CN111414551A

CN111414551A - Information acquisition method, information recommendation method and device and electronic equipment

Info

Publication number: CN111414551A
Application number: CN202010105303.5A
Authority: CN
Inventors: 施雪冰; 张明辉
Original assignee: Beijing Sankuai Online Technology Co Ltd
Current assignee: Beijing Sankuai Online Technology Co Ltd
Priority date: 2020-02-20
Filing date: 2020-02-20
Publication date: 2020-07-14

Abstract

The embodiment of the application discloses an information acquisition method, an information recommendation device and electronic equipment. The embodiment of the information acquisition method comprises the following steps: obtaining a food ordering request through a first application; determining a target store indicated by the ordering request, and acquiring related data of dishes in the target store from the second application; based on the relevant data, information related to dishes in the target store is presented in the first application. The method and the device shorten the information acquisition time of the user in the ordering process, and are beneficial to improving the ordering efficiency.

Description

Information acquisition method, information recommendation method and device and electronic equipment

Technical Field

The embodiment of the application relates to the technical field of computers, in particular to an information acquisition method, an information recommendation device and electronic equipment.

Background

In epidemic spread period or some infectious disease high-incidence environment, there is a risk that virus and bacteria are transmitted through such routes as droplets in the process of talking with others. For example, in a conventional ordering process, a customer needs to talk to the staff of a restaurant in close proximity, and when either person becomes ill, the other person talking to him is at risk of infection. Thus, the problem of safe dining is receiving wide attention.

In the prior art, when a user visits a certain store for the first time or does not know dishes in the certain store, the user usually needs to communicate with a worker, or another companion uses another device to synchronously acquire related information of the dishes in the store to assist in ordering. Therefore, under the condition, the dining safety cannot be guaranteed, and the user needs to spend longer time in the process of ordering to acquire information, so that the ordering efficiency is lower.

Disclosure of Invention

The embodiment of the application provides an information acquisition method, an information recommendation device and electronic equipment, so that the dining safety is guaranteed, the information acquisition time and the decision time of a user in the ordering process are shortened, and the ordering efficiency is improved.

In a first aspect, an embodiment of the present application provides an information obtaining method, where the method includes: obtaining a food ordering request through a first application; determining a target store indicated by the ordering request, and acquiring related data of dishes in the target store from the second application; based on the relevant data, information related to dishes in the target store is presented in the first application.

In a second aspect, an embodiment of the present application provides an information recommendation method, where the method includes: receiving a food ordering request for a target store; acquiring evaluation data of a historical client on dishes in a target store; determining a customer recommended dish based on the evaluation data; and returning to a meal ordering page containing the dish information of the dish recommended by the client.

In a third aspect, an embodiment of the present application provides an information acquiring apparatus, including: a first obtaining unit configured to obtain an ordering request through a first application; a second acquisition unit configured to determine a target store indicated by the ordering request and acquire data on dishes in the target store from a second application; a presentation unit configured to present information related to dishes in the target store in the first application based on the related data.

In a fourth aspect, an embodiment of the present application provides an information recommendation apparatus, where the apparatus includes: a receiving unit configured to receive an order request for a target store; an acquisition unit configured to acquire evaluation data of dishes in a target store by a history customer; a determination unit configured to determine a customer recommended dish based on the evaluation data; and the return unit is configured to return an ordering page containing the dish information of the dish recommended by the client.

In a fifth aspect, an embodiment of the present application provides an electronic device, including: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors, cause the one or more processors to implement the information recommendation method as described in the first aspect.

In a sixth aspect, the present application provides a computer-readable medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the information recommendation method as described in the first aspect.

According to the information recommendation method, the information recommendation device, the electronic equipment and the computer readable medium, the ordering request is obtained through the first application; then determining a target store indicated by the ordering request, and acquiring related data of dishes in the target store from the second application; finally, based on the relevant data, information related to dishes in the target store is presented in the first application. Thus, information on dishes in the target store can be automatically acquired while a meal is ordered without contact. When the dining safety is ensured, the information acquisition time of the user in the ordering process is shortened, and the ordering efficiency is improved.

Drawings

Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:

FIG. 1 is a flow diagram of one embodiment of an information acquisition method according to the present application;

FIG. 2 is a flow diagram of one embodiment of an information recommendation method according to the present application;

FIG. 3 is a flow diagram of yet another embodiment of an information recommendation method according to the present application;

FIG. 4 is a schematic structural diagram of one embodiment of an information acquisition apparatus according to the present application;

FIG. 5 is a schematic diagram of an embodiment of an information recommendation device according to the present application;

FIG. 6 is a schematic block diagram of a computer system suitable for use in implementing an electronic device according to embodiments of the present application.

Detailed Description

The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.

It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.

