CN113469796B - Method and system for recommending commodity combination - Google Patents
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Abstract
The embodiment of the specification provides a method for recommending commodity combinations. The method may include: acquiring commodity types and budget input by a user on a collection purchase platform, wherein the commodity types at least comprise agricultural products; determining flavor data and nutrition data corresponding to commodity types based on the growth data of the commodity on the set purchase platform corresponding to the commodity types; selecting candidate goods based on the flavor data and the nutritional data; one or more target commodity combinations are selected from the candidate commodities for recommendation to the user based at least on the budget.
Description
Technical Field
The present disclosure relates to the field of commodity purchasing, and in particular, to a method and system for recommending commodity combinations.
Background
Currently, online shopping has become an indispensable part of life of people, and users can purchase goods online. Because of the diversity of goods and the rapid pace of life of people, users may not have much time to read and compare the related information of each type of goods to be purchased, and when the user budget does not match with the price of the purchased goods, the users need more time to purchase.
It is therefore desirable to provide a method and system for recommending a combination of goods to meet different needs of a user and to save time costs for the user.
Disclosure of Invention
One of the embodiments of the present disclosure provides a method for combining recommended goods. The method comprises the following steps: acquiring commodity types and budget input by a user on a collection purchase platform, wherein the commodity types at least comprise agricultural products; determining flavor data and nutrition data corresponding to the commodity type based on the growth data of the commodity on the purchase platform corresponding to the commodity type; selecting a candidate commodity based on the flavor data and nutritional data; and selecting one or more target commodity combinations from the candidate commodities for recommendation to the user based at least on the budget.
One of the embodiments of the present specification provides a recommended merchandise combination system, the system including: the acquisition module is used for acquiring commodity types and budgets input by a user on the collection purchase platform, wherein the commodity types at least comprise agricultural products; the determining module is used for determining the flavor data and the nutrition data corresponding to the commodity type based on the growth data of the commodity on the set purchase platform corresponding to the commodity type; a selection module for selecting candidate goods based on the flavor data and the nutritional data; and a recommendation module for selecting one or more target commodity combinations from the candidate commodities for recommendation to the user based at least on the budget.
One of the embodiments of the present disclosure provides an apparatus for recommending commodity combinations, including a processor for executing a recommended commodity combination method.
One of the embodiments of the present disclosure provides a computer-readable storage medium storing computer instructions that, when read by a computer in the storage medium, perform operations corresponding to a method of recommending a combination of goods.
Drawings
The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:
FIG. 1 is a schematic illustration of an application scenario of a system for recommending commodity combinations according to some embodiments of the present description;
FIG. 2 is an exemplary flow chart of a method of recommending commodity combinations according to some embodiments of the present description;
FIG. 3 is an exemplary flow chart for obtaining flavor data and nutritional data according to some embodiments of the present description;
FIG. 4 is an exemplary flow chart of a method of selecting candidate merchandise according to some embodiments of the present description;
FIG. 5 is an exemplary block diagram of a system for recommending commodity combinations according to some embodiments of the present description.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.
It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies of different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.
As used in this specification and the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.
A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.
Fig. 1 is a schematic view of an application scenario of a system for recommending commodity combinations according to some embodiments of the present description.
In some application scenarios, the system 100 for recommending commodity combinations (simply referred to as system 100) may be used to recommend user target commodity combinations based on the user's selected commodity categories (e.g., agricultural products) and budgets. The user can purchase goods through the set purchase platform. The collection purchase platform refers to any platform providing commodity purchase services. In some application scenarios, when there is no match between the user's budget and the price of the purchased commodity, the system 100 may be used to recommend other commodities to the user, who balances the budget and the commodity price by purchasing the commodity in combination. In some application scenarios, when the user's budget exceeds a threshold set by the system, the user may be recommended some more expensive produce (e.g., fruit cans). The system for recommending commodity combinations 100 may be implemented to recommend commodity combinations for a user, meet different needs of the user, and save time and cost for the user by implementing the methods and/or processes disclosed herein.
In the application scenario shown in fig. 1, a system 100 for recommending commodity combinations according to some embodiments of the present description may include a server 110, a processor 120, a storage device 130, a user terminal 140, and a network 150.
The server 110 may communicate with the processor 120, the storage device 130, the user terminal 140, the means 160 for recommending a combination of goods through the network 150 to provide a function of recommending a combination of goods, and the storage device 130 may store all information of the recommended combination of goods. In some embodiments, user terminal 140 may provide for a user to purchase goods (e.g., agricultural products) on a platform (e.g., a set-top-box platform). The server 110 may process all data in the recommended merchandise combination process. The information transfer relationship between the above devices is merely an example, and the present application is not limited thereto.
In some embodiments, a storage device 130 may be included in server 110, user terminal 140, and possibly other system components.
In some embodiments, the processor 120 may be included in the server 110, the user terminal 140, and possibly other system components.
Server 110 may be used to manage resources and process data and/or information from at least one component of the present system or external data sources (e.g., a cloud data center). In some embodiments, the server 110 may be a single server or a group of servers. The server farm may be centralized or distributed (e.g., server 110 may be a distributed system), may be dedicated, or may be serviced concurrently by other devices or systems. In some embodiments, server 110 may be regional or remote. In some embodiments, server 110 may be implemented on a cloud platform or provided in a virtual manner. For example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-layer cloud, or the like, or any combination thereof.
