piece machine；
As present invention further optimization, the ARM microcontrollers directly transmit for handling from brain wave sensor
Voltage signal and the voltage signal that is transmitted via current sensor, after treatment, obtain the absolute power ratio of β ripples and θ ripples.
Pass through machine learning method --- Gradient Propulsion tree (GBDT) establishes emotion inference rule, identification actively, tranquil, passive three classes
Mood transfers data to computer backstage finally by serial communication；
Shown in the decision tree generated such as Fig. 3 Gradient Propulsions tree (GBDT), ARM microcontrollers pass through device learning method --- gradient
Tree (GBDT) is promoted to establish emotion inference rule, identification is positive, calmness, passive three classes mood.This inference rule represents such as
EEG signal of the fruit user in FT8 electrode pointsCharacteristic value is greater than or equal to 0.840105, and in FC6 electrode points
EEG signalCharacteristic value is greater than or equal to 40850.1, and in TP8 electrode pointsCharacteristic value is less than
0.858334, then the user be in positive emotion state.
As shown in Fig. 3 moods and goods matching figure, which is recommendation process mould mood in the block and goods matching figure.To logical
Cross three kinds of moods that brain wave identifies：Actively, it is tranquil, passive, different operations is carried out respectively.
Actively：Perform content-based recommendation algorithm：User's set expression is
U={ u1, u2..., ui..., um, project set is expressed as T={ t1, t2..., tj...tn}.With tf/idf
Weighting pattern, j-th of project is obtained for the weight of user i by formula below in system：
fji=freqji/maxtfi；
Wherein freqjiExpression project tjIn user uiThe number of middle appearance, maxtfiRepresent user uiMiddle all items occur
The maximum of number, idfiExpression project tjInverse document frequency.
Obtain user U's with Rocchio algorithms
Wherein IrWith InrThe project set that user u known to representing respectively likes and do not like；And β and γ is to conquer feedback
Weight, their value gives by system.Afterwards according to user'sCorresponding commodity is found in the database.It will feel with user
The similar commodity of interest are added in recommendation list；
It is tranquil：It performs the algorithm based on content and the hybrid proposed algorithm combined based on collaborative filtering algorithm：
With CCHR algorithms on the basis of modeling above, the similarity based on content between user is calculated.User uiWith user ujBetween
Similarity calculation form be
Wherein,Represent user uiThe average of each feature weight in feature vector.Due to the similarity calculation all in accordance with
Latest features vector in family carries out computing, feature weight v in institute's above formulamisAll it is abbreviated as vmi.User's rating matrix R (m × n), is commented
Nonzero element rij ∈ R in sub-matrix represent user uiTo project tjScore value.Set R (k) represents user ukDo not score
Project set, rkj' represent to user ukNon- scoring item tjThe prediction scoring of ∈ R (k), predicts arbitrary user ukDo not score
Project tjThe scoring of ∈ R (k):
Wherein,Represent target user ukThe scoring average of scoring item,Represent target user ukNeighbour user ui
Scoring item scoring average.It will finally predict that the high commodity item that scores adds in recommendation list.
It is passive：User does not have the commodity of browsing desire to purchase, no operation at this time.
As shown in Fig. 3 system flows, which is that the system flow chart user of device wears brain wave biography when browsing commodity
Sensor gathers eeg signal, by the identification of acquiring brain waves module actively, tranquil, passive three classes mood, then by dependency number
Computer backstage is transferred to according to by serial communication mode.Data-mining module in system shifts to an earlier date the information in integrated database,
The relation between the shopping history record of user and product features data is found, then gives result to recommendation process module.When
Identify user emotion for it is positive when, recommendation process module performs content-based recommendation algorithm, and similar commodity addition is pushed away
Recommend list；When identify user emotion for it is tranquil when, recommendation process module performs the algorithm based on content and based on cooperating
The hybrid proposed algorithm that filter algorithm combines will predict that the high commodity that score add in recommendation list；When identifying user's
When mood is passive, recommendation process module is without operation；Then the recommendation list that system obtains algorithm is pushed to user.User exists
It when obtaining recommendation list, while scores the commodity of recommendation, which, when browsing Recommendations, passes through system by user
The brain wave detected is evaluated to identify mood.After system obtains scoring, the product features weight in storehouse is updated the data, under
Once recommend to prepare.