Fig. 1 shows a flow 100 of an embodiment of an information acquisition method according to the present application. The execution subject of the information acquisition method may be a server. The server may be hardware or software. When the server is hardware, it may be implemented as a distributed device cluster formed by multiple devices, or may be implemented as a single device. When the server is software, it may be implemented as a plurality of software or software modules, or may be implemented as a single software or software module. And is not particularly limited herein.

In addition, when the terminal device has the capability of implementing the information acquisition method of the present application, the execution subject of the information acquisition method may also be the terminal device. The terminal device may be an electronic device such as a mobile phone, a smart phone, a tablet computer, a laptop portable computer, a wearable device, and the like.

Furthermore, the execution subject of the information acquisition method may also be an electronic system including one or more devices.

The information recommendation method comprises the following steps:

Step 101, obtaining a food ordering request through a first application.

In this embodiment, the execution subject of the information acquisition method may acquire the ordering request through the first application. Here, the first Application may be a client Application (APP) having an ordering function, such as an ordering client, running in a terminal device used by the user. Or an applet with ordering functionality loaded in some client application.

As an example, an order request may be triggered by the first application upon a user triggering an order button or an order function in the first application, or upon identification of some identifier by the first application. Therefore, the execution main body can obtain the ordering request through the first application.

In some optional implementations of this embodiment, the ordering request may be obtained by the first application recognizing the target identifier. The target identifier here may be provided by the target store, which may contain information such as the web address of the order page. By identifying the target identifier, an order page may be presented. In practice, the target identifier may be a one-dimensional code (such as a bar code), a two-dimensional code, or the like, or may be a character string composed of at least one of letters, numbers, and special characters.

In one scenario, the target identifier may be stored locally at the terminal device. As an example, when the target identifier is a one-dimensional code or a two-dimensional code, the target identifier may be stored locally in the form of a picture. After the picture is identified by the first application, a food ordering request can be sent. As yet another example, when the target identifier is a character string composed of at least one of words, letters, numbers, and special characters, the character string may be stored locally. When the first application is started after the character string is copied, a food ordering request can be sent.

In another scenario, the target identifier may be placed in a target store (e.g., inside or outside the store). The target identifier at this time may be a one-dimensional code (e.g., a bar code), a two-dimensional code, or the like. The execution subject may scan the target identifier through the first application to send the order request.

And step 102, determining a target store indicated by the ordering request, and acquiring related data of dishes in the target store from the second application.

In this embodiment, the food ordering request may include information of the store that requested food ordering, such as the name or identification of the store. Therefore, the target store indicated by the ordering request can be determined through the information carried in the ordering request.

Then, data relating to dishes in the target store may be obtained from the second application. The second application here may serve as a data acquisition source. For example, it may be a client application or service platform that provides relevant data for multiple stores. The execution subject may obtain the data related to the dishes in the target store from the second application by sending a request, a crawler, or the like.

Here, the related data of the dishes in the target store may include, but is not limited to, at least one of: assessment data, hygiene data, activity data, and the like.

In one scenario, the execution subject may obtain the relevant data of the dishes in the target store from the second application, and may include evaluation data. For a certain dish in the target store, the evaluation data of the dish may be data for evaluating the dish. The ratings data can characterize the satisfaction of the historical customer with the dish. For example, may include, but is not limited to: number of good reviews, number of medium reviews, number of bad reviews, number of praise, amount of recommendations, number of collections, and so forth.

Taking the second application as a target comment platform providing a store evaluation function as an example, each user can evaluate, approve, forward, collect, etc. dishes in each store and each store in the platform. Historical customers of the target store may also evaluate dishes of the target store in the platform. Thus, the execution subject can query and acquire evaluation data of dishes in the target store from the platform.

In another scenario, the executive body obtains the data related to the dishes in the target store from the second application, and may include hygiene data. The hygiene data may be data for characterizing the hygiene condition of the dish. For example, hygiene ratings, scores, and the like may be included.

In another scenario, the execution subject may obtain the data related to the dishes in the target store from the second application, and may include activity data. The activity data may include, but is not limited to, current store activity, activity links, coupon information, coupon expiration dates, coupon usage methods, and the like.

In some optional implementations of the embodiment, the execution subject may first query, in the second application, data associated with the target store. Then, the relevant data of the dishes in the target store can be obtained from the inquired data. For example, first, the name of the target store is used as a keyword, and data in a page associated with the keyword is queried. And then extracting relevant data of the dishes from the data.

Step 103, displaying information related to dishes in the target store in the first application based on the related data.

In this embodiment, the execution subject may present, in the first application, information related to the dishes in the target store based on the data related to the dishes in the target store. Here, the information related to the dishes in the target store may be information extracted or sorted from the acquired data related to the dishes in the target store.

In practice, the execution subject may first obtain information related to dishes in the target store based on the related data. For example, the relevant data is subjected to statistics, analysis, extraction, and the like, thereby obtaining information on dishes in the target store. The information may then be transmitted to the first application, thereby exposing the information in the first application.