Processor 120 may process data and/or information obtained from other devices or system components. The processor may execute program instructions to perform one or more of the functions described in this disclosure based on such data, information, and/or processing results. In some embodiments, processor 120 may include one or more sub-processing devices (e.g., single-core processing devices or multi-core processing devices). By way of example only, the processor 120 may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an Application Specific Instruction Processor (ASIP), a Graphics Processor (GPU), a Physical Processor (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), an editable logic circuit (PLD), a controller, a microcontroller unit, a Reduced Instruction Set Computer (RISC), a microprocessor, or the like, or any combination thereof.
Storage device 130 may be used to store data and/or instructions. Storage device 130 may include one or more storage components, each of which may be a separate device or may be part of another device. In some embodiments, the storage device 130 may include Random Access Memory (RAM), read Only Memory (ROM), mass storage, removable memory, volatile read-write memory, and the like, or any combination thereof. By way of example, mass storage may include magnetic disks, optical disks, solid state disks, and the like. In some embodiments, the storage device 130 may be implemented on a cloud platform.
Data refers to a digitized representation of information and may include various types such as binary data, text data, image data, video data, and the like. Instructions refer to programs that may control a device or apparatus to perform a particular function.
User terminal 140 refers to one or more terminal devices or software used by a user. In some embodiments, using the user terminal 140 may be one or more users for purchasing items on a set-top platform. In some embodiments, the user terminal 140 may be one or any combination of mobile device 140-1, tablet computer 140-2, laptop computer 140-3, desktop computer 140-4, and other input and/or output enabled devices.
In some embodiments, mobile device 140-1 may include a device for a user to purchase merchandise on a set purchase platform, a device to record merchandise growth data, and the like. Such as cell phones, soil monitoring data measuring devices, etc.
The above examples are only intended to illustrate the broad scope of the user terminal 140 devices and not to limit the scope thereof.
In some embodiments, the user terminal 140 may display one or a combination of several targeted items for recommendation to the user. One or more target commodity may be included in each target commodity combination. For example, commodity combinations X, Y, …, Z, etc., are recommended, wherein commodity combination X may include X1, X2, …, xn, etc.; the commodity combination Z may include Z1, Z2, …, zn, and the like. In some embodiments, the user terminal 140 may display recommended merchandise combinations and recommended values. For example, the recommended value of the recommended product combination X is Rx; the recommended value of the recommended commodity combination Y is Ry; the recommended value of the recommended commodity combination Z is Zy; the above description is intended to be illustrative only and not limiting.
Network 150 may connect components of the system and/or connect the system with external resource components. Network 150 enables communication between the various components and with other components outside the system to facilitate the exchange of data and/or information. In some embodiments, network 150 may be any one or more of a wired network or a wireless network. For example, the network 150 may include a cable network, a fiber optic network, a telecommunications network, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Public Switched Telephone Network (PSTN), a bluetooth network, a ZigBee network, a Near Field Communication (NFC), an intra-device bus, an intra-device line, a cable connection, and the like, or any combination thereof. The network connection between the parts can be in one of the above-mentioned ways or in a plurality of ways. In some embodiments, the network may be a point-to-point, shared, centralized, etc. variety of topologies or a combination of topologies. In some embodiments, network 150 may include one or more network access points. For example, the network 150 may include wired or wireless network access points, such as base stations and/or network switching points 150-1, 150-2, …, through which one or more components of the access point system 100 may connect to the network 150 to exchange data and/or information.
FIG. 2 is an exemplary flow chart of a method of recommending commodity combinations according to some embodiments of the present description. The process 200 includes the following steps.
Step 210, obtaining a commodity type and budget input by a user on a collection purchase platform, wherein the commodity type at least comprises agricultural products. In some embodiments, step 210 may be performed by the acquisition module 510.
In some embodiments, all of the items on the set purchase platform may be divided into one or more categories (e.g., a first category, a second category, a third category, etc.). For example only, the first category of merchandise may include agricultural products, agricultural product processing products, household products, cosmetic products, digital products, and the like, or combinations thereof. Taking the first category as an example of agricultural products, the second category of commodity products may include fruits, vegetables, grains, and the like, or combinations thereof. Taking the second category as an example of fruit, the third category of merchandise may include apples, bananas, oranges, and the like. The processed agricultural product refers to food prepared by taking agricultural products as main raw materials and adding one or more additives and auxiliary materials. The produce processed product may include, but is not limited to, dried fruit and vegetable products, preserved fruit, jam, canned fruit, pickled vegetables, and the like.
The commodity category refers to one or more categories of commodities classified on the set purchase platform or one or more categories of commodities input by a user. The commodities on the collection platform corresponding to the commodity type are all commodities on the collection platform corresponding to the commodity type. For example, the commodity type may be oranges, and the commodity on the purchase platform corresponding to the commodity type may be various oranges on the purchase platform and/or processed products of various oranges.
In some embodiments, the budget may be a number or a range of numbers. For example, the user may purchase a commodity orange with a budget of 50 or 30-60. In some embodiments, the budget may be a plurality of values, or a plurality of value ranges, each corresponding to a plurality of merchandise types. For example, the commodity types input by the user are watermelon and banana, and the budget can be 50 yuan for watermelon and 30 yuan for banana; the budget can also be 40-70 yuan for watermelon and 20-50 yuan for banana. In some embodiments, the budget may also include a budget proportion. For example, the types of merchandise purchased by the user are apples, oranges and watermelons, the total budget is 200 yuan, the apple budget is 30%, the orange budget is 30%, and the watermelon budget is 50%. The above description is by way of example only and not limitation, and the budget may in other cases also be displayed in other forms.
