CN104731846A - Individuation catering recommendation method and system based on multiple targets - Google Patents
Individuation catering recommendation method and system based on multiple targets Download PDFInfo
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Abstract
The invention relates to an individuation catering recommendation method and system based on multiple targets. The individuation catering recommendation method mainly includes the steps that essential information and behavior information (including diet records and browsing behaviors on webpages) of a user are collected and recorded into a database; a user model is built according to user information in the database, and individuation nutrition recipes are recommended; nutrition balanced diet recipes are generated through multi-target optimization catering models, the similarity between the recipes based on nutrient elements is calculated through a collaborative filtering recommendation model and an equivalence interchange model, various interchange recipe lists are generated from recipe bases, and the dietary structure is enriched; the nutrition balance of the recipes is detected, improvement measures are provided by comparing practical contents and recommended nutrient intakes of various nutrients in the generated recipes, and the designed recipes trend to be reasonable.
Description
Art
The present invention mainly adopts the personalized recommendation algorithm based on nutrient to be that all kinds of crowd precisely recommends oneself the delicious recipe of nutrition exclusive, belongs to the technical field of data mining aspect.
Background technology
Than before, people present nutrition dietary consciousness strengthen to some extent, but in the face of many various food, a lot of people is difficult to select suitable recipe to meet the real needs of oneself health, so just occur many can to the website of user's some of the recommendations in diet, help user can have a healthy physique.
Breakfast, lunch and dinner cuisines net: mainly contain several functional modules such as cuisines menu, food and drink cuisines, cuisines special topic, food materials encyclopaedia, cuisines menu describes a large amount of cuisines in various places in detail as the specific practice of Hebei dish, Qinghai dish etc.; Cuisines special topic then relates to various menu special topic, as breakfast, lunch, dinner recipe special topic etc., illustrates the concrete introduction of all kinds of recipe, comprises abundance of food displaying and their the way step of each class recipe.
China's cuisines net: being one provides the cuisines portal website of cuisines menu, cuisines information, health knowledge and kitchen treasured book for user, wherein covers the various cuisines ways of the eight big cuisines such as Hunan cuisine, Sichuan cuisine in recipe complete works; Health knowledge mainly provides the information of the aspects such as some diet general knowledges, dietetic contraindication, weight reducing beauty treatment; Kitchen treasured book then comprises the introduction of the several respects such as kitchen finishing, kitchen maintenance.
Cuisines are outstanding: be integrate menu, health diet knowledge, cook the cuisines network information service platform of skill, various places characteristic snack, ecommerce and light social element.It is mainly divided into several module such as menu complete works, healthy diet, cuisines menu, household shop, cuisines intelligent, discussion group.
The diet related web site more than enumerated all provide the user a large amount of cuisines messages and the popularization of knowledge of healthy diet aspect, enhance the consciousness of user's nutrition dietary, but for the user of Different age group, different labor intensity, different health, the diet program that they adopt is certainly different, and website is system, popular recommend some nutriments to user, and healthyly can not need make to recommend targetedly according to the hobby of user.In addition, collaborative filtering is in information filtering and infosystem, become just rapidly a technology be popular, it is a kind of main algorithm of Technologies of Recommendation System in E-Commerce, the online commodity of main recommendation (books, clothes etc.), Amazon, the enterprises such as Netflix have made outstanding achievement in the application of coordination technique.Recommendation in recipe is but seldom used to.
Based on multiobject personalized nutritional food and drink recommend method and the system demand mainly through the nutrient of different user, adopt collaborative filtering, for user recommends health delicious recipe really one's own, balanced in nutrition.
Summary of the invention
The technical matters that the present invention mainly solves is by analyzing dissimilar user (as healthy person, sufferer, overweight people, China's pregnant women and women breast-feeding their children and climacteric women etc.) nutrient demand amount precisely recommend the delicious recipe of the best nutritional of satisfied individual somagenic need, in addition, exploitation mobile phone app application, is convenient to user and pays close attention to record whenever and wherever possible and use.
