CN110176291A - A kind of health and fitness information recommended method based on deep learning - Google Patents
A kind of health and fitness information recommended method based on deep learning Download PDFInfo
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- CN110176291A CN110176291A CN201910322542.3A CN201910322542A CN110176291A CN 110176291 A CN110176291 A CN 110176291A CN 201910322542 A CN201910322542 A CN 201910322542A CN 110176291 A CN110176291 A CN 110176291A
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- 238000000034 method Methods 0.000 title claims abstract description 32
- 238000013135 deep learning Methods 0.000 title claims abstract description 28
- 230000036541 health Effects 0.000 title claims abstract description 15
- 235000013305 food Nutrition 0.000 claims abstract description 24
- 238000013528 artificial neural network Methods 0.000 claims description 12
- 206010027654 Allergic conditions Diseases 0.000 claims description 6
- GUBGYTABKSRVRQ-QKKXKWKRSA-N Lactose Natural products OC[C@H]1O[C@@H](O[C@H]2[C@H](O)[C@@H](O)C(O)O[C@@H]2CO)[C@H](O)[C@@H](O)[C@H]1O GUBGYTABKSRVRQ-QKKXKWKRSA-N 0.000 claims description 6
- 230000036772 blood pressure Effects 0.000 claims description 6
- 239000008101 lactose Substances 0.000 claims description 6
- 230000004297 night vision Effects 0.000 claims description 5
- 238000004458 analytical method Methods 0.000 abstract description 3
- 239000000463 material Substances 0.000 description 19
- 239000003814 drug Substances 0.000 description 8
- 239000011159 matrix material Substances 0.000 description 8
- 230000003993 interaction Effects 0.000 description 4
- 238000012549 training Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 230000004913 activation Effects 0.000 description 2
- 210000004556 brain Anatomy 0.000 description 2
- 238000000354 decomposition reaction Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 230000008707 rearrangement Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000003860 storage Methods 0.000 description 2
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000002156 mixing Methods 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000000513 principal component analysis Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
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- G06F16/9535—Search customisation based on user profiles and personalisation
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/60—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
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Abstract
The health and fitness information recommended method based on deep learning that the invention discloses a kind of, this method comprises: the collection of information and arrangement, the pretreatment of information, enter information into obtain different foods to deep learning network model, from model recommend recommending index, choosing and recommend the highest several food of index and recommend the highest exercise program of index as final recommendation food and recommend amount of exercise recommendation plan for index and exercise program.The present invention combines deep learning with proposed algorithm, the feature of user and for further analysis is extracted from the wearable device or various relevant devices of user, it is excavated from the data of magnanimity and makes reliable, effective recommendation using effective information for user, according to the health data of user, reasonable food is made for user and amount of exercise is recommended.
Description
Technical field
The present invention relates to deep learning and proposed algorithm technical fields, and in particular to a kind of health letter practised based on depth
Cease recommended method.
Background technique
Deep learning originating from neural network research is a kind of high level more abstract by the formation of combination low-level feature
Indicate attribute, algorithm to find data distribution formula character representation.The relatively common multilayer perceptron containing more hidden layers is exactly
A kind of deep learning structure.As a kind of based on the method for carrying out representative learning to data, then deep learning in machine learning
In, observation can be used various ways and be indicated.In general, can be easier using certain specific representation methods from example
Middle learning tasks.As the frontier in a machine learning, deep learning is intended to establish a simulation human brain and carries out analytics
The neural network of habit.It simulates the mechanism of human brain to explain data, and fruitful in certain field.
Proposed algorithm is then one of computer major by mathematics supposition user preference, provides the algorithm of suggestion.It calculates
The work that method is substantially carried out is conceived to the feature in user model in the information of interest demand and destination item object model
Information is matched, the primary dcreening operation calculated with relevant mathematical model, finds user's potentially interested recommendation items
Mesh, and by project recommendation to user.As a kind of intelligent individual info service, proposed algorithm can pass through certain intelligence
Generalization bounds realization is customized by targeted customized information.