In some embodiments, the acquisition module 510 may acquire the type of merchandise and the budget entered by the user on the set purchase platform. In some embodiments, the user may enter the merchandise category and budget in a variety of ways. For example, the user may manually input, voice input, etc. The acquisition module 510 acquires the commodity kind and budget by identifying the input mode and the input content of the user. In some embodiments, the acquisition module 510 may acquire the item category and budget by acquiring options entered by the user. For example, the relevant merchandise types and budgets on the set purchase platform may be options, and the user enters the merchandise types and budgets by selecting different options.
And 220, determining flavor data and nutrition data corresponding to the commodity types based on the growth data of the commodity on the purchase platform corresponding to the commodity types. In some embodiments, step 220 may be performed by determination module 520.
In some embodiments, the growth data refers to data related to the growth of commodity (e.g., agricultural product). In some embodiments, the growth data may include at least diurnal temperature difference data, rainfall data, insolation data, soil monitoring data, harvesting data, etc. during growth. For example, soil monitoring data refers to data relating to soil for commercial growth. Soil monitoring data may include phosphorus content, potassium content, water content, etc. of the soil. Picking data refers to data related to picking of the merchandise. The picking data may include time of merchandise picking, time interval from merchandise fruit to picking, color, weight, shape, etc. of merchandise picking. In some embodiments, the growth data may also include a time at which the growth data was recorded, e.g., a start recording time (e.g., planting time, fruit bearing time), a cut-off recording time (e.g., picking time), a recording time point (e.g., a time point at which the respective growth data was recorded), and so forth.
The agricultural products and processed products thereof on the collection platform can comprise growth data and are recorded in commodity information. In some embodiments, the growth data may be obtained automatically, e.g., server 110 may obtain diurnal temperature difference data, rainfall data, insolation data, etc., for commodity growth areas over network 150 and store in storage device 130. In some embodiments, the growth data may be manually recorded and server 110 may obtain the growth data entered by the merchandiser. For example, soil monitoring data of commodity is manually measured and recorded by a soil monitoring data measuring device.
In some embodiments, the flavor data refers to data related to the flavor of the merchandise. The flavor data may include sweetness data for the commodity, acidity data for the commodity, bitterness data for the commodity, and/or pungency data for the commodity, etc. In some embodiments, the flavor data may be represented in values (e.g., a number or range of values), ratings, and/or symbols (e.g., asterisks). In some embodiments, the flavor data may be positively or negatively correlated with the flavor of the merchandise. For example, if the flavor data is positively correlated with the flavor of the commodity, the higher the sweetness of the commodity, the greater the sweetness data of the commodity, the greater the bitterness data of the commodity, and/or the greater the pungency data of the commodity (e.g., the greater the number, the higher the grade, the more the symbol, etc., the greater the acidity data of the commodity). Taking sweetness data as an example, the sweetness of a commodity may be expressed in terms of 0-10, 1-5 grades, 1-5 symbols.
In some embodiments, the nutritional data refers to data related to commercial nutrition. The nutritional data of the commodity may include the type of nutritional substances contained in the commodity, the contents or ranges of contents of various nutritional substances, the nutritional efficacy of the commodity, and the like. The nutritional data may include data on the content of vitamins, minerals, proteins, cellulose, etc. contained in the commodity. In some embodiments, the nutritional data may be a numerical value or a range of numerical values. For example, apple may have a vitamin C content of 85mg/100g or 50-130mg/100g. In some embodiments, the nutritional efficacy corresponding to the commodity may be prompted based on the nutritional data of the commodity. For example, commodities with higher water-soluble dietary fiber content are helpful for regulating immune system functions and promoting the discharge of toxic heavy metals in the body; the commodity with higher vitamin E content can resist aging, improve the immunity of organisms and prevent cardiovascular diseases; the commodity with higher vitamin B2 content can promote development and regeneration of cells, improve vision, etc. The prompting mode includes, but is not limited to, direct display on an application program interface used by a user, transmission to the user through a message form, and the like, and the specific mode can be determined according to actual conditions.
In some embodiments, the nutritional data corresponding to the commodity category may also be directly obtained based on the commodity category. For example, if the commodity type is apple, the corresponding nutritional data is vitamin B1, vitamin B2, vitamin C, malic acid, phosphorus, calcium, potassium, and the like; if the commodity is mangosteen, the corresponding nutritional data are vitamin B1, vitamin B2, vitamin C4, minerals, citric acid and the like.
In some embodiments, the corresponding nutritional efficacy of the commodity may be suggested based on the type of commodity. For example, apples have the effects of moistening lung, clearing intestines and stomach, maintaining beauty and keeping young, losing weight and the like; durian can promote appetite, promote digestion, invigorate spleen, tonify kidney, enhance immunity and the like; mangosteen has effects of clearing heat, removing internal heat, relieving cough, and relieving vomiting; the banana has the effects of accelerating gastrointestinal peristalsis, promoting digestion, maintaining beauty and keeping young, promoting fluid production to quench thirst, reducing blood pressure, assisting sleep and the like. The prompting mode includes, but is not limited to, direct display on an application program interface used by a user, transmission to the user through a message form, and the like, and the specific mode can be determined according to actual conditions.
In some embodiments, the determining module 520 may determine the flavor data and the nutritional data corresponding to the commodity category based on the growth data of all commodities on the set purchase platform corresponding to the commodity category. The flavor data and the nutrition data corresponding to the commodity type can be determined by the flavor data and the nutrition data of all commodities on the set purchase platform corresponding to the commodity type. In some embodiments, flavor data and nutritional data for all agricultural products and their processed products on the set-top platform may be determined based on the commodity's growth data by a flavor data determination model and a nutritional data determination model, respectively. The determined flavor data and nutrition data of each commodity are recorded in the product information of each commodity together with the growth data and are prestored in the system 100. Details of the determination of the flavor data and the nutritional data by the flavor data determination model and the nutritional data determination model can be referred to fig. 3 and the description thereof. For example, the flavor data and the nutrition data corresponding to the product type may be the average value and the variance value of the flavor data and the nutrition data of all the products on the purchase platform corresponding to the product type. For example, the flavor data and the nutrition data corresponding to the product type may be ranges including flavor data and nutrition data of all products on the purchase platform corresponding to the product type.
