CN110544160A - Dish recommending method - Google Patents
Dish recommending method Download PDFInfo
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- CN110544160A CN110544160A CN201910849253.9A CN201910849253A CN110544160A CN 110544160 A CN110544160 A CN 110544160A CN 201910849253 A CN201910849253 A CN 201910849253A CN 110544160 A CN110544160 A CN 110544160A
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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
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
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- 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
Abstract
The invention discloses a dish recommending method, which comprises the following steps: the method comprises the steps of obtaining a dish recommending request of a consumer user, wherein the dish recommending request comprises the geographical position of the consumer user; according to the dish recommendation request, determining merchants within the preset geographic position range, selecting target dishes which are the same as popular dishes in a preset dish ranking list from the dishes issued by the merchants, and generating a dish recommendation list containing the target dishes; pushing the dish recommendation list to the consumer user. The dish recommending method and the dish recommending device can recommend dishes to the user, so that the user can conveniently and quickly order dishes, and the user experience is improved.
Description
Technical Field
the invention relates to the technical field of internet, in particular to a dish recommending method.
Background
With the development of internet technology, the life style of people is gradually changed, traditional diet consumption is not that users actively take meals, take-out is gradually changed into a dining style of fast-paced urban life, take-out platforms such as American groups and hungry places are gradually grown up, and the quantity of users for take-out is also gradually increased.
Although the dishes on the take-out platform are rich and various, the user inevitably encounters the problem of 'what to eat today' and is difficult to select the dishes suitable for the user. With the development of big data technology, dishes can be actively recommended to users completely according to historical data, so that a new dish recommendation method is provided in combination with big data analysis.
Disclosure of Invention
The dish recommending method provided by the invention can recommend dishes to a user, is convenient for the user to order dishes quickly, and improves the user experience.
The embodiment of the invention provides a dish recommending method, which comprises the following steps: the method comprises the steps of obtaining a dish recommending request of a consumer user, wherein the dish recommending request comprises the geographical position of the consumer user; according to the dish recommendation request, determining merchants within the preset geographic position range, selecting target dishes which are the same as popular dishes in a preset dish ranking list from the dishes issued by the merchants, and generating a dish recommendation list containing the target dishes; pushing the dish recommendation list to the consumer user.
preferably, the dish ranking list ranks the dishes according to sales of the dishes in the system, attention of the dishes or number of released dishes, and the dish ranking list is updated in real time.
Preferably, the step of generating a dish recommendation list including the target dish specifically includes: and acquiring historical dining records of the user, selecting dishes appearing in the historical dining records from the target dishes, and generating a dish recommendation list containing the selected dishes.
Preferably, the step of generating a dish recommendation list including the target dish specifically includes: the method comprises the steps of obtaining historical dining records of a user, counting dishes which appear at high frequency in the historical dining records, determining taste preference of a consumer user according to the dishes which appear at high frequency in the historical dining records, selecting dishes which accord with the taste preference of the consumer user from target dishes, and generating a dish recommendation list containing the selected dishes.
Preferably, the step of determining the taste preference of the consumer user according to the dishes with high frequency in the historical dining record specifically includes: and counting the cuisine and the mouthfeel of dishes which appear at high frequency in the historical dining record, and sequencing the cuisine and the mouthfeel in a descending order, so that the favorite cuisine and mouthfeel of the consumer user form the taste preference of the consumer user.
Preferably, the dish series of the dish is distributed by the system or set by a merchant issuing the dish, and the taste of the dish is determined by the voting value of the consumer user on the taste of the dish.
Preferably, the dish recommendation list comprises meal ordering links corresponding to dishes in the dish recommendation list; after the step of pushing the dish recommendation list to the consumer user, the method further comprises: and when the customer user selects the order link, displaying an order interface of the dishes corresponding to the order link at the client of the customer user.