As present invention further optimization, the shopping commending system further includes database module, data mining mould
Four block, recommendation process module, user's grading module parts；
As present invention further optimization, the database module be used for record and preserve user transaction data,
Commodity data, user information data carry out data preparation for the data mining of next stage；
As present invention further optimization, the data-mining module is used for the information and reality in database module
When Transaction Information integrate in recommended project.Data mining by statistics, online analysis and processing, information retrieval, machine learning, specially
Family's all multi-methods such as system and pattern-recognition, analyze data, make the reasoning of inductive, increasingly automatedly from substantial amounts of data
In excavate the information of potentially useful.The module finds the shopping history record and commodity of user from the data of database module
Then relation between characteristic gives result to recommendation process module；
As present invention further optimization, the recommendation process module is used to perform hybrid proposed algorithm, with reference to
Mood performs corresponding algorithm, judges Recommendations project.The module simultaneously provide include user with like commodity item including
Recommendation list；
As present invention further optimization, user's grading module is obtaining commercial product recommending list for user
Afterwards, scoring to Recommendations often completes once to recommend with regard to once being scored, which recommends business by user in browsing
During product, by system detectio to brain wave evaluated to identify mood.In the present system, the mood that will identify that changes into fixed
The number evaluation of amount, definition actively divide for full marks 2, and calmness is 1 point, and passiveness is 0 point.User's rating matrix can be expressed as R (m
× n), m represents the quantity of user, and n represents the quantity of project, the nonzero element r in rating matrixij∈R.Each project fvj∈T
Content representation be VSM feature vectors fvj=(w1j,w2j…wbj…wni), wherein wbjRepresent b-th of keyword for project tj
Weight, the weight is with tf/idf mode computations.The higher feature of selecting frequency forms high dimensional feature vector space.Widrow-
Hoff learning algorithms decline training pattern using gradient and carry out user modeling.User uiThe s times project scoring risAfterwards, the user
Feature vector is expressed as uvis=(v1is,v2is,…,vbis,…,vnis), wherein vnisRepresent after the scoring of the s times project b-th it is special
Keyword is levied to describing user uiThe importance of interest.As user uiScore to the s+1 times project ri(s+1)Afterwards, the feature of the user
Vector is expressed as vui(s+1), while update user uiTo the feature weight of j projects：
Appraisal result is fed back to recommendation process module by final system.
Those skilled in the art of the present technique are appreciated that unless otherwise defined all terms used herein are (including technology art
Language and scientific terminology) there is the meaning identical with the general understanding of the those of ordinary skill in the application fields.Should also
Understand, those terms such as defined in the general dictionary, which should be understood that, to be had and the meaning in the context of the prior art
The consistent meaning of justice, and unless defined as here, will not be with idealizing or the meaning of overly formal be explained.
The meaning of "and/or" described herein refers to that the simultaneous situation of respective individualism or both is wrapped
Including including.
The meaning of " connection " described herein can be between component to be directly connected to can also pass through between component
Other components are indirectly connected with.
Using above-mentioned desirable embodiment according to the invention as enlightenment, by above-mentioned description, relevant staff is complete
Various changes and amendments can be carried out without departing from the scope of the technological thought of the present invention' entirely.The technology of this invention
Property scope is not limited to the content on specification, it is necessary to determine its technical scope according to right.
Claims (6)
1. the purchase system based on brain wave identification mood, it is characterised in that：Recommend including acquiring brain waves module and its shopping
System, user wear the equipment equipped with brain wave sensor, after gathering eeg signal, obtain voltage signal, are divided into two afterwards
Road, the size of current that a-road-through over-current sensor obtains are input in ARM microcontrollers, and another way is directly inputted to ARM monolithics
Machine obtains β ripples and θ wave powers, calculates the absolute power ratio for obtaining β ripples and θ ripples, passes through machine learning method --- Gradient Propulsion
GBDT is set to establish emotion inference rule, identifies related emotional, when recognizing specific mood, shopping commending system obtains phase
Data are closed, provide a user Recommendations.
2. the system as claimed in claim 1, which is characterized in that acquiring brain waves module includes brain wave sensor, electric current passes
Sensor, ARM microcontrollers.
3. system as claimed in claim 1 or 2, which is characterized in that the brain wave sensor is used to measure cerebral cortex
EEG signals, have δ ripple 1-3Hz, θ ripple 4-7Hz, α ripple 8-13Hz, β ripple 14-30Hz respectively, measurement is visited using dry electrode
Head after being amplified by signal, filtering, handles using ThinkGearAM brain waves processing chip, obtains the voltage of β ripples and θ ripples
Signal.