In some embodiments, the determining module 520 may determine the flavor data and the nutritional data corresponding to the commodity category by, for example, taking an average of the growth data of all commodities on the set purchase platform corresponding to the commodity category and inputting the average into the flavor data determining model and the nutritional data determining model.
For example, the commodity type input by the user is apples, and the determining module 520 may determine that the sweetness of the flavor data corresponding to the apples is 6-8 degrees according to the growth data of the apples on the purchase platform. In some embodiments, the determination module 520 may determine flavor data from the product information. For example, flavor data of processed agricultural products. In some embodiments, the determining module 520 may estimate the flavor data via a flavor data model, see FIG. 3 and related description.
Step 230, selecting candidate goods based on the flavor data and the nutritional data. In some embodiments, step 230 may be performed by selection module 530.
Candidate items are items that are likely to be selected for recommendation to the user. For example, the product type input by the user is apple, and the candidate product may be red Fuji, snake fruit, green apple, processed product of apple, or the like. In some embodiments, the candidate commodity may include a commodity that is the same as or similar to the flavor data and/or nutritional data of the commodity category entered by the user. For example, the type of commodity input by the user is apple, and the candidate commodity may be other fruits which are the same as or similar to the flavor data and nutrition data of apple, such as apple pear, etc.
In some embodiments, the selection module 530 may select candidate merchandise by the category of merchandise entered by the user, e.g., the category of merchandise entered by the user is apple, and the selection module 530 may select apple and apple processed as candidate merchandise. In some embodiments, the selection module 530 may select candidate merchandise by matching the flavor data and/or nutritional data of the category of merchandise entered by the user with the flavor data and/or nutritional data of the merchandise on the set purchase platform.
In some embodiments, the selection module 530 may select candidate merchandise based on the flavor data and nutritional data for the category of merchandise. The selection module 530 matches flavor data and nutrition data corresponding to the commodity category inputted by the user with flavor data and nutrition data of all commodities. For example, when the matching result satisfies a preset condition, the selection module 530 selects the commodity satisfying the preset condition as the candidate commodity.
For more details regarding the selection of candidate merchandise, reference is made to FIG. 4 and the associated description thereof, and further details are omitted herein.
Step 240, selecting one or more target commodity combinations from the candidate commodities to recommend to the user based at least on the budget. In some embodiments, step 240 may be performed by recommendation module 540.
The target commodity refers to one or more commodities selected from the candidate commodities and recommended to the user. The target commodity combination may include one or more target commodities.
In some embodiments, the recommendation module 540 may select one or more target commodity combinations from the candidate commodities based on the user budget.
In some embodiments, the recommendation module 540 may preset a preset threshold for different items, and the user budget may be within the preset threshold or may exceed the preset threshold. The preset threshold may be automatically determined or manually confirmed by the system 100. In some embodiments, the user budget is within a preset threshold, and the recommendation module 540 may recommend other ones of the candidate items to the user as target items in the combination. For example, the commodity type input by the user is apple, the user budget is 40 yuan, the preset threshold is 60 yuan, and the recommendation module 540 may recommend apple pears in the candidate commodities to the user as a part of the target commodities in the combination. In some embodiments, the user budget exceeds a preset threshold, and the recommendation module 540 may recommend other items and/or item artifacts, etc. in the candidate items to the user as part of the target items in the combination. For example, the user may input an apple category, a user budget of 100 yuan, a preset threshold of 60 yuan, and the recommendation module 540 may recommend apple pears and/or apple processed products among candidate products to the user as a part of the target products in the combination. In some embodiments, the recommendation module 540 may recommend the item artifacts in the candidate items to the user as part of the target items in the combination when the user budget is high. For example, the user may input an apple product type, a user budget of 150 yuan, a preset threshold of 60 yuan, and the recommendation module 540 may recommend apple products and/or other products among the candidate products to the user as a part of the target products in the combination. The recommendation module 540 may also recommend other ones of the candidate items to the user as part of the target items in the combination, by way of example only and not limitation.
In some embodiments, the recommendation module 540 may obtain the user's taste through a record of the user's purchase or by other means. The recommendation module 540 may select one or more target commodity combinations from the candidate commodities based on the user taste and budget. In some embodiments, the recommendation module 540 may select one or more target commodity combinations of higher nutritional value from the candidate commodities based on the user's taste and budget. In some embodiments, the recommendation module 540 may select one or more target commodity combinations from the candidate commodities that have similar flavor data based on the user's tastes and budgets.
In some embodiments, the recommendation module 540 may select, from among the candidate items, items in the user purchase record that occur frequently as part of the target items in the combination when the user budget is within a preset threshold.
In some embodiments, the target combination of items recommended to the user by the recommendation module 540 may be one or more combinations. In some embodiments, the recommendation module 540 may determine a recommendation value for each of the one or more target commodity combinations.
In some embodiments, the recommendation module 540 may derive a recommendation value for each target commodity by scoring each target commodity.