Technical scheme
1) gather the behavioural information (being included in the behavior in the behavior and social network that recipe website is browsed) of user and be recorded in database;
2) user's taste model is set up: the preference information obtaining user according to the diet record of user in database;
3) in three meals in a day, often meal exchanges the generation of recipe table: adopt certain personalized recommendation algorithm, calculate based on nutrient recipe between similarity, all kinds of exchange recipe table is generated from recipe storehouse, as the cereal in breakfast exchanges recipe table, greengrocery exchanges class recipe table, and fruits exchange recipe table etc.;
4) generation of recommending recipes: to exchange recipe table from each class according to the content of a certain specific nutrition element and recipe is sorted, then in conjunction with the preference information generating recommendations list of user;
5) detecting the property balanced in nutrition of recipe: by more generating actual intake and the recommended intake of each nutrient in recipe, proposing innovative approach, making the more rational property of designed recipe.
6) exploitation of mobile phone app application: realize the link to related data by Mobile Server, response SmartClient program, main dependence CDMA 1x, EV-DO etc. are as data transfer mode, by secure link, the request of data on Client application server is pushed to Client handset end, makes user can select the healthy diet of relieved satisfaction whenever and wherever possible.
Beneficial effect
Can meet nutrient and the heat energy supply needs of different crowd, between each nutrient, ratio is suitable for, and food variation, can take into account again the eating habit of user, notes the taste of food, makes user finally reach the optimum efficiency of diet balance.
Accompanying drawing explanation
Fig. 1 shows based on the Organization Chart of multiobject personalized food and drink recommend method and system
Fig. 2 is data base administration figure
Embodiment
By reference to the accompanying drawings, be described in detail to based on multiobject personalized food and drink recommend method and system.
Fig. 1 is frame diagram of the present invention, primarily of user profile, user model, recipe recommendation model three part composition.
One, user profile
This model comprises user basic information, user's diet record and user and to be correlated with recipe web page browsing behavior.User basic information contains the information such as name, sex, body weight, labour intensity of user, can determine the theoretical intake of the nutrient of user with this.The diet situation of user's diet logging modle essential record user three meals in a day, as record party a subscriber breakfast has eaten soya-bean milk, sesame seed cake, has eaten noon such as dumpling etc.User's recipe web page browsing behavior of being correlated with mainly refers to that user is to the clicking rate of certain recipe webpage and the keyword query relevant to recipe.
Two, user model
This model comprises intake model and user's taste preference model two modules.Intake model is then obtain according to user profile nutrient recommended amounts of eating more than user, mainly by determine user have dinner every day standard energy quantity delivered, calculate user often eat three large main heat-producing element (comprising protein, fat, carbohydrates) desired contents, to calculate according to the diet record of user nutritive element content that user taken in, by required standard content with taken in content and do difference and finally try to achieve this four step of nutrient content of eating more than user and complete.User's taste preference model mainly adopts sorting technique (as Naive Bayes Classification Algorithm) to build according to personal like, cultural preference, healthy constraint and religious belief, the data analyzed come from diet record and the web page browsing behavior of user, can be obtained the taste preference of user by these data.Such as certain user often eats the many food of capsicum, and frequently browses the relevant recipe webpage of Sichuan cuisine, then can learn that the taste of this user is partially peppery.
Wherein in intake model, user's concrete calculation procedure of standard three nutritious elements intake of often eating is as follows:
1. energy
(1) baby
The total power consumption amount (TEE) of baby comprises the energy ezpenditure of basal metabolic rate (BMR), the special dynamic work of food, tissue growth building-up process and activity.
1) breast-feeding mode=0; Artificial feeding mode=1
TEE (kcal/d)=73.8+38.6 × age+40.4 × feeding patterns-35.4 × body weight
TEE (kcal/ (kgd))=60.1+2.6 × age+6.5 × feeding patterns
TEE (kcal/kgd) is calculated) according to above equation
2) the energy storage amount that baby's energy requirement=TEE+ body weight increases,
Baby's ability storage capacity is as table 1
Table 1 baby energy storage amount
3) add 5% correction to change the metabolizable energy of body (milk efficiency is about 92%, breast milk 96%) into by diet energy and make RNI (recommended intake).
Accordingly, China baby energy RNI is decided to be:
0 years old age ~: 397.7kJ/ (kgd) [95kcal/ (kgd) |
0.5 year old ~: 397.7kJ/ (kgd) [95kcal/ (kgd)]
Note: 1kcal=4.186kJ
(2) Children and teenager
1) basal metabolic rate (BMR) computing formula adopting the Schofield adopted in World Health Organization (WHO) WHO (World Health Organization) report (1985) to calculate calculates BMR (basic metabolism), as Chinese children and teen-age BMR reference value, as table 2.
