CN110675226A - Dish recommendation method and device, computer equipment and readable storage medium - Google Patents
Dish recommendation method and device, computer equipment and readable storage medium Download PDFInfo
- Publication number
- CN110675226A CN110675226A CN201910916349.2A CN201910916349A CN110675226A CN 110675226 A CN110675226 A CN 110675226A CN 201910916349 A CN201910916349 A CN 201910916349A CN 110675226 A CN110675226 A CN 110675226A
- Authority
- CN
- China
- Prior art keywords
- dish
- user
- target
- target dish
- historical
- 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
- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000004364 calculation method Methods 0.000 claims abstract description 11
- 230000006978 adaptation Effects 0.000 claims description 23
- 238000004590 computer program Methods 0.000 claims description 7
- 230000003044 adaptive effect Effects 0.000 claims description 6
- 235000012054 meals Nutrition 0.000 claims description 5
- 235000021186 dishes Nutrition 0.000 description 54
- 241000251468 Actinopterygii Species 0.000 description 13
- 240000002853 Nelumbo nucifera Species 0.000 description 4
- 235000006508 Nelumbo nucifera Nutrition 0.000 description 4
- 244000061456 Solanum tuberosum Species 0.000 description 4
- 235000002595 Solanum tuberosum Nutrition 0.000 description 4
- 241001372210 Gobioides broussonnetii Species 0.000 description 3
- 241000252230 Ctenopharyngodon idella Species 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 235000012015 potatoes Nutrition 0.000 description 2
- 235000019640 taste Nutrition 0.000 description 2
- 240000007124 Brassica oleracea Species 0.000 description 1
- 235000003899 Brassica oleracea var acephala Nutrition 0.000 description 1
- 235000011301 Brassica oleracea var capitata Nutrition 0.000 description 1
- 235000001169 Brassica oleracea var oleracea Nutrition 0.000 description 1
- 244000046738 asparagus lettuce Species 0.000 description 1
- 235000006705 asparagus lettuce Nutrition 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 235000011389 fruit/vegetable juice Nutrition 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- 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
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0631—Item recommendations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/12—Hotels or restaurants
Landscapes
- Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Finance (AREA)
- Marketing (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Accounting & Taxation (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The dish recommending method comprises the steps of obtaining interest degree of a user for a target dish through calculation according to similarity among dishes and historical dish ordering of the user, obtaining sum bearing value of the user for the target dish through calculation according to historical diner number of the user and sum bearing value of historical dishes, obtaining recommending degree of the target dish through calculation according to the interest degree and the sum bearing value of the user for the target dish, and recommending the target dish according to the recommending degree of the target dish. Therefore, dish recommendation meeting the requirements of the user can be carried out for different users.
Description
Technical Field
The application relates to the technical field of internet, in particular to a dish recommending method, a dish recommending device, computer equipment and a readable storage medium.
Background
At present, when a user selects dishes at a restaurant, the user mostly checks a recommendation menu of the restaurant through a terminal device to select the dishes. However, the recommendation menu can only represent the view of a part of people (for example, most users cannot evaluate dishes on the terminal device after consuming the dishes), and the existing recommendation menu is relatively single and cannot meet the diversity of the users.
In view of this, how to provide a recommendation menu capable of meeting different user requirements is a problem to be solved by those skilled in the art.
Disclosure of Invention
The embodiment of the application provides a dish recommending method and device, computer equipment and a readable storage medium.
The embodiment of the application can be realized as follows:
in a first aspect, an embodiment of the present application provides a dish recommendation method, which is applied to a computer device, where historical ordering dishes, historical diner numbers, historical dish amounts, and similarities among dishes of a user are stored in the computer device, and the method includes:
calculating the interest degree of the user on the target dish according to the similarity between dishes and historical dish ordering of the user;
calculating to obtain the sum bearing value of the user to the target dish according to the historical diner number of the user and the historical dish sum;
and calculating the recommendation degree of the target dish according to the interest degree and the sum bearing value of the user on the target dish, and recommending the target dish according to the recommendation degree of the target dish.