In some optional implementation manners of this embodiment, the execution subject may specifically display information related to dishes in the target store in a meal ordering page of the first application.

In some optional implementations of this embodiment, the related data may include evaluation data. At this time, the execution subject may first determine a customer recommended dish based on the evaluation data; then, the dish information of the customer recommended dishes is displayed in the first application.

When the dish information of the dish recommended by the customer is displayed, the recommended dish class can be added to the original menu of the target store, and the dish information of the dish recommended by each customer is classified into the recommended dish class, so that the updated target menu is obtained. And then, displaying an ordering page used for presenting the target menu in the first application, so that the dish information of the dishes recommended by the customer can be presented through the ordering page.

In practice, the ordering page may include one or more preset categories, such as cold dishes, hot dishes, and the like. Or special dishes, staple foods, side dishes, drinks and the like. The dish information of the dish recommended by the client may be individually set to one category, and may be referred to as a recommended dish category, for example. Therefore, the ordering page can contain the normal menu and the dish recommended by the client.

In some alternative implementations of the present embodiment, the related data may include hygiene data. At this time, the execution main body may determine the health condition information (e.g., expressed in the form of characters or pictures) of dishes in the target store based on the health data; then, the health condition information is presented in the first application.

In some optional implementations of this embodiment, the related data may include activity data. At this time, the execution agent may determine a coupon for dishes in the target store based on the event data; the coupon is then presented in the first application described above.

In the method provided by the above embodiment of the application, the order request is obtained through the first application; then determining a target store indicated by the ordering request, and acquiring related data of dishes in the target store from the second application; finally, based on the relevant data, information related to dishes in the target store is presented in the first application. Thus, information on dishes in the target store can be automatically acquired while a meal is ordered without contact. When the dining safety is ensured, the information acquisition time of the user in the ordering process is shortened, and the ordering efficiency is improved.

With continued reference to FIG. 2, a flow 200 of one embodiment of an information recommendation method according to the present application is shown. The execution subject of the information recommendation method may be a server. The server may be hardware or software. When the server is hardware, it may be implemented as a distributed device cluster formed by multiple devices, or may be implemented as a single device. When the server is software, it may be implemented as a plurality of software or software modules, or may be implemented as a single software or software module. And is not particularly limited herein. The information recommendation method comprises the following steps:

Step 201, receiving a food ordering request aiming at a target store.

In this embodiment, the execution subject of the information recommendation method may receive an order request for a target store. The target store is a store where the user currently waits to order. The target store can be various types of dining stores, such as fast food stores, traditional restaurants, milky tea stores and the like. In practice, the ordering request may include information such as an identifier or a name of the target store, so that the executing entity may obtain information related to the target store. The ordering request here can be sent via the terminal device used by the user. The terminal device may be an electronic device such as a mobile phone, a smart phone, a tablet computer, a laptop portable computer, a wearable device, and the like.

In some optional implementations of this embodiment, the ordering request may be sent through a first application installed by the terminal device, for example, after the target identifier is recognized by the first application. The first application may be a client application running in a device used by the user. The method can be specifically used for ordering the food clients.

The ordering client can be a client with ordering function, and can also be implemented as one or more program modules. The ordering client can support the ordering function of one or more stores including the target store. In practice, a user can open the ordering client and select a target store needing ordering, so that the ordering request is sent. Furthermore, the first application may also be an applet loaded in some client application.

The target identifier here may be provided by the target store, which may contain information such as the web address of the order page. By identifying the target identifier, an order page may be presented. In practice, the target identifier may be a one-dimensional code (such as a bar code), a two-dimensional code, or the like, or may be a character string composed of at least one of letters, numbers, and special characters.

Step 202, obtaining evaluation data of the historical customers on dishes in the target store.

In this embodiment, the execution subject may acquire evaluation data of dishes in the target store by the history client. The historical customers are customers who have ordered the target store before the current time. For a certain dish in the target store, the evaluation data of the dish is the data for evaluating the quality of the dish. The ratings data can characterize the satisfaction of the historical customer with the dish. For example, may include, but is not limited to: number of good reviews, number of medium reviews, number of bad reviews, number of praise, amount of recommendations, number of collections, and so forth.

It should be noted that the ordering manner of the historical customers is not limited in the embodiment of the present application, for example, the historical customers may include customers ordering by scanning a code, customers ordering by an ordering client, customers entering a store for manual ordering by a conventional ordering manner, and the like.

In one scenario, the execution subject may obtain the rating data from any one of the rating data obtaining sources after receiving the ordering request. The evaluation data acquisition source may include, but is not limited to, an evaluation page of a target store in the internet, a target review platform, a server or a database storing evaluation data, and the like. The target commenting platform may be a service platform providing a store evaluation function.

In another scenario, the execution subject may further obtain evaluation data in advance before receiving the ordering request, and store the obtained evaluation data locally. For example, the subject may periodically obtain evaluation data of recent time (e.g., last month, week, etc.) from the target review platform and store the evaluation data locally. After receiving the order request, the evaluation data of the dishes in the target store can be directly acquired from the local.