Preferably, after the step of pushing the dish recommendation list to the consumer user, the method further includes: recording dishes in a dish recommendation list pushed to a consumer user each time to form a recommended dish set; recording the dishes actually selected by the consumer user in the dish recommendation list to form a dish set for placing the order; judging whether the dish with the occurrence frequency reaching the preset frequency in the dish recommendation list belongs to a dish set for placing an order or not, if not, determining that the dish is a dish which is not loved; before pushing the dish recommendation list to the consumer user next time, judging whether the dish recommendation list contains the dish which is not loved, and if so, removing the dish which is not loved from the dish recommendation list.
Preferably, when the target dish has a plurality of dishes, the dish recommendation list contains only one dish randomly determined from the target dish.
Preferably, after the pushing the dish recommendation list to the consumer user, the method further includes: and when the consumer user places an order to purchase the dishes in the dish recommendation list, randomly reducing the order amount below a preset limit.
In the invention, when the user does not know what to order, the server can recommend the dishes in the dish ranking list to the user according to the request of the user, and the dishes are ordered and purchased by the user, so that the selection is provided for the user to order, the user can conveniently and quickly order the dishes, and the user experience is improved.
Drawings
Fig. 1 is a flowchart illustrating a dish recommending method according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following detailed description and accompanying drawings.
Before describing the method of the embodiment of the present invention, it is necessary to describe a system to which the present invention is applied. The system applied by the invention comprises a server, a consumption user side and a merchant user side, wherein the consumption user side and the merchant user side log in the system through the client sides. The system is a take-out consumer goods platform, a consumer user is characterized as a buyer, the consumer user can place an order and purchase the order by browsing the dish information issued by a merchant on a website, the merchant user is characterized as a seller, the merchant user can issue the product information needing to be sold to the website, receive the order of the user and deliver the dish purchased by the user to the home through third-party logistics service.
An embodiment of the present invention provides a method for recommending dishes, which will be described from a server side, as shown in fig. 1, and includes the following steps:
S100: the method comprises the steps of obtaining a dish recommending request of a consumer user, wherein the dish recommending request comprises the geographical position of the consumer user.
When the consumer user uses the system of the embodiment, a floating button can be displayed on the main interface of the system, the floating window does not move along with the interface, and when the consumer user slides the interface up and down, the floating button can be kept at a preset position of the interface. Some characters can be written on the suspension button, for example, "help me choose a meal". When the consumer user does not know what food to select and wants the system to recommend, the consumer user can click the hover button, and in response to the user operating the hover button, the client sends a dish recommendation request to the server, where the dish recommendation request includes information of the consumer user, and specifically may include a user name, an ID number, a request sending time, and the like of the consumer user.
Since the take-out merchant has a distance requirement for food delivery, the merchant to which the dishes pushed by the subsequent server belong can meet the food delivery condition only when the merchant is within a predetermined range of the location of the consumer user, and therefore the dish recommendation request necessarily includes the geographic location of the consumer user. Generally, a client requests permission of a user position, and after the user gives the permission to the client, the system can be refreshed in real time to obtain the geographic position of the user each time the user opens the client. And when the server receives a dish recommending request of the user, the information such as the geographical position of the user is obtained.
s200, according to the dish recommendation request, determining merchants within the preset geographic position range, selecting target dishes which are the same as popular dishes in a preset dish ranking list from the dishes issued by the merchants, and generating a dish recommendation list containing the target dishes.
the server responds to the dish recommendation request of the user, determines the merchants located in the preset range of the geographic position of the user, namely selects the merchants which are close to the consumer user and can be sent for taking out. When registering, the merchant user needs to provide the address of the merchant user to the system, so that the server can judge whether the distance difference between the address position of the merchant and the geographic position of the consumer user is smaller than the preset range value, and if the distance difference is smaller than the preset range value, the merchant meets the requirement on the geographic position and is the merchant to be selected.