In an alternative embodiment, the interest level of the user in the target dish is calculated by the following formula:
wherein, PubThe interestingness of the user u on the target dish b is shown, N (u) is a set of historical order dishes of the user u, S (b, K) is a set of K dishes with the highest similarity to the target dish b, W isabIs the similarity of dish a and target dish b, RuaThe interestingness of the user u in the dish a.
In an optional embodiment, the calculating, according to the historical number of people having meals and the historical amount of dishes, the value borne by the user on the amount of the target dish is obtained, and the calculating includes:
acquiring the dish type of the target dish;
calculating to obtain the average consumption amount of the user for consuming the dish type of the target dish according to the historical diner number, the historical dish amount and the dish type of the target dish;
and calculating the sum bearing value of the user to the target dish according to the average consumption sum and a preset expansion coefficient.
In an alternative embodiment, the amount of money borne by the user on the target dish is calculated by the following formula:
wherein max is the sum of the target dishes borne by the user, m is the consumption frequency, moneyiA price of dish belonging to the category to which the target dish is consumed for the ith time, scopeiNum for the number of meals consumed for the ith timeiTheta is the number of the dishes belonging to the category of the ith consumption target dish and is a preset expansion coefficient,
is the average amount of money consumed.
In an optional embodiment, the calculating the recommendation degree of the target dish according to the interest degree and the amount tolerance of the user on the target dish includes:
calculating to obtain a first adaptive value according to the price of the target dish and the average consumption amount;
calculating to obtain a second adaptive value according to the price of the target dish and the sum bearing value of the target dish consumed by the user;
calculating to obtain a third adaptation value according to the interest degree of the user on the target dish and a preset repeated adaptation coefficient;
and calculating the recommendation degree of the target dish according to the first adaptation value, the second adaptation value and the third adaptation value.
In an optional embodiment, the recommending the target dish according to the recommendation degree of the target dish includes:
acquiring the set weight of the target dish;
calculating to obtain the actual recommendation degree of the target dish according to the set weight of the target dish and the recommendation degree of the target dish;
and placing the target dish to a corresponding position in a dish recommendation interface according to the actual recommendation degree of the target dish.
In an alternative embodiment, the method further comprises:
acquiring the weights of the target dish in a first ordering database and a second ordering database, calculating the recommended weight of the target dish according to a preset super parameter and the weight, and recommending the target dish according to the recommended weight of the target dish.
In a second aspect, an embodiment of the present application provides a dish recommendation device, which is applied to a computer device, where historical ordering dishes, historical diner numbers, historical dish amounts, and similarities among dishes of a user are stored in the computer device, and the device includes:
the calculation module is used for calculating the interest degree of the user in the target dish according to the similarity between dishes and the historical dish ordering of the user, and calculating the sum bearing value of the user on the target dish according to the historical diner number of the user and the historical dish sum;
and the recommending module is used for calculating the recommending degree of the target dish according to the interest degree and the sum bearing value of the user on the target dish and recommending the target dish according to the recommending degree of the target dish.
In a third aspect, an embodiment of the present application provides a computer device, where the computer device is communicatively connected to a terminal device, the computer device includes a processor and a non-volatile memory storing computer instructions, and when the computer instructions are executed by the processor, the computer device executes the dish recommendation method in any one of the foregoing embodiments.
In a fourth aspect, an embodiment of the present application provides a readable storage medium, where the readable storage medium includes a computer program, and the computer program controls, when executed, a computer device on the readable storage medium to perform the dish recommendation method according to any one of the foregoing embodiments.
The beneficial effects of the embodiment of the application include, for example:
by adopting the dish recommending method, the dish recommending device, the computer equipment and the readable storage medium, the interestingness of the user to the target dish is obtained according to the similarity between dishes and the historical dish ordering of the user, the sum bearing value of the user to the target dish is obtained through calculation according to the historical diner number of the user and the historical dish sum, the recommended degree of the target dish is obtained through skillfully combining the interestingness and the sum bearing value of the user to the target dish, and then the dish is recommended to the user according to the recommended degree corresponding to the target dish, so that dish recommendation meeting the requirements of the user can be carried out for different users.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a schematic flowchart illustrating steps of a dish recommendation method according to an embodiment of the present application;
FIG. 2 is a schematic flow chart illustrating the sub-steps of step S202 in FIG. 1;
fig. 3 is a block diagram schematically illustrating a structure of a dish recommending apparatus according to an embodiment of the present application;
fig. 4 is a block diagram schematically illustrating a structure of a computer device according to an embodiment of the present disclosure.