A few days ago, as deep learning is increasingly becoming the hot spot of industry, both recommendation field is also just being thought deeply merge into each other can
Can, although still the problems such as cold start-up, Sparse row exist in recommendation field, but with the addition of deep learning, algorithm
The raising of energy great-leap-forward is not completely without possible.Time overhead is smaller, the higher proposed algorithm of accuracy just adding with deep learning
Enter and is in the industry cycle fermented at leisure.
Existing technology has a kind of Chinese medicine medicinal material based on deep neural network of South China Science & Engineering University, production and research institute, Guangzhou to push away
Recommend method, the specific steps are as follows:
1. the feature vector of computer acquisition human body tongue fur picture and corresponding Chinese medicine medicinal material digital code are as data set
Input；
2. the feature vector of pair tongue fur picture carries out dimension-reduction treatment using Principal Component Analysis Algorithm；
3. the digital code of pair traditional Chinese medicine material carries out embedded characterization processing；
4. learning the relationship between tongue fur picture and Chinese medicine medicinal material；
5. calculating tongue fur figure using Chinese medicine medicinal material proposed algorithm according to the relationship between tongue fur picture and Chinese medicine requirement
Degree of association score between piece and Chinese medicine medicinal material, and choose the high Chinese medicine medicinal material as recommendation of degree of association score.
The shortcomings that this method, is:
Traditional recommended method is limited to the collaborative filtering method of matrix decomposition, can only consider single score information.Separately
Outside, as shallow Model, traditional algorithm causes to prejudge result and reality is managed without calligraphy learning to deeper characteristic information
Thinking situation, there is biggish deviations.
Summary of the invention
The purpose of the present invention is overcoming the shortcomings of existing methods, proposes a kind of health and fitness information based on deep learning and push away
Recommend method.Traditional recommended method is limited to the collaborative filtering method of matrix decomposition, can only consider single score information.Separately
Outside, as shallow Model, traditional algorithm causes to prejudge result and reality is managed without calligraphy learning to deeper characteristic information
Thinking situation, there is biggish deviations.The present invention proposes a kind of mixing interaction models based on deep learning, utilizes deep learning
The model factor that more auxiliary informations are taken into consideration alleviates the excessively sparse problem of existing algorithm input data data, so
It goes to learn more abstract profound character representation using the nonlinear network structure of multilayer interaction afterwards；By to user and its
Profound information carries out the character representation result that multiple inner product interaction obtains more different levels；Finally it polymerize all knots
Fruit is predicted.
To solve the above-mentioned problems, the health and fitness information recommended method based on deep learning that the invention proposes a kind of, it is described
Method includes:
Weight, height, blood pressure, night vision feelings are obtained from smart machine (smartwatch, intelligent electronic-scale etc.) or user
Condition, taste preference, lactose tolerance situation, the information of special allergic conditions.
The user information being collected into is passed through into one-hot coding, obtains sparse vector, then obtain corresponding feature vector.
Using the sparse vector of acquisition, feature vector as the input vector of neural network, it is input in network.
The recommendation index of various different foods and various amounts of exercise is calculated using the proposed algorithm in deep learning network
(score), and according to preceding 5 food of size relation descending recommendation and first 3 amount of exercise plans for recommending index to user.
The health and fitness information recommended method based on deep learning that the invention proposes a kind of, this method by deep learning with push away
It recommends algorithm to combine, the feature of user is extracted from the wearable device or various relevant devices of user and makees further point
Analysis excavates from the data of magnanimity and makes reliable, effective recommendation using effective information for user, according to the strong of user
Health data make reasonable food for user and amount of exercise are recommended.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, below will to embodiment or
Attached drawing needed to be used in the description of the prior art is briefly described, it should be apparent that, the accompanying drawings in the following description is only
Some embodiments of the present invention, for those of ordinary skill in the art, without creative efforts, also
Other attached drawings can be obtained according to these attached drawings.
Fig. 1 is the health and fitness information recommended method flow diagram based on deep learning of the embodiment of the present invention；
Fig. 2 is the neural network configuration structure chart of the embodiment of the present invention.
