WO2021051744A1 - 菜品推荐方法、推荐内容上传方法、装置及电子设备 - Google Patents
菜品推荐方法、推荐内容上传方法、装置及电子设备 Download PDFInfo
- Publication number
- WO2021051744A1 WO2021051744A1 PCT/CN2020/076677 CN2020076677W WO2021051744A1 WO 2021051744 A1 WO2021051744 A1 WO 2021051744A1 CN 2020076677 W CN2020076677 W CN 2020076677W WO 2021051744 A1 WO2021051744 A1 WO 2021051744A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- recommended content
- dish
- user
- content
- target
- Prior art date
Links
- 238000000034 method Methods 0.000 title claims abstract description 61
- 230000015654 memory Effects 0.000 claims description 36
- 238000006243 chemical reaction Methods 0.000 claims description 17
- 230000004044 response Effects 0.000 claims description 15
- 230000002349 favourable effect Effects 0.000 claims description 8
- 238000004458 analytical method Methods 0.000 claims description 6
- 238000004590 computer program Methods 0.000 claims description 4
- 238000004891 communication Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000013500 data storage Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000010295 mobile communication Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012552 review Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9538—Presentation of query results
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0631—Item recommendations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/12—Hotels or restaurants
Definitions
- This application relates to the field of information processing technology, in particular to a method for recommending dishes, a method for uploading recommended articles, a device, and electronic equipment.
- the quality of the recommendation information is not high for the user, and it still takes a lot of time for the user to finally select the dishes to be ordered.
- the purpose of the embodiments of the present application is to provide a method for recommending dishes, a method for uploading recommended articles, a device, and electronic equipment, which can accurately recommend dishes that the user may be interested in, with strong pertinence, and at the same time can improve the order conversion rate.
- the embodiment of the present application provides a dish recommendation method, including: analyzing the user's ordering habits according to the user's historical order data; and selecting target recommendations from pre-collected recommended content according to the ordering habits Content, where the recommended content is used to recommend dishes and provide an ordering channel for the dishes; the target recommended content is pushed to the user's client for display.
- the embodiment of the present application also provides a method for uploading recommended content, which includes: obtaining the dish identification of the recommended dish selected by the user; in response to the upload operation of the user, binding the graphic information uploaded by the user with the dish identification To form recommended content; upload the recommended content to the server for execution by the server: analyze the user’s ordering habits according to the user’s historical order data; select the target recommended content from the pre-collected recommended content according to the ordering habits, among which, The recommended content is used to recommend dishes and provide an ordering channel for the dishes; the target recommended content is pushed to the client terminal where the user logs in for display.
- the embodiment of the present application also provides a dish recommendation device, including: an analysis module for analyzing the user's ordering habits according to the user's historical order data; a selection module for selecting pre-collected recommendations according to the ordering habits Select the target recommended content in the content, where the recommended content is used to recommend dishes and provide an ordering channel for the dishes; the push module is used to push the target recommended content to the user's client for display.
- a dish recommendation device including: an analysis module for analyzing the user's ordering habits according to the user's historical order data; a selection module for selecting pre-collected recommendations according to the ordering habits Select the target recommended content in the content, where the recommended content is used to recommend dishes and provide an ordering channel for the dishes; the push module is used to push the target recommended content to the user's client for display.
- the embodiment of the present application also provides an electronic device, including a memory and a processor, the memory stores a computer program, and the processor executes the analysis of the user’s ordering habits according to the user’s historical order data when running the program; Select the target recommended content from the collected recommended content, where the recommended content is used to recommend dishes and provide an ordering channel for the dishes; push the target recommended content to the user's client for display.
- the embodiment of the present application also provides a device for uploading recommended content, including: an acquisition module for acquiring the identity of the recommended dishes selected by the user; and a binding module for uploading the user in response to the upload operation of the user
- the graphic and text information of the menu is bound to the identity of the dish to form recommended content
- the upload module is used to upload the recommended content to the server for execution by the server: analyze the user’s ordering habits according to the user’s historical order data; according to the ordering habits Select the target recommended content from the pre-collected recommended content, where the recommended content is used to recommend dishes and provide an ordering channel for the dishes; push the target recommended content to the client logged in by the user for display.