The system can count the sales volume, the attention volume or the release number of the same dish in a preset time period in real time, wherein the sales volume refers to the sales volume of the same dish in the system in the preset time period, the attention volume refers to the collection number of the same dish or the times of searching the same dish by a user, the release number of the dish refers to the number of the same dish released by different merchants, the three parameters directly reflect the popularity of a certain dish, the system counts the parameter values of various dishes, and accordingly ranks the dishes according to the popularity from large to small to form a real-time updated dish ranking list, only the top ten or twenty popular dishes can be listed in the dish ranking list, and the preset time period for counting the dish ranking list can be one week or one day.
Because the popularity of dishes is greatly affected by regions, the concepts and tastes of users in different regions are different, and the tastes of users in the same region are relatively uniform through big data statistical analysis, the dish ranking list needs to limit the regions, only data in a preset region is counted, and the dish ranking list for the regions is generated, wherein the dish ranking list can be a city where a consumer and a user are located or a province where the consumer is located. How to divide the data can be set in advance by system staff after market research.
In the statistical process of the ranking list of the dishes, the names of merchants and consumers may be different for the same dish, and the same dish may have a plurality of names in the processes of dish selling, dish searching and dish publishing, and if a single keyword is relied on, certain inaccuracy is brought to data statistics. For example, the dish is tomato scrambled eggs, and the common users also refer to the dish as tomato scrambled eggs, and the like. Therefore, keyword combination can be established for each dish, the keyword set collects all titles of the dish, and when the keyword for a certain dish falls into the keyword set, the dish is the dish corresponding to the keyword set. Therefore, the accuracy of the data can be ensured to a certain extent.
Because the to-be-selected merchants of the user in the preset geographic position range are determined, the server can count the dish information issued by the to-be-selected merchants, select the target dishes identical to the popular dishes in the preset dish ranking list, and then generate a dish recommendation list containing the target dishes, wherein the dish recommendation list comprises the information of the target dishes and the merchant information.
S300: pushing the dish recommendation list to the consumer user.
After the dish recommendation list is generated, the server sends the dish recommendation list to the consumer user, a dish recommendation page is displayed on a client of the consumer user, information of a target dish and corresponding merchant information can be presented in a list form, and specifically, information such as the name, the price, the name and the address of a merchant of the target dish can be displayed. Because the pushed dishes are popular dishes, the selection is provided for the user to select the dishes, the ordering by the user is facilitated, and the user experience is improved.
In an embodiment, in step S200, the step of generating a dish recommendation list including the target dish specifically includes: and acquiring historical dining records of the user, selecting dishes appearing in the historical dining records from the target dishes, and generating a dish recommendation list containing the selected dishes.
After the target dishes are determined, the target dishes are further screened. Although the target dishes are embodied as dishes that are popular with the user, and are also popular with the consumer user on behalf of the same, for this reason, the target dishes may be combined with the consumer user's own taste, and then dishes that are popular with the user may be selected.
The server can obtain historical dining records of the consumer user, the historical dining records are reflected in dishes recorded in previous orders of the user, and the dish users consume the dishes in the past and are popular with the user. Dishes appearing in the historical eating history are thus selected from the target dishes, which more closely match the needs of the consumer user, from which a dish recommendation list is generated. In order to enhance the fitness of the recommended dishes and the user requirements, dishes with high frequency in the historical dining records are selected in the process of screening the target dishes, and the dish with high frequency shows that the dishes are frequently consumed by the user and can better meet the user requirements.