The formula of table 2 BMR according to the weight
Note: body weight m, kg.
2) physical activity level (physical activity level, PAL) is calculated as table 3 according to the TEE of the actual measurement of international diet energy advisor group estimation.
The TEE that table 3 is surveyed estimates the PAL of children and youth
Adopt factorial approach method (factorial approach) to estimate the recommended intake of Children and teenager energy, namely BMR is multiplied by energy consumption or the requirement that physical activity level (PAL) calculates human body.
PLA＝TEE/BMR
3) 1-6 year, 7-10 year, 11-17 year add 3%, 1%, 2% of TEE respectively as the energy requirement grown.
(3) be grown up
1) according to Schofield formulae discovery BMR (basic metabolism), in table 2.According to result and the situation of the actual measurement of China and Asia some other country, the result that formula is calculated subtract 5% as Chinese 18 ~ 49 years old Adult Groups and 50-59 year presenium crowd BMR adjustment.
2) activity intensity of China resident is adjusted to three grades, in table 4 by Pyatyi.Adopt the recommended intake of factorial approach method estimation adult energy.
China's activities of adults horizontal mipmap advised by table 4
3) special, the pregnancy period additionally adds 837.2kJ/d [200kcal/d], and the RNI of wet nurse increases by 2.1 × 10 on the basis of original intake
2kJ/d [500kcal/d].
(4) the elderly
1) with Schofield formulae discovery BMR (see table 2), 5% is subtracted as the BMR reference value of China the elderly.
2) PAL: in table 5.
The estimation of table 5 the elderly PAL
3) the elderly's energy RNI=BMR × PAL.
2. Macronutrient:
Needed by human body energy is mainly derived from three large magnanimity energy-producing materials in food: carbohydrate, fat, protein.Carbohydrates is main energy sources, and be secondly fat, the Main Function of protein is not heat supply.The carbohydrates of general recommendations adult accounts for 55% ~ 65% of heat energy, and fat accounts for 20% ~ 30%, and protein accounts for 11% ~ 15%.Breakfast, lunch and dinner heat distributes by 2: 4: 4 or 3: 4: 3.
(1) protein
| Age/year | Recommended intake |
| Baby (0 ~) | 1.5～3g/(kg·d) |
| Children and teenager (1 ~) | 1.68g/(kg·d) |
| Adult (18 ~) | 1.16g/(kg·d) |
| First pregnancy period | Add 5g/d |
| Second pregnancy period | Add 15g/d |
| 3rd pregnancy period | Add 20g/d |
| Old man (60 ~) | 1.27g/ (kgd) or protein calories value account for 15% of total amount of heat |
The recommended intake (RNIs) of table 6 Chinese residents dietary protein
(2) lipid
| Age/year | Fat (adipose energy accounts for the number percent of gross energy)/% | Cholesterol/mg |
| 0～ | 45-50 | |
| 0.5～ | 35-40 | |
| 2～ | 30-35 | |
| 7～ | 25-30 | |
| 18～ | 20-30 | ＜300 |
Table 7 Chinese residents dietary fat is suitable for intake (AIs)
Note: every gram of fat is oxidized in vivo can produce power 37.66kJ, is equivalent to the energy of 9kcal.
(3) carbohydrates (carbohydrate);
According to the actual intake of current China dietary carbohydrate, with the proposed recommendations amount of FAO/WHO, except infant (< 2 years old), the suitable intake (AI) of carbohydrates should provide the 55%-65% of gross energy.
The carbohydrates of note: 1g can produce 16.7KJ (4kcal) heat energy.
Three, recipe recommendation model
This model is divided into multiple-objection optimization pantry model, collaborative filtering recommending model and exchange model of equal value three models.
Needed for people's health, according to the content of nutriment various in food, design one meal, one day or meals for more time, the nutrient ratio that human body is taken in is reasonable, to reach the object of balanced diet.Nutrition dietary decision-making belongs to a typical multi-objective optimization question.Optimization problem condition is as follows: initial conditions, customer information, comprises age, sex, height and body weight etc.; Constraint condition, the ingestion standard etc. of each nutrient; The distribution etc. of breakfast/Chinese meal/dinner.Solution procedure is exactly in these restrictions on the parameters, find the optimum combination satisfied condition.