- the embodiment of the present application also provides an electronic device, including a memory and a processor, the memory stores a computer program, and the processor executes when the program runs: obtains the dish identification of the recommended dish selected by the user; in response to the upload operation of the user, uploads the user The graphic information and the dish’s identity are bound to form the recommended content; the recommended content is uploaded to the server for execution by the server: the user’s ordering habits are analyzed according to the user’s historical order data; the recommendations collected in advance according to the ordering habits Select the target recommended content in the content, where the recommended content is used to recommend dishes and provide an ordering channel for the dishes; push the target recommended content to the client logged in by the user for display.
- the embodiment of the present application also provides a non-volatile storage medium for storing a computer readable program, and the computer readable program is used for a computer to execute the above dish recommendation method or the upload method of recommended content.
- the target recommendation content includes the graphic information of the recommended dish, and the graphic information is bound with the dish ID of the dish.
- the order channel includes the dish operation page corresponding to the dish ID of the dish; when the target recommended content is pushed to the user After displaying by the client, it also includes: in response to the user's triggering operation on the picture in the graphic information, calling the corresponding dish operation page according to the dish identity to provide the user with an ordering service for the dish.
- the user’s ordering habit includes at least the identification information of the content to be marked.
- the content to be marked includes recommended content that has been viewed by the user reaching the first preset threshold without generating an ordering action; Before selecting the target recommended content in the recommended content, it also includes: marking the status of the content to be marked as not selectable; selecting the target recommended content from the pre-collected recommended content according to the ordering habit, specifically: according to the ordering habit from the pre-collected Select the target recommended content from the recommended content whose status is optional.
- select the target recommendation content from the pre-collected recommended content according to the ordering habit specifically including: determining the target dish according to the ordering habit; if the content of the recommended target dish is recommended in the pre-collected recommendation content, the content of the recommended target dish is recommended Determine at least one piece of content as the target recommended content.
- the determining at least one piece of recommended content as the target recommended content specifically includes: determining the target recommended content based on the credit of the author of the recommended content.
- the way to obtain the credit of the author includes: counting the order conversion rate of recommended content collected in advance; determining the credit of the author of each recommended content according to the order conversion rate, where the higher the order conversion rate, the higher the credit.
- the user’s ordering habits include the category of the ordered dish; if there is no recommended target dish in the pre-collected recommended content, at least one of the recommended contents of the same category as the target dish will be determined from the recommended content.
- the content is recommended as the target content.
- the user’s ordering habits also include the favorable rate of historically ordered dishes; determine at least one piece of content from the recommended target dishes as the target recommended content, specifically including: the favorable rate of selecting recommended dishes from the recommended target dishes. The highest recommended content is used as the target recommended content.
- Fig. 1 is a flowchart of a method for recommending dishes according to a first embodiment of the present application
- FIG. 2 is a flowchart of a method for recommending dishes according to a second embodiment of the present application
- Fig. 3 is a flowchart of a method for uploading recommended content provided according to a third embodiment of the present application.
- Fig. 4 is a schematic structural diagram of a dish recommending device according to a fourth embodiment of the present application.
- Fig. 5 is a schematic structural diagram of a recommended content uploading device according to a fifth embodiment of the present application.
- FIG. 6 is a schematic diagram of the structure of an electronic device according to a sixth embodiment of the present application.
- Fig. 7 is a schematic structural diagram of an electronic device according to a seventh embodiment of the present application.
- the first embodiment of the application relates to a method for recommending dishes. As shown in FIG. 1, the following describes the implementation details of the method for recommending dishes in this embodiment. The following content is only provided for ease of understanding, not implementation. A must for this program.
- Step S101 Analyze the user's ordering habits according to the user's historical order data.
- the historical order data of each user is stored, including the user's historical delivery address, ordered dishes, dish prices, delivery time, order time, user reviews, and so on.