Further, when selecting the target dish from the historical dining records of the consumer user, the historical dining records are not directly compared with the target dish, because the target dish is likely not to appear in the historical dining records of the consumer user, and if so, no dish is recorded in the dish recommendation list. After the historical dining records of the user are obtained, dishes with high frequency in the historical dining records are counted, wherein the dishes with high frequency can be dishes with frequency of occurrence larger than a preset frequency, or the frequency of occurrence of each dish in the historical dining records of the user is counted, ranking is carried out from large to small, and the first few dishes with high frequency are taken. The dishes have different dishes and tastes, and for each dish, the system can set the dish to which the dish belongs, for example, the 'white chicken' belongs to Guangdong dish, and the 'couple lung tablet' belongs to Chuan dish; for more common dishes without direct affiliation, the dishes can be listed as home dishes, such as tomato fried eggs; meanwhile, the system can set the taste of dishes, the taste can be divided into two major categories, namely heavy taste and light taste, the heavy taste can be subdivided into partial peppery taste, partial salty taste, partial sour taste and the like, and the light taste can be subdivided into poaching type, partial sweet taste and the like. Of course, the system may also require that the merchant must specify the cuisine and taste to which the dish belongs when issuing the dish, which is more accurate than the system settings.
For dishes released by a merchant, any consumer user who consumes the dishes can evaluate the taste of the dishes. In an evaluation system, the taste of the dish is represented in a numerical form, the maximum value of the numerical value is 10, the minimum value is 1 and is used for representing the 'lightest dish', the maximum value 10 is used for representing the 'heaviest taste dish', the value is closer to the minimum value and indicates that the dish is lighter, and the value is closer to the maximum value and indicates that the dish is heavier in taste. The system will count the scores of all users and calculate the average value to obtain the final score, and can grade according to the scores to determine whether the dish is a very light dish, a relatively light dish, a moderate dish, a heavy-tasting dish or a very heavy-tasting dish. In another evaluation system, more than three evaluation options can be set, for example, the evaluation options can be a spicy option, a salty option, a sour option and a bitter option, the system counts the number of times of selection of each option, and determines the option with the most number of times of selection, namely, the dish is the taste represented by the option with the most number of times of selection. Therefore, the taste of the dish is evaluated by the consumer, and the obtained data is more accurate and real.
The cuisine and the mouthfeel are sequenced by counting the cuisine and the mouthfeel of each high-frequency appearing cuisine in the historical dining record, so that the most favorite cuisine and the first few tastes of a consumer user are determined, and the cuisine and the mouthfeel form the taste preference of the user. And selecting dishes which accord with the taste preference of the consumer user from the target dishes, and further generating a dish recommendation list containing the selected dishes, so that the requirements of the user can be better met.
in the process of determining the taste preference of the user, it is preferable to judge in advance whether the number of dishes recorded in the historical dining record reaches a preset number, and if the number reaches the preset number, the taste preference is determined, and if the number does not reach the preset number, the counted base number is insufficient, which affects the accuracy of the judgment.
In one embodiment, the dish recommendation list not only displays information of recommended dishes in a list form, but also can further provide an interactive function. Specifically, the dish recommendation list includes an order link corresponding to the dishes in the dish recommendation list, where the order link may be an order button or a hyperlink of a page, which is displayed at an associated position of the dishes, for example, at the end of a row where the dishes are located, so as to correspond to the selected dishes.
After step S300 in the above embodiment, the method further includes the following steps: and when the customer user selects the order link, displaying an order interface of the dishes corresponding to the order link at the client of the customer user.
When a consumer user feels that a certain dish in the dish recommendation list is suitable for the consumer user, the consumer user can click the ordering link, the client responds to the operation of the user and requests the server to order dishes, the server feeds the ordering data back to the client of the consumer user after receiving the ordering request of the consumer user, so that an ordering interface is displayed at the client of the consumer user, the ordering interface is the ordering interface of the dish selected by the user, the ordering interface displays the name, specification, quantity and other information of the dish, the user can fill in the address, the receiver, the ordering quantity and other information at the interface, after the user confirms, a corresponding order is generated and temporarily reserved at the client and has a preset valid time limit, and when the user does not pay money within the valid time limit, the order is automatically invalidated. When the user pays enough money within the valid time limit, the order is valid, the server sends the order to a merchant corresponding to the dish, and the merchant arranges and dispatches the dish.