Icon: 100-a computer device; 110-a dish recommending device; 1101-a calculation module; 1102-a recommendation module; 111-a memory; 112-a processor; 113-communication unit.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
Furthermore, the appearances of the terms "first," "second," and the like, if any, are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
It should be noted that the features of the embodiments of the present application may be combined with each other without conflict.
At present, a user generally does not directly look at a common menu of a restaurant when going to the restaurant for consumption and ordering dishes, and focuses more on looking at a recommended menu of the restaurant through a terminal in order to save time and find dishes with better taste. However, most of the existing recommendation menus simply recommend the praise number of a certain dish according to a plurality of users, and cannot meet the requirements of different users (the tastes and consumption levels of different users are different). Based on the above, through research, the applicant provides a dish recommendation method, which is applied to a computer device, wherein the computer device stores historical ordering dishes, historical diner number, historical dish amount and similarity among various dishes of a user. As shown in fig. 1, the method may include steps S201 to S203.
Step S201, calculating the interest degree of the user on the target dishes according to the similarity between the dishes and the historical menu dishes of the user.
And step S202, calculating to obtain the sum bearing value of the user to the target dish according to the historical diner number of the user and the sum of the historical dishes.
Step S203, calculating the recommendation degree of the target dish according to the interest degree and the sum tolerance of the user to the target dish, and recommending the target dish according to the recommendation degree of the target dish.
In this embodiment, the user's historical dining history may be saved and may include historical ordering dishes (i.e., dishes in which the user is interested), historical dining population (i.e., the number of people the user consumes), historical dish amount (the amount the user consumes dishes), meanwhile, the similarity among the dishes can be stored, the similarity among the dishes can refer to the relationship between the degree of the dish a favored by the user and the degree of the dish b favored by the user, for example, when the dish ordering related to the grilled fish is carried out, the user A selects the spicy grilled fish, meanwhile, the potato, the asparagus lettuce, the lotus root slices and the plum juice are selected, the potato, the cabbage, the lotus root slices and the cola are selected by the user B, and if the record is only recorded according to the current point of the two users A and B, it can be seen that users who prefer potatoes also prefer lotus root slices, and therefore the similarity between potatoes and lotus root slices is high. It should be understood that the similarity between the dishes in the present embodiment may be calculated and obtained according to the dishes appearing in the multiple orders of the multiple users and the single user, and the foregoing example is for explanation only. For example, the following formula can be used to obtain the similarity between the dishes:
wherein, WabThe similarity between the dish a and the dish b is shown, n (a) is the number of users who like the dish a, and n (b) is the number of users who like the dish b, so that the calculation results of the similarity between the dishes can be normalized for the convenience of subsequent calculation.
On the basis, the interest degree of the user in the target dish can be calculated by the following formula:
wherein, PubThe interestingness of the user u on the target dish b is shown, N (u) is a set of historical order dishes of the user u, S (b, K) is a set of K dishes with the highest similarity to the target dish b, W isabIs the similarity of dish a and target dish b, RuaThe interestingness of the user u in the dish a.
In the formula, the interest degree of the user in the target dish b can be obtained according to the interest degree of the user u in the dish a and the similarity between the dish a and the target dish b, and for calculation convenience, the interest degree R of the user u in the dish a can be calculateduaTo be simplified to 1.
The step of calculating the sum of the target dishes borne by the user according to the historical dining number of the user and the historical dish sum can be realized through substeps S2021 to S2023, as shown in fig. 2.
And a substep S2021 of obtaining the dish type of the target dish.
And a substep S2022, calculating to obtain the average consumption amount of the user consuming the dish type to which the target dish belongs according to the historical diner number, the historical dish amount and the dish type to which the target dish belongs.
And a substep S2023, calculating the sum bearing value of the user to the target dish according to the average consumption sum and a preset expansion coefficient.