4. system as claimed in claim 1 or 2, which is characterized in that the current sensor uses Hall sensor,
For the current value in circuit to be converted into voltage value according to certain linear relationship, so as to be input in ARM microcontrollers, and then
The size of current in circuit is calculated by ARM microcontrollers；
The ARM microcontrollers are for handling the voltage signal that directly transmits from brain wave sensor and passed via current sensor
The current signal come, after treatment, obtains the absolute power ratio of β ripples and θ ripples；Pass through machine learning method --- Gradient Propulsion
GBDT is set to establish emotion inference rule, and identification is positive, tranquil, passiveness three classes mood, data is passed finally by serial communication
It is sent to computer backstage.
5. the system as claimed in claim 1, which is characterized in that the shopping commending system further includes database module, number
According to four excavation module, recommendation process module, user's grading module parts；
The database module for record and preserve user's eeg signal, the transaction data of user, commodity data, user
Information data carries out data preparation for the data mining of next stage；
The data-mining module is used for the information in database module and real-time deal information integration into recommended project；
Data mining passes through statistics, online analysis and processing, information retrieval, machine learning, expert system and mode identification method, automation
Data are analyzed on ground, make the reasoning of inductive, the information of potentially useful is excavated from data；The module is from database module
The relation between the shopping history record of user and product features data is found in data, then gives result to recommendation process mould
Block；
The recommendation process module runs corresponding algorithm for performing hybrid proposed algorithm, with reference to mood, judges to recommend
Commodity item；The module simultaneously provide include user with like commodity item including recommendation list；
User's grading module is used for user after commercial product recommending list is obtained, to scoring for Recommendations, per complete
Into once recommending just once to be scored, the scoring by user when browsing Recommendations, the brain wave that is arrived by system detectio
It is evaluated to identify mood.
6. the system as claimed in claim 1, which is characterized in that in user's grading module, the mood that will identify that changes into
Quantitative number evaluation, definition actively divide for full marks 2, and calmness is 1 point, and passiveness is 0 point；User's rating matrix can be expressed as R
(m × n), m represent the quantity of user, and n represents the quantity of project, the nonzero element r in rating matrixij∈R；Each project fvj
The content representation of ∈ T is VSM feature vectors fvj=(w1j,w2j…wbj…wni), wherein wbjRepresent b-th of keyword for project
tjWeight, the weight is with tf/idf mode computations；The higher feature of selecting frequency forms high dimensional feature vector space；
Widrow-Hoff learning algorithms decline training pattern using gradient and carry out user modeling；User uiThe s times project scoring risAfterwards,
The feature vector of the user is expressed as uvis=(v1is,v2is,…,vbis,…,vnis), wherein vnisAfter representing the s times project scoring
B-th of characteristic key words is to describing user uiThe importance of interest；As user uiScore to the s+1 times project ri(s+1)Afterwards, the use
The feature vector at family is expressed as vui(s+1), while update user uiTo the feature weight of j projects：
<mrow>
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Appraisal result is fed back to recommendation process module by final system.
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108932511A (en) * | 2018-10-09 | 2018-12-04 | 南京邮电大学 | A kind of shopping decision-making technique based on brain-machine interaction |
| CN109002531A (en) * | 2018-07-17 | 2018-12-14 | 泉州装备制造研究所 | A kind of video display recommender system and video display recommended method based on eeg data analysis |
| CN109815361A (en) * | 2019-01-29 | 2019-05-28 | 南京信息工程大学 | A kind of intelligent music recommender system based on E.E.G identification |
| CN110472395A (en) * | 2019-08-05 | 2019-11-19 | 武汉联影医疗科技有限公司 | Brain wave data processing method, device, equipment, medium and brain wave data processor |
| CN112188597A (en) * | 2018-07-25 | 2021-01-05 | Oppo广东移动通信有限公司 | Proximity-aware network creation method and related product |
| CN112333215A (en) * | 2021-01-06 | 2021-02-05 | 浙江育英职业技术学院 | Commodity recommendation method based on block chain system, storage medium and electronic equipment |
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| CN115064022A (en) * | 2022-03-25 | 2022-09-16 | 深圳尼古拉能源科技有限公司 | Enhanced perception experience platform and use method thereof |
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