Therefore, the user can not only obtain the information of the recommended dishes from the dish recommendation list, but also directly request to order on the page of the dish recommendation list, and then directly jump to the ordering interface, so that the user can directly place an order. The meal ordering operation of the user is greatly facilitated, the user does not need to search by himself, and then meal ordering is carried out additionally.
In one embodiment, after step S300, the method further includes the following steps: the server records dishes in a dish recommendation list pushed to the consumer user each time to form a recommended dish set, wherein the dish recommendation set comprises information such as names, occurrence times and each recommended time of the recommended dishes; and recording the dishes actually selected by the consumer user in the dish recommendation list to form a dish ordering set, wherein the dish ordering set records the names of the actually ordered dishes, the ordering times, the ordering time and other information. When the times recorded by the same dish in the recommended dish set reach the preset times, the dish is indicated to be repeatedly recommended to the user, meanwhile, whether the dish with the times reaching the preset times in the dish recommendation list belongs to the dish set for ordering is judged, if not, the dish is not a favorite dish, the user does not like the dish, and the repeated recommendation to the user is meaningless.
Therefore, in the process of recommending dishes next time, whether the dish recommendation list contains the dish which is not loved or not is judged, if yes, the dish which is not loved is removed from the dish recommendation list, meanwhile, the dish can be replaced by a new dish, the dish which can be recommended by the dish recommendation list is guaranteed, and otherwise, after the dish is removed for multiple times, the dishes reserved in the dish recommendation list are gradually reduced.
In an embodiment, when there are multiple target dishes determined in step S200, one of the multiple target dishes may be randomly selected, and the dish recommendation list only includes the randomly determined one dish, that is, only a single dish is recommended, so that the user only selects the dish or does not make a selection, on one hand, time for the user to make a second selection is reduced, which is more convenient for the user to quickly select, and on the other hand, the randomly generated recommended dish will be more random, which may give a sense of freshness and a sense of surprise to the user. In order to further prevent the dishes recommended by two adjacent times from being the same, the last recommended dish can be recorded, when the dish is recommended next time, whether the dish randomly selected from the target dishes is the last recommended dish is judged, if not, the dish is listed in the dish recommendation list, and if so, one dish is randomly selected from the target dishes again.
The technical means for randomly selecting one dish can be combined with the historical dining record of the user, specifically, the server obtains the historical dining record of the user, selects dishes appearing in the historical dining record from the target dishes to form a dish to be selected, randomly selects one dish from the dishes to be selected, and generates a dish recommendation list containing the randomly selected dish. Therefore, the recommended dishes at each time are dishes conforming to the taste habits of the user, and the acceptance degree of the user is improved. Of course, the user acceptance can be further improved. After the historical dining records of the user are obtained, dishes with high frequency in the historical dining records are counted, wherein the dishes with high frequency can be dishes with frequency of occurrence larger than a preset frequency, or the frequency of occurrence of each dish in the historical dining records of the user is counted, ranking is carried out from large to small, and the first dishes with high frequency are taken. The cuisine and the mouthfeel are sequenced by counting the cuisine and the mouthfeel of each high-frequency appearing cuisine in the historical dining record, so that the most favorite cuisine and the first few tastes of a consumer user are determined, and the cuisine and the mouthfeel form the taste preference of the user. The method comprises the steps of selecting dishes with preference to taste of a user from target dishes to form a dish to be selected, randomly selecting a dish from the dishes to be selected, and generating a dish recommendation list containing the randomly selected dish.
Further, after step S300, the method further includes the steps of: when a consumer user places an order to purchase a unique dish in the dish recommendation list, the system gives a reward to the user, the reward can be represented by distributing an electronic red packet to the user, the electronic red packet can be used for deducting the amount of money consumed by the user in the system, the use permission of the electronic red packet can be given, and the method is only limited to the user when the user orders a meal. After a user selects a certain dish in the dish recommendation list, the user can click an order link corresponding to the dish, the client responds to the operation of the user and requests the server to order the dish, the server receives the order request of the consumer user and then verifies whether the dish selected by the user is the only dish in the dish recommendation list, if so, the system feeds back order data to the client of the consumer user and simultaneously dispatches an electronic red packet only used for ordering at the time to the consumer user, the client of the consumer user displays an order interface, the order interface displays information such as name, specification, quantity and the like of the dish and can also display options for deduction of the red packet, the user can select the electronic red packet to deduct the amount of money required to be paid by the user, and the total amount of money required to be paid in the order is correspondingly reduced. This will attract the user to use the dish recommendation function of the system.