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, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts it is all its
His embodiment, shall fall within the protection scope of the present invention.
Fig. 1 is the health and fitness information recommended method flow diagram based on deep learning of the embodiment of the present invention, such as Fig. 1 institute
Show, this method comprises:
S1 obtains weight, height, blood pressure, night from smart machine (smartwatch, intelligent electronic-scale etc.) or user
Optionally, the information of taste preference, lactose tolerance situation, special allergic conditions.
The user information being collected into is passed through one-hot coding, obtains sparse vector, then obtain corresponding feature vector by S2.
S3, the sparse vector that S2 is obtained, feature vector are input in network as the input vector of neural network.
S4 calculates the recommendation of various different foods and various amounts of exercise using the proposed algorithm in deep learning network
Index (score), and according to preceding 5 food of size relation descending recommendation and first 3 amount of exercise plans for recommending index to use
Family.
Step S4 contains training stage and test phase in the practice of entire proposed algorithm.It is specific as follows:
The neural network configuration being related to such as Fig. 2.
In neural network, the number of plies of hidden layer N depends on the sum of food materials type and amount of exercise type.Define hidden layer
It calculates as follows:
Z1=F1(W1x1)#(1)
Z2=F2(W2Z1+b2)#(2)
……
Zn=Fn(WnZn-1+bn)#(3)
N indicates the number of plies of neural network in hidden layer in formula (3).Define Zn,Fn,Wn,bnRespectively indicate the defeated of n-th layer
Be worth out, the activation primitive of n-th layer, the weight matrix of n-th layer and the bias vector of n-th layer, bias vector regard concrete condition and
It is fixed, enter no special case, defaults full 0.Activation primitive then selects tanh.
Interactive operation in each hidden layer calculates as follows:
Xu=Wuiu+Wuau′#(7)
Xv=Wuiv+Wvav′#(8)
pu1=F1(Wu1Xu)#(9)
qv1=F1(Wv1Xv)#(10)
……
pun=Fn(Wunpu(n-1)+bun)#(11)
qvn=Fn(Wunqv(n-1)+bvn)#(12)
Yn=pun⊙qvn#(13)
P is defined in formulaun、qvnThe output of n-th layer module respectively in hidden layer.Xu,XvRespectively indicate users personal data
With food materials, amount of exercise data by the latent variable after embeding layer.WuiAnd WuaIndicate users personal data and food materials, movement
Measure the weight matrix of data；WunIndicate the weight matrix of n-th layer in depth network training；⊙ indicates that corresponding element is multiplied；YnTable
Show the interaction results that corresponding n-layer learns.
Entire method should include training stage and test phase.
Wherein the step of training stage includes:
[1] obtained original from smart machine (smartwatch, intelligent electronic-scale etc.) or user weight, height,
Blood pressure, night vision situation, taste preference, lactose tolerance situation, special allergic conditions information input network, corresponding input is correct
Food materials and amount of exercise information.
[2] inverted order rearrangement is converted to latent variable after carrying out one-hot coding to information, then according to tandem shown in Fig. 2.
[3] n is set, n should be the sum of food materials Yu amount of exercise type.
[4] vector after series connection is inputted into neural network, by available n different scores of hidden layer, corresponded to
N kind food materials and amount of exercise.
[5] by obtained score compared with correct score, the mean square error L and gradient G of the two are calculated, L is defined such as
Under:
WhereinIndicate that the score matrix of prediction, Y indicate true score matrix.
[6] each weight of the weight matrix of hidden layer subtracts gradient G.
[7] above-mentioned [4] to [6] process is repeated, until the value Jing Guo enough iteration or loss function is no longer obvious
Until becoming smaller.
Test phase includes:
[1] obtained original from smart machine (smartwatch, intelligent electronic-scale etc.) or user weight, height,
Blood pressure, night vision situation, taste preference, lactose tolerance situation, special allergic conditions information input network.
[2] obtained original from smart machine (smartwatch, intelligent electronic-scale etc.) or user weight, height,
Blood pressure, night vision situation, taste preference, lactose tolerance situation, special allergic conditions information input network, corresponding input is correct
Food materials and amount of exercise information.