The sum bearing value of the user for the target dish can be understood as the maximum sum bearing value of the user for a single dish in the dish category to which the target dish belongs, for example, when the user performs fish roasting and ordering, the types of roasted fish can include grass carp, weever, dragon fish and Qingjiang fish, and the roasted fish needs to be recommended to the user at this time, so the average consumption sum of the roasted fish eaten by the user can be calculated according to the number of people eating the roasted fish by the user and the sum of the roasted fish eaten by the user historically, and then the sum bearing value of the user for the dish such as the roasted fish can be calculated according to the preset expansion coefficient. The preset expansion coefficient can be set by a merchant, and the preset expansion coefficient is set to recommend that the expansion coefficients of dishes with higher price and different categories of dishes with different prices in the sum bearing range of the user can be different, so that the sales amount of the merchant is increased. For example, the amount of money borne by the user on the target dish can be calculated by the following formula:
wherein max is the sum of the target dishes, m is the consumption frequency, moneyiA price of dish belonging to the category to which the target dish is consumed for the ith time, scopeiNum for the number of meals consumed for the ith timeiTheta is the number of the dishes belonging to the category of the ith consumption target dish and is a preset expansion coefficient,
is the average amount of money consumed.
For example, in this embodiment, the grass carp price is 100 yuan, the weever price is 130 yuan, the dragon fish price is 180 yuan, and the qingjiang fish price is 220 yuan, the calculated average consumption amount of the user on the roasted fish is 120 yuan, the preset expansion coefficient θ is set to 0.5, and the bearing value of the amount of the user on the roasted fish is (1+0.5) × 120 ═ 180, so that the dragon fish and the weever are recommended to the user appropriately.
In order to further recommend dishes to the user, the present embodiment further provides an example of adaptively adjusting the recommendation level of the target dish. The method specifically comprises the following steps:
and calculating to obtain a first adaptive value according to the price of the target dish and the average consumption amount.
And calculating to obtain a second adaptive value according to the price of the target dish and the sum bearing value of the target dish consumed by the user.
And calculating to obtain a third adaptation value according to the interest degree of the user on the target dish and a preset repeated adaptation coefficient.
And calculating the recommendation degree of the target dish according to the first adaptation value, the second adaptation value and the third adaptation value.
Under the condition that interest degree and sum bearing value of a user on a target dish are met, in order to increase sales volume for merchants, the recommended dish price cannot be too low, so that the price of the target dish and the average consumption sum can be compared, dishes with too low prices can be adapted, a first adaptation value is calculated, meanwhile, dishes with too high prices are recommended and cannot be accepted by the user, so that the price of the target dish and the sum bearing value of the target dish consumed by the user can be compared, a second adaptation value is calculated, besides, in order to ensure freshness of the dishes ordered by the user, a preset repeated adaptation coefficient can be added to the dishes already ordered by the user, the dishes already eaten by the user are adapted, a third adaptation value is calculated, and then according to the first adaptation value, the second adaptation value and the third adaptation value, the recommendation degree of the target dish is obtained, and can be calculated by the following formula:
wherein, P'ubTo recommend to the useru recommendation degree of the target dish b,
in order to preset the repeated adaptation coefficients,
m is the price of the target dish b, average is the average consumption amount, max is the amount of the user to the target dish b, sigma, omega, tau belongs to [0,1 ]]And delta is the price difference value of the dish with the highest price sum and the dish with the lowest price sum in the dish category to which the target dish b belongs.
On this basis, the present embodiment may further limit the recommendation degree of the target dish:
and acquiring the set weight of the target dish.
And calculating the actual recommendation degree of the target dish according to the set weight of the target dish and the recommendation degree of the target dish.
And placing the target dish to a corresponding position in a dish recommendation interface according to the actual recommendation degree of the target dish.
In this embodiment, the merchant may also recommend the user according to its own needs, for example, the merchant signboard menu item or the newly pushed menu item may be optimized based on the recommendation degree of the target menu item obtained by the above calculation, a set weight may be given to the target menu item, and then the actual recommendation degree of the target menu item is calculated according to the set weight and the recommendation degree of the target menu item. Illustratively, this can be calculated by the following formula:
P″ub＝′ub+(1-η)Qb
wherein, P ″)ubActual recommendation degree, Q, of target dish b recommended to user ubAnd setting weight for the merchant to the target dish b.