Multiple-objection optimization pantry mainly with the final nutrient recommendations amount obtained in intake model for benchmark is for user's recommending recipes, the gross energy contained by the food namely recommended, protein, fat, carbohydrates will be consistent with corresponding nutrient standard intake.Optimize pantry and be mainly divided into two steps: the first step is the ratio of reasonable distribution breakfast, lunch and dinner Middle nutrition cellulose content, normally distributes according to the ratio of 2: 4: 4 or 3: 4: 3.Second step finds the optimum combination satisfied condition, and realizes balanced in nutrition.
Multiple objective function:
Min Z
1=| ∑
c ∈ Vx
ic-2400| (formula 1)
MinZ
2=| ∑
c ∈ Vx
jc-69.6| (formula 2)
MinZ
3=| ∑
c ∈ Vx
lc-22.5| (formula 3)
MinZ
4=| ∑
c ∈ Vx
kc-65.5| (formula 4)
Wherein, formula 1 is the least error of Energy intaking, the least error that formula 2 is taken in for protein, and formula 3 is the least error of fat intake, the least error that formula 4 is taken in for carbohydrates.
Collaborative filtering recommending model mainly carries out recipe recommendation by collaborative filtering.Need the preliminary work done to be that food nourishment composition table is converted into Nutritional Score of Food table, concrete conversion formula is:
Wherein 500g is as the unified weight of food being carried out to malnutrition rate, and the man being engaged in very light physical labor is called " reference man ".The food that following table represents-nutrient rating matrix, row represents food, and row represent each nutrient, and the numerical value in table represents the scoring of each nutrient in food:
| Food is numbered | Food name | Protein | Fat | Carbohydrates |
| 35 | Steamed bun (steaming, mark powder) | 5571 | 83 | 1677 |
| 31 | Thin pancake | 5429 | 58 | 2594 |
| 29 | Steamed twisted roll | 4571 | 83 | 1583 |
| 44 | Rice (steaming, long-grained nonglutinous rice) | 1786 | 17 | 889 |
| 59 | Sesame seed cake (sugar) | 5714 | 175 | 2177 |
| 69 | Milled congee | 1000 | 58 | 292 |
| 72 | Oil cake | 5643 | 1908 | 1403 |
| 74 | Deep-fried twisted dough sticks | 4929 | 1467 | 1740 |
| 155 | Fried Tofu pudding | 23857 | 1233 | 1156 |
Table 8 dietary nutrient grade form
This system mainly uses project-based collaborative filtering to carry out recipe recommendation, and the first step sets up Rating Model: it is number of users that the input data of collaborative filtering are typically expressed as the user of a m*n-Evaluations matrix R, m, and n is item number, R
i, jrepresent that i-th user is to the score value of a jth project, m is here the quantity of food, and n is the number of contained nutrient in food, R
i, jrepresent the scoring of jth kind nutrient in i-th kind of food.
Second step finds nearest-neighbors: namely mainly complete searching target food nearest-neighbors, by calculating the similarity between goal object and other food, calculate to slip up to target the most similar " nearest-neighbors " to collect: first calculate the similarity between food, this proposed algorithm mainly contains the method for similarity between three kinds of measure user at present, respectively: the cosine similarity of cosine similarity, associated similarity and correction.What this algorithm adopted is associated similarity, and namely adopt Pearson correlation coefficients to measure, its computing formula is:
<math><math display = 'block'> <mrow> <mi>sim</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <msub> <mi>&Sigma;</mi> <mrow> <mi>c</mi> <mo>&Element;</mo> <msub> <mi>I</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> </mrow> </msub> <mrow> <mo>(</mo> <msub> <mi>R</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>c</mi> </mrow> </msub> <mo>&minus;</mo> <msub> <mover> <mi>R</mi> <mo>&macr;</mo> </mover> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <msub> <mi>R</mi> <mrow> <mi>j</mi> <mo>,</mo> <mi>c</mi> </mrow> </msub> <mo>&minus;</mo> <msub> <mover> <mi>R</mi> <mo>&macr;</mo> </mover> <mi>j</mi> </msub> <mo>)</mo> </mrow> </mrow> <mrow> <msqrt> <mrow> <msub> <mi>&Sigma;</mi> <mrow> <mi>c</mi> <mo>&Element;</mo> <msub> <mi>I</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> </mrow> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>R</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>c</mi> </mrow> </msub> <mo>&minus;</mo> <msub> <mover> <mi>R</mi> <mo>&macr;</mo> </mover> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> <msqrt> <mrow> <msub> <mi>&Sigma;</mi> <mrow> <mi>c</mi> <mo>&Element;</mo> <msub> <mi>I</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> </mrow> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>R</mi> <mrow> <mi>j</mi> <mo>,</mo> <mi>c</mi> </mrow> </msub> <mo>&minus;</mo> <msub> <mover> <mi>R</mi> <mo>&macr;</mo> </mover> <mi>j</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> </mrow> </mfrac> <mo>,</mo> </mrow></math> Wherein I
i, jrepresent the set of nutrient total in food.Secondly similarity is selected to be greater than the food of setting threshold value.