- the server can analyze the user's ordering habits based on these data. For example, according to the user's history of ordering dishes, it can analyze the dishes that the user often orders and the types of dishes that the user likes, and the price of the dishes ordered by the user can be analyzed according to the user's history.
- the price range of the dishes frequently ordered by the user can be analyzed according to the order delivery time and order time in the user's historical record and user evaluation to analyze the user's requirements for delivery time (that is, the delivery time usually required for an order) and so on.
- step S102 the target recommended content is selected from the recommended content collected in advance according to the order habit.
- each recommended content collected is used to recommend dishes and provide an ordering channel for the recommended dishes; among the recommended content collected in advance, each recommended content can contain the graphic information of the dishes to be recommended, and among these graphic information Bind the dish ID of the dish to be recommended, and the order channel provided for the recommended dish includes the dish operation page corresponding to the dish ID of the recommended dish.
- the dish operation page can be the dish order page corresponding to the dish. It may also be the operation page corresponding to the operation of adding dishes to the shopping cart.
- the user’s ordering habits may include the user’s frequently ordered dishes and the categories of the dishes ordered by the user.
- the target dishes recommended to the user can be determined, and then Determine at least one piece of target recommendation content from the recommended content collected in advance; in the process of selecting the target recommendation content, if there is recommended content recommending the target dish in the recommended content collected in advance, determine at least one piece from the recommended content of the recommended target dish
- One piece of recommended content is used as the target recommended content; if there is no recommended target dish in the pre-collected recommended content, at least one piece of content is determined as the target recommended content from the recommended content of the same category of the recommended dish as the target dish .
- the recommended content of the dish of the same category as the target dish is selected and recommended to the user, so as to ensure that the recommended content is of interest to the user as much as possible.
- the recommended content can be recommended articles.
- the author of each recommended article can upload the edited content through various terminal devices.
- selecting the target recommended content it will be selected from the recommended content uploaded by each author. Make a selection, and different authors have different credit levels.
- the target recommended content can be determined based on the credit of the author of the recommended content.
- the author of the selected target recommended content is the author of the content of each recommended target dish The author with the highest credit rating among them.
- select recommended content written by the author with the highest credit rating so that users can see reliable recommended content as much as possible.
- the method for obtaining the credit of the author includes: counting the order conversion rate of recommended content collected in advance; determining the credit of the author of each recommended content according to the order conversion rate, where the higher the order conversion rate, the higher the credit. Improve the credit of the author with a high order conversion rate, so that the content written by the author is more likely to be pushed to the user, thereby guiding the user to place an order quickly.
- the user can also follow the author of the article. Then, when recommending the target recommendation content to the user, the article uploaded by the author concerned by the user can be directly recommended. In this way, the recommended article is not only an article of interest to the user, but also Moreover, the range of user-selectable dishes is expanded, and the variety of users' choices is increased.
- the user’s ordering habits also include the favorable rate of historical orders; at least one piece of content is determined from the content of the recommended target dish as the target recommended content, specifically: the recommended dish is selected from the content of the recommended target dish The recommended content with the highest praise rate is regarded as the target recommended content.
- the recommended content corresponding to the dish with a high evaluation rate is recommended to the user as the target recommended content, which can ensure the user experience as much as possible.
- Step S103 Push the target recommended content to the user's client for display.
- the recommended content can be pushed to the user's client home page for display, and the recommended content is displayed on the client home page, which can attract the user's attention and enable the user to see the recommended content.
- the target recommended content includes the graphic information of the target dish, and the graphic information of the target dish is bound to the dish identification of the recommended dish, after the target recommended content is pushed to the user's client for display If the user clicks on the picture in the recommended content, that is, the user needs to place an order.
- the corresponding dish operation page can be called according to the dish identification to provide the user with the dish Single service.