The foregoing is a more detailed description of the present invention that is presented in conjunction with specific embodiments, and the practice of the invention is not to be considered limited to those descriptions. It will be apparent to those skilled in the art that a number of simple derivations or substitutions can be made without departing from the inventive concept.
Claims (10)
1. A dish recommending method is characterized by comprising the following steps:
The method comprises the steps that a server obtains a dish recommending request of a consumer user, wherein the dish recommending request comprises the geographical position of the consumer user;
According to the dish recommendation request, determining merchants within the preset geographic position range, selecting target dishes which are the same as popular dishes in a preset dish ranking list from the dishes issued by the merchants, and generating a dish recommendation list containing the target dishes;
Pushing the dish recommendation list to the consumer user.
2. The method of claim 1, wherein:
And the dish ranking list sorts the dishes according to the sales of the dishes in the system, the attention of the dishes or the release number of the dishes, and is updated in real time.
3. The method of claim 1, wherein the step of generating the dish recommendation list including the target dish comprises: and acquiring historical dining records of the consumer user, selecting dishes appearing in the historical dining records from the target dishes, and generating a dish recommendation list containing the selected dishes.
4. The method of claim 1, wherein the step of generating the dish recommendation list including the target dish comprises: the method comprises the steps of obtaining historical dining records of a consumer user, counting dishes which appear at high frequency in the historical dining records, determining taste preference of the consumer user according to the dishes which appear at high frequency in the historical dining records, selecting dishes which accord with the taste preference of the consumer user from target dishes, and generating a dish recommendation list containing the selected dishes.
5. The method of claim 4, wherein the step of determining the taste preference of the consumer user based on the dishes that occur at a high frequency in the historical dining history comprises: and counting the cuisine and the mouthfeel of dishes which appear at high frequency in the historical dining record, and sequencing the cuisine and the mouthfeel in a descending order, so that the favorite cuisine and mouthfeel of the consumer user form the taste preference of the consumer user.
6. The method of claim 5, wherein the cuisine of the dish is assigned by the system or set by the merchant issuing the dish, and the mouthfeel of the dish is determined by a consumer user's taste vote value for the dish.
7. The method of claim 1, wherein the dish recommendation list contains order links corresponding to dishes in the dish recommendation list; after the step of pushing the dish recommendation list to the consumer user, the method further comprises: and when the customer user selects the order link, displaying an order interface of the dishes corresponding to the order link at the client of the customer user.
8. The method of claim 1, wherein after the step of pushing the menu recommendation list to the consumer user, further comprising: recording dishes in a dish recommendation list pushed to a consumer user each time to form a recommended dish set; recording the dishes actually selected by the consumer user in the dish recommendation list to form a dish set for placing the order; judging whether the dish with the occurrence frequency reaching the preset frequency in the dish recommendation list belongs to a dish set for placing an order or not, if not, determining that the dish is a dish which is not loved; before pushing the dish recommendation list to the consumer user next time, judging whether the dish recommendation list contains the dish which is not loved, and if so, removing the dish which is not loved from the dish recommendation list.
9. The method of claim 1, wherein the dish recommendation list contains only one dish randomly determined from the target dishes when the target dish has a plurality.
10. The method of claim 9, wherein after pushing the menu recommendation list to the consumer user, further comprising: and when the consumer user places an order to purchase the dishes in the dish recommendation list, randomly reducing the order amount below a preset limit.
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Application publication date: 20191206 |