In some optional implementation manners of the embodiment, the execution subject may obtain, from the second application, evaluation data of dishes in the target store by the historical customer. The second application here may serve as a data acquisition source. Such as an application or service platform that provides data about multiple stores. The execution main body may acquire evaluation data of dishes in the target store from the second application by sending a request, a crawler, or the like. As an example, the second application may specifically be a target critique platform. The target commenting platform is a service platform providing store evaluation functions.

And step 203, determining the recommended dishes of the client based on the evaluation data.

In this embodiment, since the evaluation data is output by the historical client, the satisfaction degree of the historical client on the dishes can be represented, and thus, the recommended dishes of the client can be determined by performing statistics and analysis on the evaluation data. The dish recommended by the customer is the dish which is more satisfied by the user.

As an example, the execution subject may determine that the customer recommends dishes based on the number of praise in the evaluation data. Specifically, the execution subject may select a preset number of dishes from the dishes provided by the target store in order from high to low of the number of praise as the recommended dishes for the customer.

As yet another example, the execution main body may determine that the customer recommends dishes based on the recommended amount in the evaluation data. Specifically, the execution main body may select a preset number of dishes from dishes provided by the target store in order of the recommended amount from high to low, as the customer recommended dishes.

In some optional implementations of the embodiment, the execution main body may determine the customer recommended dishes according to the following sub-steps S11 to S12:

And a substep S11 of determining the number of dishes satisfying a preset recommendation condition in the target store based on the evaluation data.

The preset recommended condition here may be preset as needed. For example, the preset recommendation condition may be that the number of praise is greater than the preset number of praise, the rating is greater than the preset rating, and the like. The preset recommendation condition may be set as two or more items at the same time. The number of items or specific content of the preset recommendation condition is not limited in the embodiment of the application.

Optionally, when the preset recommendation condition is set based on the number of praise, the execution main body may take dishes with the number of praise greater than or equal to the preset number of praise as the dishes meeting the preset recommendation condition, and determine the number of the dishes meeting the preset recommendation condition.

Optionally, when the preset recommendation condition is set based on the good evaluation rate, the execution subject may first perform statistics on the evaluation data to determine the good evaluation rate of each dish in the target store. The above-mentioned good appraisal rate is the ratio of the number of good appraisals to the total number of appraisals. And then, dishes with the high evaluation rate being greater than or equal to the preset high evaluation rate can be used as dishes meeting the preset recommendation condition, and the number of dishes meeting the preset recommendation condition is determined.

And a substep S12 of selecting a part of dishes as the customer recommended dishes from the dishes meeting the preset recommendation condition under the condition that the number is greater than the preset first threshold value.

The first threshold value here may be set as needed, and may be set to a positive integer such as 8 or 10, for example. As an example, when the first threshold is 10, if the number of dishes meeting the preset recommendation condition is 12, only 10 dishes are selected from the dish number, and are used as the customer recommendation dishes.

In practice, the execution main body may select some dishes in the target store as the recommended dishes for the customer from the dishes meeting the preset recommendation conditions according to a preset rule. The above-mentioned rule may be preset as necessary. For example, it may be set to select in the order of the praise number from high to low, in the order of the goodness from high to low, in the order of the recommended amount from high to low, or the like.

Therefore, under the condition that the number of dishes meeting the preset recommendation conditions is large, only part of dishes are selected as the recommended dishes of the client, the number of the recommended dishes of the client is ensured not to be too large, and interference on the decision process of the user is avoided.

Optionally, when the preset recommendation condition is set based on the good evaluation rate, for example, when the number of the dishes meeting the preset recommendation condition is the number of the dishes of which the number of praise is greater than or equal to the preset number of praise, the execution main body may select the dishes of which the number is the first threshold value from the dishes meeting the preset recommendation condition as the recommended dishes for the client according to the sequence of the numbers of praise from large to small.

Optionally, when the preset recommendation condition is set based on the good evaluation rate, for example, when the number of dishes meeting the preset recommendation condition is the number of dishes with a good evaluation rate greater than or equal to the preset good evaluation rate, the execution main body may select, from the dishes meeting the preset recommendation condition, the dishes with the number of the first threshold value as the recommended dishes for the client in the order from high to low of the good evaluation rate.

It should be noted that, in a case that the number of dishes meeting the preset recommendation condition is less than or equal to the first threshold and greater than or equal to a preset second threshold, the execution main body may use each dish meeting the preset recommendation condition as a customer recommendation dish. Here, the second threshold is a non-negative integer smaller than the first threshold. As an example, when the first threshold is 10 and the second threshold is 3, if the number of dishes meeting the preset recommendation condition is 8, all of the 8 dishes are regarded as the customer recommended dishes.