3rd step is the generation of recommending recipes: sort according to a certain specific nutrition element to the similar foodstuff collection produced in last step, and produces recommendation list in conjunction with the taste preference of user.
Additional: the availability detecting recipe:
A) the actual intake of nutrient is calculated: by searching composition table of foods, list calculates the actual intake of each nutrient;
B) extraction takes in reference value accordingly: the reference intake extracting each nutrient that user should take according to the personal information of user;
C) each nutrient evaluation: the difference number percent calculating the actual intake of various nutrient and Dietary reference intakes, if institute's value is not in the fluctuation range of respective element, then represents that the absorption of this kind of nutrient is unreasonable, need adjust.Fluctuation range as the standard intake of protein is between-5% and 5%, if the actual protein intake difference rate of user does not just represent within the scope of this that absorption is unreasonable.
Last model of recipe recommendation model, i.e. exchange model of equal value, this model meets the diversity of user's diet under being primarily implemented in the constant condition of food value, main use k-means clustering algorithm produces foods exchange list, Stochastic choice k was according to as initial center before this, calculate the distance of each data to selected each center out, data object is assigned in nearest bunch, then the average of each bunch is calculated, move in circles execution, until meet clustering criteria function convergence.Finally make the food similarity in the middle of each cluster higher, the food similarity in different cluster is lower.Help realizes food exchange, and the diet of people can be made to accomplish variation, and under the principle observing balanced diet, what three meals in a day arranged is rich and varied, tasteful, tastes delicious food all over the world.
Claims (6)
1. based on multiobject personalized food and drink recommend method and a system, comprising: carry out quantitative nutritious recipe according to user's idiotrophic element demand to user and recommend; Application class algorithm builds user model and realizes personalized recommendation; Application multi-objective optimization algorithm provides scientific and reasonable nutrient diet suggested design; Applicating cooperation filter algorithm and clustering algorithm carry out recommendation and realize dietary structure diversity.
2. described by claim 1 based on multiobject personalized food and drink recommend method and system, it is characterized in that: according to the age of user, sex, labor force, listener clustering (healthy person, patient, China's pregnant women and the dissimilar colony such as women breast-feeding their children and climacteric women) etc. user basic information obtain its human body and often to eat in the middle of one day the theoretical intake of essential nutrients, calculate in conjunction with user's diet record and take in nutrient content, further acquisition user remaining meal nutrient content, build user's intake model, quantitative nutritious recipe is recommended to user.
3. described by claim 1 based on multiobject personalized food and drink recommend method and system, it is characterized in that: by the context-aware computing of sight history and user browsing behavior and above-mentioned user basic information, classification is carried out to user and build user's taste preference model, in conjunction with above-mentioned user's intake model construction user model.
4. described by claim 1 based on multiobject personalized food and drink recommend method and system, it is characterized in that: multi-objective optimization algorithm find under the constraint condition meeting each nutrient intake standard in above-mentioned user's intake model optimum pantry combination.
5. described by claim 1 based on multiobject personalized food and drink recommend method and system, it is characterized in that: food nourishment composition table is converted into Nutritional Score of Food table, carry out Similarity Measure and build similarity set, project-based collaborative filtering carries out recipe recommendation.
6. described by claim 1 based on multiobject personalized food and drink recommend method and system, it is characterized in that: the diversity realizing meeting user's diet under the constant condition of food value, the main k-means of use clustering algorithm produces foods exchange list.
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