In some embodiments, the recommended value for each target commodity may be determined by the following formula:
equation 1
Wherein the historical score may be determined from the ratings in the user's historical purchase record; the coefficient A is a numerical value between 0 and 1, the coefficient A can be determined by the nutrition data difference between the target commodity and the commodity type input by the user, and the smaller the nutrition data difference is, the closer A is to 1; the coefficient B is a numerical value between 0 and 1, the coefficient B can be determined by the prices of all commodities of the target commodity combination and the commodity budget input by the user, and the closer the preset threshold is to the commodity budget ratio (20%) input by the user, the closer B is to 1.
In some embodiments, the recommendation module 540 may continuously update the history score based on the ratings in the user's incremental purchase records.
In some embodiments, the recommendation module 540 may score each target commodity combination to obtain a recommendation value for each target commodity combination. For example, the recommendation module 540 may average the recommendation value for each target commodity in the combination to obtain a recommendation value for each target commodity combination.
In some embodiments, the recommendation module 540 presents each target commodity combination and its recommendation value to the user. For example, the recommendation module 540 displays each target commodity combination and its recommendation value to the user, who makes a selection of combinations based on each target commodity combination and its recommendation value. And evaluating and scoring the target commodity combination through the formula 1, and displaying the target commodity combination on a terminal for a user to select a proper target commodity, so that the user can more intuitively compare each target commodity combination, and the user is facilitated.
In some embodiments of the present disclosure, based on the recommended value of each target commodity combination, the user can clearly understand the score of each target commodity combination, so as to facilitate the selection of the user and save time and cost for the user.
Fig. 3 is an exemplary flow chart for obtaining flavor data and nutritional data according to some embodiments of the present description.
In step 310, the growth data is input into the flavor data model, and flavor data corresponding to the product type is obtained. In some embodiments, step 310 may be performed by determination module 520.
In some embodiments, the machine learning model may be used as the flavor data model to determine the flavor data corresponding to the product type.
In some embodiments, the flavor data model may include a trained machine learning model that may include various models or structures, such as a deep neural network model, a recurrent neural network model, a custom model structure, etc., the selection of which may be contingent on the particular situation.
The input of the flavor data model may include growth data, such as, for example, day-night temperature differences, insolation, soil monitoring data, picking time, etc. The soil monitoring data may include, among other things, phosphorus content, potassium content, water content, etc. The output of the flavor data model may include flavor data, which may be represented in numerical values, scales, and/or symbols.
In some embodiments, the initial flavor data model may be trained using sample data, resulting in a trained flavor data model. The sample data may include sample input data and sample output data of the commodity. The sample input data may include sample growth data. The sample output data may be sample flavor data. The sample flavor data may be manually labeled, e.g., manually labeled acidity values, sweetness values, etc. For example, if not sweet, the sweetness value may be marked as 0; if perceived as particularly sweet, the sweetness value may be marked as 10. And the trained flavor data model can be tested through the test data, the flavor data output by the trained flavor data model is compared with the flavor data in the test data to obtain a difference value, and when the difference value is smaller than a model threshold value, the training is completed to obtain the flavor data model. In some embodiments, the test data may be part of the sample data.
In some embodiments, the flavor data may be displayed directly in the client's application interface. In some embodiments, the flavor data may be sent to the user in the form of a message. For example, the prompt message is sent to the client of the user. In some embodiments, the flavor data may be transferred to a storage device for storage. The storage device can be a storage device of a recommended commodity combination system, or can be a storage device outside the system. Such as an optical disc, a hard disk, etc. In some embodiments, the flavor data may be passed to specific interfaces including, but not limited to, a program interface, a data interface, a transmission interface, and the like. In some embodiments, the results of the flavor data may also be output in any manner known to those skilled in the art, as the case may be.
Input data and output data (e.g., sample data and/or test data, growth data for the commodity, etc.) for the model may be retrieved from memory. The storage device may be a storage device 130 of the system 100 for recommending the commodity combination, or may be an external storage device of the system 100 not belonging to the commodity combination, for example, a hard disk, a usb disk, or the like. In some embodiments, the sample data and/or test data may also be read through interfaces including, but not limited to, a program interface, a data interface, a transport interface, and the like. In some embodiments, the system 100 for recommending commodity combinations operates by automatically extracting input data for a flavor data model from the interface. In some embodiments, the system 100 of recommending a combination of goods may be invoked by an external other device or system, at which time input data of the flavor data model is passed to the system 100 of recommending a combination of goods. In some embodiments, the input data of the flavor data model may also be obtained in any manner known to those skilled in the art, as may be determined as appropriate. The flavor data model may be trained in additional devices or modules.
In some embodiments, the commodity-type flavor data may be output when the commodity-type growth data is input into the flavor data model. The growth data of the commodity category may be a numerical range or a numerical value (e.g., an average value) of the growth data of all commodities of the commodity category on the purchase platform, and accordingly, the output flavor data may be a numerical range or a numerical value.
And 320, inputting the growth data into a nutrition data model to obtain nutrition data corresponding to the commodity type. In some embodiments, step 310 may be performed by determination module 520.
In some embodiments, a machine learning model may be used as the nutritional data model to determine nutritional data corresponding to the commodity category.
In some embodiments, the nutritional data model may be the same as or different from the machine learning model employed by the flavor data model.
The input of the nutrition data model is the same as the input of the flavor data model and may include growth data such as day and night temperature difference, amount of sunlight, soil monitoring data, picking time, etc. The soil monitoring data may include, among other things, phosphorus content, potassium content, water content, etc. The output of the nutritional data model may include nutritional data, such as individual nutritional levels and their values.