It should be noted that, if the number of dishes meeting the preset recommendation condition is smaller than the second threshold (e.g. 3), the ordering page indicating the original menu may be directly returned, and the dish information of the dish recommended by the customer is not displayed in the ordering page. In addition, the second threshold may not be set, and in this case, when the number of dishes satisfying the preset recommendation condition is less than or equal to the first threshold, the execution main body may use each dish satisfying the preset recommendation condition as the customer recommended dish.

It should be noted that, the specific manner of determining the dish recommended by the client based on the evaluation data in this embodiment may also be applied to the relevant steps in the embodiment corresponding to fig. 1.

And step 204, returning to a meal ordering page containing the dish information of the dish recommended by the client.

In this embodiment, the execution main body may return an ordering page containing the dish information of the dish recommended by the client. The dish selection and ordering can be realized by operating (such as clicking) the information and the keys in the ordering page.

In some optional implementation manners of this embodiment, the executing main body may first add a recommended dish class to an original menu of the target store, and put dish information of dishes recommended by each customer into the recommended dish class, so as to obtain an updated target menu. And then returning to the ordering page for presenting the target menu.

In some optional implementation manners of this embodiment, when the ordering request is sent by a first application (e.g., an ordering client), the execution main body may return an ordering page containing the dish information of the customer-recommended dish to the first application (the ordering client), so that the ordering page is presented in the first application, and the dish information of the customer-recommended dish is presented in the ordering page.

According to the method provided by the embodiment of the application, the ordering request for the target store is received, then the evaluation data of the historical customers on dishes in the target store are obtained, and then the recommended dishes of the customers are determined based on the evaluation data, so that the ordering page containing the dish information of the recommended dishes of the customers is returned. Therefore, in the non-contact ordering process, communication with workers is not needed, and dishes recommended by net friends are not needed to be inquired synchronously by other peers. Therefore, the meal ordering method and the meal ordering device ensure the safety of meal, shorten the decision time of a user in the meal ordering process and improve the meal ordering efficiency.

With further reference to FIG. 3, a flow 300 of yet another embodiment of an information recommendation method is shown. The execution subject of the information recommendation method can be an electronic device such as a server and a desktop computer, and can also be software or a software module. The process 300 of the information recommendation method includes the following steps:

Step 301, receiving a food ordering request for a target store.

Step 301 of this embodiment can refer to step 201 of the corresponding embodiment in fig. 2, and is not described herein again.

Step 302, obtaining evaluation data of the historical customers on dishes in the target store.

Step 302 of this embodiment can refer to step 202 of fig. 2, which is not described herein again.

And step 303, determining the number of dishes meeting the preset recommendation conditions in the target store based on the evaluation data.

In this embodiment, the execution subject may determine the number of dishes satisfying the preset recommendation condition in the target store based on the evaluation data acquired in step 302. The preset recommended condition here may be preset as needed. For example, the preset recommendation condition may be that the number of praise is greater than the preset number of praise, the rating is greater than the preset rating, and the like. In addition, two or more preset recommendation conditions can be set simultaneously. The number of items or specific content of the preset recommendation condition is not limited in the embodiment of the application.

In some optional implementation manners of this embodiment, when the preset recommendation condition is set based on the number of praise, the execution main body may take dishes with the number of praise greater than or equal to the preset number of praise as dishes meeting the preset recommendation condition, and determine the number of dishes meeting the preset recommendation condition.

In some optional implementation manners of this embodiment, when the preset recommendation condition is set based on the good rating, the execution subject may first perform statistics on the evaluation data to determine the good rating of each dish in the target store. And then, taking the dishes with the high evaluation rate greater than or equal to the preset high evaluation rate as the dishes meeting the preset recommendation condition, thereby counting the number of the dishes meeting the preset recommendation condition. The rating here is the ratio of the number of ratings to the total number of ratings.

And 304, under the condition that the number is larger than a preset first threshold value, selecting part of dishes from the dishes meeting the preset recommendation condition as the recommended dishes of the client.

In this embodiment, in a case that the number of dishes satisfying the preset recommendation condition in the target store is greater than a preset first threshold, the execution main body may select a part of dishes from the dishes satisfying the preset recommendation condition as the customer recommended dishes.

The first threshold value here may be set as needed, and may be set to a positive integer such as 8 or 10, for example. Taking the first threshold equal to 10 as an example, if the number of dishes meeting the preset recommendation condition is 12, only 10 dishes may be selected from the number of dishes, and the selected dishes serve as the recommended dishes for the customer.

In practice, the execution main body may select a part of dishes from the dishes meeting the preset recommendation condition as the customer recommended dishes according to a preset rule. The above-mentioned rule may also be preset as required, and for example, the rule may be set in a manner of selecting in an order from top to bottom according to the number of praise, selecting in an order from top to bottom according to the rating, selecting in an order from top to bottom according to the recommended amount, and the like.