The value of η may be set smaller when the merchant deems the recommendation itself to be relatively important, in which case η < (1- η), and larger when the merchant deems the recommendation itself not to be particularly important, in which case η > (1- η). The specific recommendation mode may be that the merchant sequentially displays the target dishes in a recommendation menu interface of the ordering terminal according to the calculated value of the actual recommendation degree of the target dishes.
On the basis of the foregoing, it can be clearly known how to make dish recommendations for a user according to historical order data of the user, and besides, this embodiment also provides an example of making dish recommendations for a new user (i.e., a user without various historical order data). The method can be realized by the following steps: acquiring the weights of the target dish in a first ordering database and a second ordering database, calculating the recommended weight of the target dish according to a preset super parameter and the weight, and recommending the target dish according to the recommended weight of the target dish.
In this embodiment, the method for recommending dishes for a new user may be to obtain the praise numbers of the dishes from the multi-dish ordering platform, and then recommend the user according to the praise numbers of the dishes and preset hyper parameters. For example, dish i has a like number of z on the order platformiThen the total number of praise on the ordering platform may be
The weight of the dish i on the ordering platform can be obtained
The weight q of the dish i can be obtained from two ordering platforms, namely the ordering platform mm，iAcquiring the weight q of the dish i on the ordering platform dd,iThen, setting a preset hyper-parameter alpha, and further calculating and obtaining the recommended weight of the target dish according to the following formula:
qa,i＝αqm,i+(1-α)qd,i
wherein q isa,iFor the recommended weight of dish i, α ∈ [0,1 ]]If the user thinks that the ordering platform m has more reference value, the value of the super parameter alpha can be set to be larger, and if the user thinks that the ordering platform d has more reference value, the value of the super parameter alpha can be set to be smaller.
Referring to fig. 3, the dish recommending apparatus 110 of the present embodiment further includes:
the calculating module 1101 is configured to calculate an interest degree of the user in the target dish according to the similarity between dishes and historical dish ordering items of the user, and calculate an amount of money borne by the user on the target dish according to the historical number of people for having a meal and the amount of money of the historical dish of the user.
And the recommending module 1102 is configured to calculate the recommendation degree of the target dish according to the interest degree and the sum tolerance of the user on the target dish, and recommend the target dish according to the recommendation degree of the target dish.
The embodiment further provides a computer device 100, as shown in fig. 4, the computer device 100 is in communication connection with a terminal device, the computer device 100 includes a processor and a non-volatile memory storing computer instructions, and when the computer instructions are executed by the processor, the computer device 100 executes the aforementioned dish recommendation method. The computer device 100 comprises a dish recommending means 110, a memory 111, a processor 112 and a communication unit 113.
The memory 111, the processor 112 and the communication unit 113 are electrically connected to each other directly or indirectly to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The dish recommending means 110 includes at least one software function module which can be stored in the memory 111 in the form of software or firmware (firmware) or solidified in an Operating System (OS) of the computer device 100. The processor 112 is used for executing executable modules stored in the memory 111, such as software functional modules and computer programs included in the dish recommending device 110.
The Memory 111 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.
The present embodiment further provides a readable storage medium, which includes a computer program, and the computer program controls the computer device 100 on which the readable storage medium is located to execute the aforementioned dish recommendation method when the computer program runs.
In summary, the embodiment of the application provides a dish recommending method, a dish recommending device, computer equipment and a readable storage medium, wherein the interestingness and the sum bearing value of a user on a target dish are obtained by analyzing and calculating historical menu ordering items, historical diner numbers, historical dish sum and the similarity between dishes of the user, and the recommendability of the user on the target dish is obtained by skillfully calculating the interestingness and the sum bearing value of the user on the target dish, so that dishes more suitable for the user are recommended for different users.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A dish recommendation method is applied to a computer device, historical ordering dishes, historical dinning number, historical dish amount and similarity among various dishes of a user are stored in the computer device, and the method comprises the following steps:
calculating the interest degree of the user on the target dish according to the similarity between dishes and historical dish ordering of the user;
calculating to obtain the sum bearing value of the user to the target dish according to the historical diner number of the user and the historical dish sum;
and calculating the recommendation degree of the target dish according to the interest degree and the sum bearing value of the user on the target dish, and recommending the target dish according to the recommendation degree of the target dish.