In some embodiments, the initial nutritional data model may be trained using sample data, resulting in a trained nutritional data model. The sample data may include sample input data and sample output data of the commodity. The sample input data may include sample growth data. The sample output data may be sample nutritional data. The sample nutritional data may be manually labeled or detected. For example, each commodity contains a nutrient (e.g., protein, etc.) and its content. And the trained nutrition data model can be tested through the test data, the nutrition data output by the trained nutrition data model is compared with the nutrition data in the test data to obtain a difference value, and when the difference value is smaller than a model threshold value, the training is completed to obtain the nutrition data model. In some embodiments, the test data may be part of the sample data.
In some embodiments, the commodity category nutrient data may be output when the commodity category growth data is input into the nutrient data model. The nutritional data for the commodity category may be a range of values or a number (e.g., an average) of the growth data for all commodities of the commodity category on the set purchase platform, and the nutritional data output may be a range of values or a number, respectively.
In some embodiments, the nutritional data may be displayed directly in the client's application interface. The nutritional data may be included in product information for each commodity. In some embodiments, the nutritional data may be transferred to a storage device for storage. The storage device can be a storage device of a recommended commodity combination system, or can be a storage device outside the system. Such as an optical disc, a hard disk, etc. In some embodiments, the nutritional data may be passed to specific interfaces including, but not limited to, program interfaces, data interfaces, transmission interfaces, and the like. In some embodiments, the results of the nutritional data may also be output in any manner known to those skilled in the art, as the case may be.
Input data and output data (e.g., sample data and/or test data, growth data for the commodity, etc.) for the model may be retrieved from memory. The storage device may be a storage device 130 of the system 100 for recommending the commodity combination, or may be an external storage device of the system 100 not belonging to the commodity combination, for example, a hard disk, a usb disk, or the like. In some embodiments, the sample data and/or test data may also be read through interfaces including, but not limited to, a program interface, a data interface, a transport interface, and the like. In some embodiments, the system 100 for recommending commodity combinations operates by automatically extracting input data for a flavor data model from the interface. In some embodiments, the system 100 of recommending a combination of goods may be invoked by an external other device or system, at which time input data of the flavor data model is passed to the system 100 of recommending a combination of goods. In some embodiments, the input data of the nutritional data model may also be obtained in any manner known to those skilled in the art, as may be determined as appropriate. The nutritional data model may be trained in additional devices or modules.
In some embodiments, the flavor data and the nutritional data of the commodity may be determined simultaneously by one model (e.g., flavor and nutritional determination model). For example, growth data of commercial products is input into the model, while flavor data and nutrition data are output. In some embodiments, the model may be trained by taking sample growth data as input, sample flavor data and sample nutrition data as output.
FIG. 4 is an exemplary flow chart of a method of selecting candidate merchandise according to some embodiments of the present description. The process 400 includes the steps of:
step 410, obtaining growth data of all commodities on the set purchase platform. In some embodiments, step 410 may be performed by the acquisition module 510.
In some embodiments, the growth data for all of the commodity may be obtained by manual input, may be obtained in server 110 via network 150, or may be obtained in other ways. For more details on the growth data, see the acquisition of input data and its associated description in step 310 of FIG. 3.
Step 420, determining flavor data and nutritional data for all commodities based on the growth data for all commodities. In some embodiments, step 420 may be performed by determination module 520.
In some embodiments, the flavor data may be determined by a flavor data model. In some embodiments, the nutritional data may be determined by a nutritional data model. For example, the growth data of each commodity is input into a flavor data model and a nutrition data model, respectively, to obtain flavor data and nutrition data. For more details on determining flavor data and determining nutritional data see fig. 3 and its associated description.
And step 430, matching the flavor data and the nutrition data corresponding to the commodity types with the flavor data and the nutrition data of all commodities. In some embodiments, step 430 may be performed by selection module 530.
In some embodiments, the flavor data and the nutrition data corresponding to the commodity category may be matched with the data of all commodities on the set purchase platform to determine candidate commodities meeting the preset condition.
In some embodiments, all the commodities may be pre-classified based on the flavor data and the nutrition data, the flavor data and the nutrition data corresponding to the commodity types are matched with commodity information in the pre-classified commodity combination, and the pre-classified commodity combination meeting the preset condition is selected from one or more pre-classified commodity combinations to be used as a candidate commodity.
In some embodiments, after the flavor data and the nutrition data of all the commodities are obtained, the flavor data and/or the nutrition data can be pre-classified to obtain a pre-classified commodity combination, and after the user inputs the commodity types, the flavor data and the nutrition data corresponding to the commodity types are matched with the pre-classified commodity combination.
In some embodiments, all the commodities may be pre-classified according to the flavor data, and the flavor data corresponding to each group of commodities obtained by classification is recorded as a classified label in the pre-classified commodity information of each group of commodities. In some embodiments, products with similar or identical flavor data may be classified as, for example, apples and dried apples, oranges and oranges, and the like. In some embodiments, products that differ in flavor but are suitable for collocation can be classified into one category, for example, passion fruit and pineapple, kumquat, lemon, and the like. The above examples are for illustration only and not limitation, and the combination of articles pre-sorted may include only one article, as well as at least one article, depending on the actual sort.
In some embodiments, all the commodities may be classified according to the nutrition data, and the nutrition data corresponding to each group of commodities obtained by classification is recorded in the classification information of each group of commodities as a classification label. In some embodiments, the products for which the nutritional data is suitable for collocation can be classified into one category, for example, apples and pears, which can promote the production of body fluid to quench thirst, moisten lung and remove noise. In some embodiments, the products with similar nutritional data may be classified as, for example, lemon and orange, each of which is rich in vitamin C. The above examples are for illustration only and not limitation, and the combination of articles pre-sorted may include only one article, as well as at least one article, depending on the actual sort.