In some optional implementation manners of this embodiment, when the preset recommendation condition is set based on the good evaluation rate, that is, when the number of the dishes meeting the preset recommendation condition is the number of the dishes of which the number of praise is greater than or equal to the preset number of praise, the execution main body may select, from the dishes meeting the preset recommendation condition, the dishes of which the number is the first threshold as the recommended dishes for the client according to a descending order of the number of praise.

In some optional implementation manners of this embodiment, when the preset recommendation condition is set based on the good evaluation rate, that is, when the number of dishes meeting the preset recommendation condition is the number of dishes with a good evaluation rate greater than or equal to the preset good evaluation rate, the execution main body may select, from the dishes meeting the preset recommendation condition, the number of dishes with the first threshold as the customer recommended dishes in an order from high to low in the good evaluation rate.

And 305, taking each dish meeting the preset recommendation condition as a customer recommendation dish under the condition that the number is less than or equal to the first threshold and is greater than or equal to a preset second threshold.

In this embodiment, in a case that the number of dishes meeting the preset recommendation condition is less than or equal to the first threshold and greater than or equal to the preset second threshold, the execution main body may use each dish meeting the preset recommendation condition as a customer recommendation dish.

Here, the second threshold is a positive integer smaller than the first threshold. As an example, when the first threshold is 10 and the second threshold is 3, if the number of dishes meeting the preset recommendation condition is 8, all of the 8 dishes are regarded as the customer recommended dishes.

It should be noted that, if the number of dishes meeting the preset recommendation condition is smaller than the second threshold, the ordering page indicating the original menu may be directly returned, and the dish information of the dish recommended by the customer is not displayed in the page.

And step 306, adding recommended dish categories in the original menu of the target store, and classifying dish information of dishes recommended by each customer into the recommended dish categories to obtain the target menu.

In this embodiment, the execution main body may add a recommended dish class to the original menu of the target store, and classify the dish information of the dish recommended by each customer into the recommended dish class, thereby obtaining the updated target menu. In practice, the target menu may include various categories in the original menu, such as cold dishes, hot dishes, etc., in addition to the recommended dish categories. Or special dishes, staple foods, side dishes, drinks and the like. Therefore, the ordering page can contain the dish information in the original menu and the dish information of the dish recommended by the customer.

Step 307, return to the order page for presenting the target menu.

In this embodiment, the execution subject may return an ordering page for presenting the target menu. The dish selection and ordering can be realized by operating (such as clicking) the information and the keys in the ordering page.

As can be seen from fig. 3, compared with the embodiment corresponding to fig. 2, the flow 300 of the information recommendation method in this embodiment highlights a step of determining the number of dishes meeting the preset recommendation condition in the target store based on the evaluation data, and determining that the customer recommends the dishes when the number is different. Therefore, the scheme described in the embodiment can provide more reasonable amount of recommended dishes for the user to select. The decision making of the user is prevented from being interfered by the fact that the number of dishes recommended by the client is large.

With further reference to fig. 4, as an implementation of the method shown in fig. 1, the present application provides an embodiment of an information obtaining apparatus, which corresponds to the embodiment of the method shown in fig. 1, and which can be applied to various electronic devices.

As shown in fig. 4, the information acquiring apparatus 400 according to the present embodiment includes: a first obtaining unit 401 configured to obtain an ordering request through a first application; a second obtaining unit 402, configured to determine a target store indicated by the ordering request, and obtain data related to dishes in the target store from a second application; a display unit 403 configured to display information related to dishes in the target store in the first application based on the related data.

In some optional implementations of this embodiment, the first obtaining unit 401 is further configured to: and identifying the target identifier through the first application to obtain the ordering request.

In some optional implementations of this embodiment, the second obtaining unit 402 is further configured to: in a second application, inquiring data associated with the target store; and acquiring related data of dishes in the target store from the inquired data.

In some optional implementations of this embodiment, the display unit 403 is further configured to: and displaying information related to dishes in the target store in an ordering page of the first application.

In some optional implementations of this embodiment, the related data includes evaluation data; and, the display unit 403 is further configured to: determining a recommended dish of the client based on the evaluation data; and displaying the dish information of the dish recommended by the client in the first application.

In some optional implementations of this embodiment, the related data includes hygiene data; and, the display unit 403 is further configured to: determining health condition information of dishes in the target store based on the health data; the health condition information is presented in the first application.

In some optional implementations of this embodiment, the related data includes activity data; and, the display unit 403 is further configured to: determining a coupon for a dish in the target store based on the activity data; and displaying the coupon in the first application.

The device provided by the above embodiment of the present application obtains the order request through the first application; then determining a target store indicated by the ordering request, and acquiring related data of dishes in the target store from the second application; finally, based on the relevant data, information related to dishes in the target store is presented in the first application. Thus, information on dishes in the target store can be automatically acquired while a meal is ordered without contact. When the dining safety is ensured, the information acquisition time of the user in the ordering process is shortened, and the ordering efficiency is improved.