2. The method of claim 1, wherein the user's interest level in the target dish is calculated by the following formula:
wherein, PubThe interestingness of the user u on the target dish b is shown, N (u) is a set of historical order dishes of the user u, S (b, K) is a set of K dishes with the highest similarity to the target dish b, W isabIs the similarity of dish a and target dish b, RuaThe interestingness of the user u in the dish a.
3. The method of claim 1, wherein calculating the sum exposure value of the user to the target dish according to the historical dining number of the user and the historical dish sum comprises:
acquiring the dish type of the target dish;
calculating to obtain the average consumption amount of the user for consuming the dish type of the target dish according to the historical diner number, the historical dish amount and the dish type of the target dish;
and calculating the sum bearing value of the user to the target dish according to the average consumption sum and a preset expansion coefficient.
4. The method of claim 3, wherein the amount of money the user has borne on the target dish is calculated by the following formula:
wherein max is the sum of the target dishes borne by the user, m is the consumption frequency, moneyiA price of dish belonging to the category to which the target dish is consumed for the ith time, scopeiNum for the number of meals consumed for the ith timeiTheta is the number of the dishes belonging to the category of the ith consumption target dish and is a preset expansion coefficient,
is the average amount of money consumed.
5. The method of claim 3, wherein calculating the recommendation degree of the target dish according to the interest degree and the amount tolerance value of the user on the target dish comprises:
calculating to obtain a first adaptive value according to the price of the target dish and the average consumption amount;
calculating to obtain a second adaptive value according to the price of the target dish and the sum bearing value of the target dish consumed by the user;
calculating to obtain a third adaptation value according to the interest degree of the user on the target dish and a preset repeated adaptation coefficient;
and calculating the recommendation degree of the target dish according to the first adaptation value, the second adaptation value and the third adaptation value.
6. The method of claim 1, wherein the recommending the target dish according to the recommendation degree of the target dish comprises:
acquiring the set weight of the target dish;
calculating to obtain the actual recommendation degree of the target dish according to the set weight of the target dish and the recommendation degree of the target dish;
and placing the target dish to a corresponding position in a dish recommendation interface according to the actual recommendation degree of the target dish.
7. The method of claim 1, further comprising:
acquiring the weights of the target dish in a first ordering database and a second ordering database, calculating the recommended weight of the target dish according to a preset super parameter and the weight, and recommending the target dish according to the recommended weight of the target dish.
8. A dish recommendation device is applied to a computer device, historical ordering dishes, historical dinning number, historical dish amount and similarity among various dishes of a user are stored in the computer device, and the dish recommendation device comprises:
the calculation module is used for calculating the interest degree of the user in the target dish according to the similarity between dishes and the historical dish ordering of the user, and calculating the sum bearing value of the user on the target dish according to the historical diner number of the user and the historical dish sum;
and the recommending module is used for calculating the recommending degree of the target dish according to the interest degree and the sum bearing value of the user on the target dish and recommending the target dish according to the recommending degree of the target dish.
9. A computer device communicatively connected to a terminal device, the computer device comprising a processor and a non-volatile memory storing computer instructions, the computer instructions when executed by the processor cause the computer device to perform the dish recommendation method of any one of claims 1-7.