- this embodiment analyzes the user's ordering habits according to the user's historical order data, so that the analysis results of the user's ordering habits can be made accurate and reliable; by selecting the recommended content collected in advance according to the ordering habits Select the target recommended content, and push the target recommended content to the user’s client for display, so that the selected target recommended content is more in line with the user’s ordering habits, and the final recommended content to the user is the dish information that the user is interested in.
- the second embodiment of the present application relates to a method for recommending dishes.
- the flowchart of the method for recommending dishes related to this embodiment is shown in FIG. 2, which will be specifically described below.
- Step S201 Analyze the user's ordering habits according to the user's historical order data.
- the user’s ordering habits obtained from the analysis of the user’s historical order data may also include recommended content that the user has browsed many times but did not place an order. These recommended content can be used as items to be marked. Recommended content, that is, the content of recommended content that has been browsed by the user reaches a certain preset threshold without generating an order action, and the preset threshold can be adjusted according to the actual situation.
- Step S202 Mark the state of the content to be marked as an unselectable state.
- a flag bit can be added to each recommended content, the flag bit of the recommended content in the optional state is uniformly set to 1, and the state of marking the content to be marked is in the non-selectable state, that is, the flag position of the content to be marked is set to zero.
- step S203 the target recommended content is selected from the pre-collected recommended content in the optional state according to the order habit.
- the target recommended content is selected from the recommended content whose flag bit is 1.
- Step S204 Push the target recommended content to the user's client for display.
- Step S204 is substantially the same as step S103 in the first embodiment. To avoid repetition, it will not be repeated here.
- the target recommended content when a user browses a certain recommended content without placing an order, it indicates that the user is not interested in the recommended content, and the status of the recommended content is marked as not selectable, and when the user selects
- the target recommended content is only selected from the recommended content in the optional state, that is, the content that the user is not interested in will not be recommended to the user again. In this way, unnecessary calculation steps can be omitted, and it can be used Users find dishes of interest faster.
- the third embodiment of the present application relates to a method for uploading recommended content.
- the flowchart of this embodiment is shown in FIG. 3, which will be described in detail below.
- Step S301 Obtain the dish identification of the recommended dish selected by the user.
- the terminal device can provide the relevant entries of the dishes in each store for the user to choose.
- the terminal device can obtain the dish identification corresponding to the entry clicked by the user.
- Step S302 in response to the upload operation of the user, bind the graphic information uploaded by the user with the dish identification to form recommended content.
- the user can describe the selected dishes with graphics and text, and upload the description information to the designated location of the terminal device.
- the terminal device can combine the graphics and text information uploaded by the user with The identity of the dish is bound to form recommended content.
- step S303 the recommended content is uploaded to the server.
- the terminal device may package the recommended content and send it to a corresponding server for the server to execute the recommended content uploading method mentioned in the first embodiment or the second embodiment.
- the terminal device can provide an editing page for uploading recommended content.
- the user can select a dish in a certain shop in the dish selection area on this page, where the picture or video of the dish (you can directly use the shop in the shop)
- the displayed picture or video, or it can be taken by the user) will be displayed in the dish selection area, and the picture or video of the dish is bound to the shop ID and the dish ID in the shop; the user can edit the article
- the area will import the edited file content; after detecting that the user clicks the upload button, the terminal device can generate a recommended article and send the recommended article to the backend server.
- the recommended content has been bound to the corresponding store ID and dish ID, therefore, after the server pushes the recommended content to the ordering user, the ordering user can click on the specific area of the picture in the recommended article to place an order for the dish directly from the store.
- this embodiment can be used in conjunction with the first embodiment or the second embodiment, so as to realize the recommendation of personalized dishes to the user.
- the fourth embodiment of the present application relates to a dish recommendation device.
- the device includes: an analysis module 401 for analyzing the user's ordering habits according to the user's historical order data; a selection module 402 for using According to the order habit, the target recommended content is selected from the recommended content collected in advance, where the recommended content is used to recommend dishes and provide an ordering channel for the dishes; the push module 403 is used to push the target recommended content to the user’s client for execution display.