With further reference to fig. 5, as an implementation of the method shown in fig. 2, the present application provides an embodiment of an information recommendation apparatus, which corresponds to the embodiment of the method shown in fig. 2, and which can be applied to various electronic devices.

As shown in fig. 5, the information recommendation apparatus 500 according to the present embodiment includes: a receiving unit 501 configured to receive a food ordering request for a target store; an acquisition unit 502 configured to acquire evaluation data of dishes in the target store by the history client; a determination unit 503 configured to determine a customer recommended dish based on the evaluation data; a returning unit 504 configured to return an ordering page containing the dish information of the customer recommended dishes.

In some optional implementation manners of this embodiment, the food ordering request is sent after the target identifier is identified by a food ordering client installed in the terminal device.

In some optional implementations of the present embodiment, the obtaining unit 502 is further configured to: and acquiring evaluation data of the historical customers on dishes in the target store from the target commenting platform.

In some optional implementations of this embodiment, the returning unit 504 is further configured to: adding recommended dish categories into the original menu of the target store, and classifying dish information of dishes recommended by each customer into the recommended dish categories to obtain a target menu; and returning to an ordering page for presenting the target menu.

In some optional implementations of this embodiment, the determining unit 503 is further configured to: determining the number of dishes meeting preset recommendation conditions in the target store based on the evaluation data; and under the condition that the number is larger than a preset first threshold value, selecting part of dishes as the recommended dishes of the client from the dishes meeting the preset recommendation condition.

In some optional implementations of this embodiment, the determining unit 503 is further configured to: and under the condition that the number is less than or equal to the first threshold value and greater than or equal to a preset second threshold value, taking each dish meeting a preset recommendation condition as a customer recommendation dish.

In some optional implementations of this embodiment, the evaluation data includes a number of praise; and, the above-mentioned returning unit 504 is further configured to: dishes with the praise number larger than or equal to the preset praise number are used as dishes meeting the preset recommendation condition, and the number of the dishes meeting the preset recommendation condition is determined; and selecting dishes with the number of the first threshold value from the dishes meeting the preset recommendation condition according to the sequence of the praise numbers from large to small as the recommended dishes of the client.

In some optional implementations of this embodiment, the returning unit 504 is further configured to: counting the evaluation data to determine the good evaluation rate of each dish in the target store; taking dishes with the good evaluation rate greater than or equal to a preset good evaluation rate as dishes meeting preset recommendation conditions, and determining the number of the dishes meeting the preset recommendation conditions; and selecting part of the dishes from the dishes meeting the preset recommendation conditions as the recommended dishes for the client, wherein the method comprises the following steps: and selecting dishes with the number of the first threshold value from the dishes meeting the preset recommendation condition according to the sequence of high-to-low favorable rating as the recommended dishes for the client.

According to the device provided by the embodiment of the application, the ordering request for the target store is received, then the evaluation data of the historical customers on the dishes in the target store are obtained, and then the recommended dishes of the customers are determined based on the evaluation data, so that the ordering page containing the dish information of the recommended dishes of the customers is returned. Therefore, in the non-contact ordering process, communication with workers is not needed, and dishes recommended by net friends are not needed to be inquired synchronously by other peers. Therefore, the meal ordering method and the meal ordering device ensure the safety of meal, shorten the decision time of a user in the meal ordering process and improve the meal ordering efficiency.

Referring now to FIG. 6, shown is a block diagram of a computer system 600 suitable for use in implementing the electronic device of an embodiment of the present application. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.

As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the system 600 are also stored. The CPU601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

to the I/O interface 605, AN input section 606 including a keyboard, a mouse, and the like, AN output section 607 including a network interface card such as a liquid crystal display (L CD), and the like, a speaker, and the like, a storage section 608 including a hard disk, and the like, and a communication section 609 including a network interface card such as AN L AN card, a modem, and the like, the communication section 609 performs communication processing via a network such as the internet, a drive 610 is also connected to the I/O interface 605 as necessary, a removable medium 611 such as a magnetic disk, AN optical disk, a magneto-optical disk, a semiconductor memory, and the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted into the storage section 608 as necessary.

In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program performs the above-described functions defined in the method of the present application when executed by a Central Processing Unit (CPU) 601. It should be noted that the computer readable medium described herein can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in the embodiments of the present application may be implemented by software or hardware. The units described may also be provided in a processor, where the names of the units do not in some cases constitute a limitation of the units themselves.

As another aspect, the present application also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments; or may be present separately and not assembled into the device. The computer readable medium carries one or more programs which, when executed by the apparatus, cause the apparatus to: obtaining a food ordering request through a first application; determining a target store indicated by the ordering request, and acquiring related data of dishes in the target store from the second application; based on the relevant data, information related to dishes in the target store is presented in the first application. Alternatively, the one or more programs, when executed by the apparatus, cause the apparatus to: receiving a food ordering request for a target store; acquiring evaluation data of a historical client on dishes in a target store; determining a customer recommended dish based on the evaluation data; and returning to a meal ordering page containing the dish information of the dish recommended by the client.