10. A readable storage medium, characterized in that the readable storage medium comprises a computer program, which when executed controls a computer device on which the readable storage medium is located to perform the dish recommendation method according to any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910916349.2A CN110675226B (en) | 2019-09-26 | 2019-09-26 | Dish recommendation method and device, computer equipment and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910916349.2A CN110675226B (en) | 2019-09-26 | 2019-09-26 | Dish recommendation method and device, computer equipment and readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110675226A true CN110675226A (en) | 2020-01-10 |
| CN110675226B CN110675226B (en) | 2022-08-09 |
Family
ID=69079217
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910916349.2A Active CN110675226B (en) | 2019-09-26 | 2019-09-26 | Dish recommendation method and device, computer equipment and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110675226B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112199406A (en) * | 2020-09-30 | 2021-01-08 | 聚好看科技股份有限公司 | Information recommendation method, food material storage device and server |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106651524A (en) * | 2016-12-27 | 2017-05-10 | 杭州火小二科技有限公司 | Method for intelligently generating recommended menu |
| CN106682417A (en) * | 2016-12-27 | 2017-05-17 | 杭州火小二科技有限公司 | Healthcare based recommended menu generation method |
| US20170262948A1 (en) * | 2016-03-08 | 2017-09-14 | International Business Machines Corporation | Determination of targeted food recommendation |
| CN107203950A (en) * | 2016-03-18 | 2017-09-26 | 湖南餐启科技有限公司 | A kind of vegetable recommends method and system |
| CN108280729A (en) * | 2017-01-06 | 2018-07-13 | 中兴通讯股份有限公司 | A kind of food preparation method and device |
| CN109934658A (en) * | 2017-12-19 | 2019-06-25 | 阿里巴巴集团控股有限公司 | A kind of data processing method, display methods and calculate equipment |
-
2019
- 2019-09-26 CN CN201910916349.2A patent/CN110675226B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170262948A1 (en) * | 2016-03-08 | 2017-09-14 | International Business Machines Corporation | Determination of targeted food recommendation |
| CN107203950A (en) * | 2016-03-18 | 2017-09-26 | 湖南餐启科技有限公司 | A kind of vegetable recommends method and system |
| CN106651524A (en) * | 2016-12-27 | 2017-05-10 | 杭州火小二科技有限公司 | Method for intelligently generating recommended menu |
| CN106682417A (en) * | 2016-12-27 | 2017-05-17 | 杭州火小二科技有限公司 | Healthcare based recommended menu generation method |
| CN108280729A (en) * | 2017-01-06 | 2018-07-13 | 中兴通讯股份有限公司 | A kind of food preparation method and device |
| CN109934658A (en) * | 2017-12-19 | 2019-06-25 | 阿里巴巴集团控股有限公司 | A kind of data processing method, display methods and calculate equipment |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112199406A (en) * | 2020-09-30 | 2021-01-08 | 聚好看科技股份有限公司 | Information recommendation method, food material storage device and server |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110675226B (en) | 2022-08-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10474705B2 (en) | Iterative image search algorithm informed by continuous human-machine input feedback | |
| US20210134434A1 (en) | System and Method for Improving Food Selections | |
| JP6161992B2 (en) | Sales prediction system and sales prediction method | |
| US20130138656A1 (en) | Method and Device for Expiration Date Weighted Food Inventory System and Meal Planner | |
| US20080086374A1 (en) | Nutrition management and meal planning program | |
| KR102371787B1 (en) | System for providing customized dietary management service | |
| US20100312668A1 (en) | Methods and systems producing reliable personalized adaptive information regarding products | |
| Chisanga et al. | The changing food expenditure patterns and trends in Zambia: implications for agricultural policies | |
| JP2019045980A (en) | Information processing apparatus, information processing method, and program | |
| CN110675226B (en) | Dish recommendation method and device, computer equipment and readable storage medium | |
| US9818144B2 (en) | Visual product feedback | |
| CN110852299A (en) | Method and device for determining eating habits of customers | |
| CN110555752A (en) | Personalized recommendation method and device, electronic equipment and readable storage medium | |
| US20220180408A1 (en) | Quantification of human food tastes across food matrices, food servers and food consumers | |
| KR20220152794A (en) | System for providing user-custormized recommending drinks and method thereof | |
| KR102453378B1 (en) | Personalized menu recommendation system using micro data basis artificial intelligence | |
| US20220012800A1 (en) | System and method for ordering food | |
| KR20220075139A (en) | Recipe customized for users and method for providing meal-kit products using the same | |
| Ringuette et al. | Market surveys to determine availability of reduced glycemic load/high fiber products | |
| JP2023154330A (en) | Shopping support system, shopping support method, and program | |
| US20140136368A1 (en) | Avatar having artificial intelligence for facilitating the creation of personal decision contracts | |
| CN114297482A (en) | Food material information processing method and device and food material management terminal | |
| Nicita | Price elasticities and tax reform in Mexico | |
| Koski et al. | Do grocery feedback systems enabling access to past consumption impact individual food purchase behavior? | |
| Yang | Essays on Price and Time in Trade and Household Production |
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 |