In some embodiments, the products may also be pre-categorized according to the combination of flavor data and nutritional data. For example, products with similar flavor data are selected, and among the products with similar flavor data, products with higher nutritional value are preferably selected. In some embodiments, when matching and combining commodities based on flavor data and nutritional data, commodities with similar flavor data are preferentially selected.
The flavor data and/or the nutrition data are pre-classified, and commodities are selected from the pre-classified commodities and recommended to users, so that the method is faster and more convenient. The user can intuitively see pre-classified commodities with similar flavor data and/or nutrition data, and the screening of the user is facilitated.
Step 440, if the matching result meets the preset condition, selecting the commodity meeting the preset condition as the candidate commodity. In some embodiments, step 440 may be performed by selection module 530.
In some embodiments, the preset condition may be that the similarity of the flavor data and/or the nutrition data of the commodity category input by the user and the flavor data and the nutrition data of a certain commodity in all commodities is less than the first flavor similarity threshold and/or the first nutrition similarity threshold. In some embodiments, the first flavor similarity threshold may be the same as or different from the first nutritional similarity threshold. And selecting the commodity as a candidate commodity when the similarity of the flavor data and/or the nutrition data of the commodity is smaller than the flavor similarity threshold value and/or the nutrition similarity threshold value.
In some embodiments, the preset condition may be that the user-entered flavor data and/or nutritional data of the commodity category has a similarity to the flavor data and nutritional data of the pre-classified commodity category that is less than a second flavor similarity threshold and/or a second nutritional similarity threshold. In some embodiments, the second flavor similarity threshold may be the same as or different from the second nutritional similarity threshold. The second flavor similarity threshold and/or the second nutritional similarity threshold may be the same as or different from the first flavor similarity threshold and/or the first nutritional similarity threshold. In some embodiments, the flavor data and nutritional data of the pre-classified commodity category may be a value (e.g., average) or range of flavor data and/or nutritional data of the commodity under that category. And when the similarity of the flavor data and/or the nutrition data of the pre-classified commodity category is smaller than the second flavor similarity threshold value and/or the second nutrition similarity threshold value, selecting all or part of commodities in the pre-classified commodity category as candidate commodities.
FIG. 5 is an exemplary block diagram of an apparatus for recommending commodity combinations according to some embodiments of the present description.
As shown in fig. 5, the apparatus 160 for recommending a commodity combination may include: the acquisition module 510, the determination module 520, the selection module 530, and the recommendation module 540.
The obtaining module 510 may be configured to obtain a commodity category and a budget input by a user on the purchase collection platform, where the commodity category includes at least agricultural products. In some embodiments, the obtaining module 510 is further configured to obtain growth data for all of the items on the set purchase platform.
The determining module 520 is configured to determine flavor data and nutrition data corresponding to the commodity type based on the growth data of the commodity on the purchase platform corresponding to the commodity type. In some embodiments, the determining module 520 is further configured to input the growth data into a flavor data model to obtain flavor data corresponding to the commodity category. In some embodiments, the determining module 520 is further configured to input the growth data into a nutritional data model to obtain nutritional data corresponding to the commodity category. In some embodiments, the determination module 520 is further configured to determine flavor data and nutritional data for all commodities based on the growth data for all commodities.
A selection module 530 for selecting candidate merchandise based on the flavor data and nutritional data. In some embodiments, the selection module 530 is further configured to match the flavor data and the nutrition data corresponding to the commodity types input by the user with the flavor data and the nutrition data of all commodities, and if the matching result meets the preset condition, select the commodity meeting the preset condition as the candidate commodity.
A recommendation module 540 for selecting one or more target commodity combinations from the candidate commodities for recommendation to the user based at least on the budget. In some embodiments, the recommendation module 540 is further configured to determine a recommendation value for each of the one or more target commodity combinations. In some embodiments, the recommendation module 540 is also configured to present each target commodity combination and its recommendation value to the user.
It should be noted that the above description of the system for recommending commodity combinations and its modules is for convenience of description only and is not intended to limit the present description to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the principles of the system, various modules may be combined arbitrarily or a subsystem may be constructed in connection with other modules without departing from such principles. In some embodiments, the acquisition module, determination module, selection module, and recommendation module disclosed in fig. 5 may be different modules in a system, or may be one module to implement the functions of two or more modules described above. For example, each module may share one memory module, or each module may have a respective memory module. Such variations are within the scope of the present description.
While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present invention.
Meanwhile, the specification uses specific words to describe the embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present description. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.
Furthermore, the order in which the elements and sequences are processed, the use of numerical letters, or other designations in the description are not intended to limit the order in which the processes and methods of the description are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present disclosure. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.
Likewise, it should be noted that in order to simplify the presentation disclosed in this specification and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure, however, is not intended to imply that more features than are presented in the claims are required for the present description. Indeed, less than all of the features of a single embodiment disclosed above.
In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the number allows for a 20% variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.
Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., referred to in this specification is incorporated herein by reference in its entirety. Except for application history documents that are inconsistent or conflicting with the content of this specification, documents that are currently or later attached to this specification in which the broadest scope of the claims to this specification is limited are also. It is noted that, if the description, definition, and/or use of a term in an attached material in this specification does not conform to or conflict with what is described in this specification, the description, definition, and/or use of the term in this specification controls.
Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.