The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.

Claims

1. An information acquisition method, characterized in that the method comprises:

Obtaining a food ordering request through a first application;

Determining a target store indicated by the ordering request, and acquiring related data of dishes in the target store from a second application;

Displaying, in the first application, information related to dishes in the target store based on the related data.

2. The method of claim 1, wherein obtaining the order request through the first application comprises:

And identifying the target identifier through the first application to obtain the ordering request.

3. The method of claim 1, wherein obtaining data regarding the dishes in the target store from a second application comprises:

In a second application, querying data associated with the target store;

And acquiring related data of dishes in the target store from the inquired data.

4. The method of claim 1, wherein presenting information related to dishes in the target store in the first application comprises:

And displaying information related to dishes in the target store in an ordering page of the first application.

5. The method of claim 1, wherein the relevant data comprises evaluation data; and

The presenting, in the first application, information related to dishes in the target store based on the related data, includes:

Determining a customer recommended dish based on the evaluation data;

And displaying the dish information of the dish recommended by the client in the first application.

6. The method of claim 1, wherein the related data comprises hygiene data; and

Determining health condition information of dishes in the target store based on the health data;

Displaying the health condition information in the first application.

7. The method of claim 1, wherein the related data comprises activity data; and

Determining a coupon for a dish in the target store based on the activity data;

Presenting the coupon in the first application.

8. An information recommendation method, characterized in that the method comprises:

Receiving a food ordering request for a target store;

Acquiring evaluation data of historical customers on dishes in the target store;

Determining a customer recommended dish based on the evaluation data;

And returning to a meal ordering page containing the dish information of the dish recommended by the client.

9. The method of claim 8, wherein the ordering request is sent after the target identifier is identified by an ordering client installed in the terminal device.

10. The method of claim 8, wherein obtaining historical customer rating data for dishes in the target store comprises:

And acquiring evaluation data of the historical customers on dishes in the target store from the target commenting platform.

11. The method of claim 8, wherein returning an ordering page containing meal information for the customer recommended meals comprises:

Adding recommended dish categories in the original menu of the target store, and classifying dish information of dishes recommended by each customer into the recommended dish categories to obtain a target menu;

And returning to an ordering page for presenting the target menu.

12. The method of claim 8, wherein determining a customer recommended dish based on the ratings data comprises:

Determining the number of dishes meeting preset recommendation conditions in the target store based on the evaluation data;

And under the condition that the number is larger than a preset first threshold value, selecting part of dishes as the recommended dishes of the client from the dishes meeting the preset recommendation condition.

13. The method of claim 12, wherein determining a customer recommended dish based on the ratings data further comprises:

And under the condition that the number is smaller than or equal to the first threshold value and larger than or equal to a preset second threshold value, taking each dish meeting a preset recommendation condition as a customer recommendation dish.

14. The method of claim 12, wherein the ratings data includes praise numbers; and

The determining, based on the evaluation data, the number of dishes in the target store that meet a preset recommendation condition includes:

Dishes with the praise number larger than or equal to the preset praise number are used as dishes meeting the preset recommendation condition, and the number of the dishes meeting the preset recommendation condition is determined;

And selecting part of dishes from the dishes meeting the preset recommendation conditions as the recommended dishes for the client, wherein the method comprises the following steps:

And selecting dishes with the number of the first threshold value from the dishes meeting the preset recommendation condition according to the sequence of the praise numbers from large to small as the recommended dishes of the client.

15. The method of claim 12, wherein determining the number of dishes meeting a preset recommendation condition in the target store based on the evaluation data comprises:

Counting the evaluation data, and determining the good evaluation rate of each dish in the target store;

Taking dishes with the good evaluation rate greater than or equal to a preset good evaluation rate as dishes meeting preset recommendation conditions, and determining the number of the dishes meeting the preset recommendation conditions;

And selecting dishes with the number of being the first threshold value from the dishes meeting the preset recommendation condition according to the sequence of high-to-low favorable rating as the recommended dishes for the client.

16. An information acquisition apparatus, characterized in that the apparatus comprises:

A first obtaining unit configured to obtain an ordering request through a first application;

A second obtaining unit configured to determine a target store indicated by the ordering request and obtain data related to dishes in the target store from a second application;

A presentation unit configured to present information related to dishes in the target store in the first application based on the related data.

17. An information recommendation apparatus, characterized in that the apparatus comprises:

A receiving unit configured to receive an order request for a target store;

An acquisition unit configured to acquire evaluation data of dishes in the target store by a history customer;

A determination unit configured to determine a customer recommended dish based on the evaluation data;

A returning unit configured to return an ordering page containing the dish information of the customer recommended dish.

18. An electronic device, comprising:

One or more processors;

A storage device having one or more programs stored thereon,

When executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-15.

19. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-15.