[3] inverted order rearrangement is converted to latent variable after carrying out one-hot coding to information, then according to tandem shown in Fig. 2.
[4] n is set, n should be the sum of food materials Yu amount of exercise type.
[5] vector after series connection is inputted into neural network, by hidden layer available n different scores,
Correspond to n kind food materials and amount of exercise.
[6] all scores for being under the jurisdiction of food materials are distinguished into descending with the score for being under the jurisdiction of amount of exercise.Choose highest in food materials
Point 5, in amount of exercise best result 3 (food materials and amount of exercise such as input are insufficient, then export corresponding export 1) conducts
Recommendation.
The embodiment of the present invention propose a kind of health and fitness information recommended method based on deep learning, by deep learning with push away
It recommends algorithm to combine, the feature of user is extracted from the wearable device or various relevant devices of user and makees further point
Analysis excavates from the data of magnanimity and makes reliable, effective recommendation using effective information for user, according to the strong of user
Health data make reasonable food for user and amount of exercise are recommended.
Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of above-described embodiment is can
It is completed with instructing relevant hardware by program, which can be stored in a computer readable storage medium, storage
Medium may include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random
Access Memory), disk or CD etc..
In addition, be provided for the embodiments of the invention above a kind of health and fitness information recommended method based on deep learning into
It has gone and has been discussed in detail, used herein a specific example illustrates the principle and implementation of the invention, the above implementation
The explanation of example is merely used to help understand method and its core concept of the invention；Meanwhile for the general technology people of this field
Member, according to the thought of the present invention, there will be changes in the specific implementation manner and application range, in conclusion this explanation
Book content should not be construed as limiting the invention.
Claims (1)
1. a kind of health and fitness information recommended method based on deep learning, which is characterized in that the described method includes:
Weight, height, blood pressure, night vision situation, mouth are obtained from smart machine (smartwatch, intelligent electronic-scale etc.) or user
Taste preference, lactose tolerance situation, the information of special allergic conditions.
The user information being collected into is passed through into one-hot coding, obtains sparse vector, then obtain corresponding feature vector.
Using the sparse vector of acquisition, feature vector as the input vector of neural network, it is input in network.
Recommendation index (of various different foods and various amounts of exercise is calculated using the proposed algorithm in deep learning network
Point), and according to preceding 5 food of size relation descending recommendation and first 3 amount of exercise plans for recommending index to user.
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| CN108182967A (en) * | 2017-12-14 | 2018-06-19 | 华南理工大学 | A kind of traditional Chinese medical science medicinal material based on deep neural network recommends method |
| CN108198603A (en) * | 2017-12-12 | 2018-06-22 | 昆明亿尚科技有限公司 | A kind of nutrition dietary based on personal physical examination information recommends method |
| CN108320187A (en) * | 2018-02-02 | 2018-07-24 | 合肥工业大学 | A kind of recommendation method based on depth social networks |
| WO2018201083A1 (en) * | 2017-04-28 | 2018-11-01 | University Of Southern California | System and method for predicting survival time |
| CN109074867A (en) * | 2016-04-25 | 2018-12-21 | 三星电子株式会社 | Summarize the system and method for improving healthy result with successive learning for providing |
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- 2019-04-19 CN CN201910322542.3A patent/CN110176291A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109074867A (en) * | 2016-04-25 | 2018-12-21 | 三星电子株式会社 | Summarize the system and method for improving healthy result with successive learning for providing |
| CN106920147A (en) * | 2017-02-28 | 2017-07-04 | 华中科技大学 | A kind of commodity intelligent recommendation method that word-based vector data drives |
| CN107092797A (en) * | 2017-04-26 | 2017-08-25 | 广东亿荣电子商务有限公司 | A kind of medicine proposed algorithm based on deep learning |
| WO2018201083A1 (en) * | 2017-04-28 | 2018-11-01 | University Of Southern California | System and method for predicting survival time |
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Application publication date: 20190827 |