Claims (5)
1. A method of recommending a combination of goods, comprising:
Acquiring commodity types and budget input by a user on a collection purchase platform, wherein the commodity types at least comprise agricultural products;
inputting the growth data into a flavor data model to obtain flavor data corresponding to the commodity type;
inputting the growth data into a nutrition data model to obtain nutrition data corresponding to the commodity type;
the growth data at least comprises day-night temperature difference, sunshine amount, soil monitoring data and picking data in the commodity growth process;
the flavor data model and the nutrition data model are trained machine learning models;
the training of the flavor data model comprises: training an initial flavor data model by using first sample data to obtain a trained flavor data model, testing the trained flavor data model by using first test data, and obtaining the flavor data model after training when the difference value between the flavor data output by the trained flavor data model and the flavor data in the first test data is smaller than a first model threshold value;
the first sample data includes first sample input data and first sample output data of the commodity, the first sample input data including sample growth data; the first sample output data is sample flavor data;
Training of the nutritional data model includes: training the initial nutrition data model by using the second sample data to obtain a trained nutrition data model; testing the trained nutrition data model through second test data, and when the difference value between the nutrition data output by the trained nutrition data model and the nutrition data in the second test data is smaller than a second model threshold value, completing training to obtain the nutrition data model;
the second sample data comprises second sample input data and second sample output data of the commodity, the second sample input data comprising the sample growth data; the second sample output data is sample nutritional data;
selecting a candidate commodity based on the flavor data and nutritional data; and
selecting one or more target commodity combination recommendations from the candidate commodities to the user based at least on the budget, including:
determining a recommended value for each of the one or more target commodity combinations;
presenting each target commodity combination and its recommended value to the user;
wherein the recommended value of each target commodity combination is determined based on an average value of the recommended values of the target commodities in the target commodity combination, and the recommended value of each target commodity is determined by the following formula:
Recommendation value for target commodity = history scoreCoefficient A->A coefficient B;
the historical score is determined based on the evaluations in the user's historical purchase record; the coefficient A is a numerical value between 0 and 1, the coefficient A is determined by the nutrition data difference between the target commodity and the commodity type input by a user, and the smaller the nutrition data difference is, the closer the coefficient A is to 1; and the coefficient B is a numerical value between 0 and 1, and is determined by the prices of all commodities of the target commodity combination and the commodity budget input by the user, and the closer the preset threshold value is to the commodity budget ratio input by the user, the closer the coefficient B is to 1.
2. The method of claim 1, wherein selecting a candidate commodity based on the flavor data and nutritional data comprises:
acquiring growth data of all commodities on the set purchase platform;
determining flavor data and nutritional data for the entire commodity based on the growth data for the entire commodity;
matching the flavor data and the nutrition data corresponding to the commodity types with the flavor data and the nutrition data of all commodities; and
and if the matching result meets the preset condition, selecting the commodity meeting the preset condition as a candidate commodity.
3. A system for recommending a combination of goods, comprising:
the acquisition module is used for acquiring commodity types and budgets input by a user on the collection purchase platform, wherein the commodity types at least comprise agricultural products;
the determining module inputs the growth data into the flavor data model to obtain flavor data corresponding to the commodity type;
inputting the growth data into a nutrition data model to obtain nutrition data corresponding to the commodity type;
the growth data at least comprises day-night temperature difference, sunshine amount, soil monitoring data and picking data in the commodity growth process;
the flavor data model and the nutrition data model are trained machine learning models;
the training of the flavor data model comprises: training an initial flavor data model by using first sample data to obtain a trained flavor data model, testing the trained flavor data model by using first test data, and obtaining the flavor data model after training when the difference value between the flavor data output by the trained flavor data model and the flavor data in the first test data is smaller than a first model threshold value;
the first sample data includes first sample input data and first sample output data of the commodity, the first sample input data including sample growth data; the first sample output data is sample flavor data;
Training of the nutritional data model includes: training the initial nutrition data model by using the second sample data to obtain a trained nutrition data model; testing the trained nutrition data model through second test data, and when the difference value between the nutrition data output by the trained nutrition data model and the nutrition data in the second test data is smaller than a second model threshold value, completing training to obtain the nutrition data model;
the second sample data comprises second sample input data and second sample output data of the commodity, the second sample input data comprising the sample growth data; the second sample output data is sample nutritional data;
a selection module for selecting candidate goods based on the flavor data and the nutritional data; and
a recommendation module for selecting one or more target commodity combinations from the candidate commodities for recommendation to the user based at least on the budget, comprising:
a recommendation value for determining each of the one or more target commodity combinations;
presenting each target commodity combination and its recommended value to the user;
wherein the recommended value of each target commodity combination is determined based on an average value of the recommended values of the target commodities in the target commodity combination, and the recommended value of each target commodity is determined by the following formula:
Recommendation value for target commodity = history scoreCoefficient A->A coefficient B;
the historical score is determined based on the evaluations in the user's historical purchase record; the coefficient A is a numerical value between 0 and 1, the coefficient A is determined by the nutrition data difference between the target commodity and the commodity type input by a user, and the smaller the nutrition data difference is, the closer the coefficient A is to 1; and the coefficient B is a numerical value between 0 and 1, and is determined by the prices of all commodities of the target commodity combination and the commodity budget input by the user, and the closer the preset threshold value is to the commodity budget ratio input by the user, the closer the coefficient B is to 1.
4. An apparatus for recommending a combination of goods, the apparatus comprising a processor and a memory for storing instructions, the processor for executing the instructions to implement operations corresponding to the method for recommending a combination of goods of any of claims 1-2.
5. A computer readable storage medium storing computer instructions which, when executed by a processor, implement operations corresponding to a method of recommending commodity combinations according to any one of claims 1 to 2.
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