- the target recommendation content includes graphic information of the recommended dish, the graphic information is bound with the dish ID of the dish, and the ordering channel includes the dish operation page corresponding to the dish ID of the dish; in the target recommended content After being pushed to the user's client for display, it also includes: in response to the user's triggering operation on the picture in the graphic information, calling the corresponding dish operation page according to the dish identity to provide the user with an ordering service for the dish.
- the user’s ordering habit includes at least the identification information of the content to be marked, and the content to be marked includes recommended content that has been viewed by the user reaching a first preset threshold without generating an ordering action; Before selecting the target recommended content from the pre-collected recommended content, it also includes: marking the status of the content to be marked as not selectable; selecting the target recommended content from the pre-collected recommended content according to the ordering habits, specifically: according to the ordering habits Select the target recommended content from the pre-collected recommended content whose status is optional.
- the user's ordering habits include at least the user's history of ordering dishes and the number of orders; selecting target recommended content from pre-collected recommended content according to the ordering habits, specifically including: determining the target dishes according to the ordering habits ; If there is a content recommending a target dish in the recommended content collected in advance, at least one piece of content is determined as the target recommended content from the content of the recommended target dish.
- determining at least one piece of recommended content as the target recommended content specifically includes: determining the target recommended content based on the credit of the author of the recommended content.
- the method for obtaining the credit of the author includes: counting the order conversion rate of recommended content collected in advance; determining the credit of the author of each recommended content according to the order conversion rate, where the higher the order conversion rate, the higher the credit.
- the user’s ordering habits include the category of the ordered dish; if there is no recommended target dish in the pre-collected recommended content, it is determined from the recommended content that the recommended dish category is the same as the target dish category At least one piece of content is recommended as the target content.
- the user’s ordering habit also includes the favorable rate of historically ordered dishes; determining at least one piece of content from the recommended target dishes as the target recommended content, specifically including: selecting recommended dishes from the recommended target dishes content The recommended content with the highest praise rate is regarded as the target recommended content.
- this embodiment is a system embodiment corresponding to any one of the first to second embodiments, and this embodiment can be implemented in cooperation with any one of the first to second embodiments.
- the related technical details mentioned in the first to second embodiments are still valid in this embodiment, and in order to reduce repetition, they will not be repeated here.
- the related technical details mentioned in this embodiment can also be applied to the first embodiment to the second embodiment.
- the fifth embodiment of the present application relates to a device for uploading recommended content.
- the device includes: an obtaining module 501, configured to obtain a dish identification of a recommended dish selected by a user; and a binding module 502, configured to In response to the upload operation of the user, the graphic information uploaded by the user is bound to the identity of the dish to form recommended content; the upload module 503 is used to upload the recommended content to the server for execution by the server: according to the user's historical order data Analyze the user's ordering habits; select target recommended content from the pre-collected recommended content according to the ordering habits, where the recommended content is used to recommend dishes and provide an ordering channel for the dishes; push the target recommended content to the user's login client To display.
- the device for uploading recommended content further includes a judging module for judging whether the user's credit rating reaches the second preset threshold, and if so, uploading the recommended content to the server is executed, otherwise a prompt message is given.
- this embodiment is a system embodiment corresponding to the third embodiment, and this embodiment can be implemented in cooperation with the third embodiment.
- the related technical details mentioned in the third embodiment are still valid in this embodiment, and in order to reduce repetition, they will not be repeated here.
- the related technical details mentioned in this embodiment can also be applied in the third embodiment.
- modules involved in the fourth to fifth embodiments are all logical modules.
- a logical unit can be a physical unit, or a part of a physical unit, or The combination of multiple physical units is realized.
- this embodiment does not introduce units that are not closely related to solving the technical problems proposed by the present application, but this does not indicate that there are no other units in this embodiment.
- the electronic device 600 includes: at least one processor 601; and, a memory 602 communicatively connected with the at least one processor 601; and, and a scanning device
- the communication component 603 connected in communication, the communication component 603 receives and sends data under the control of the processor 601; wherein the memory 602 stores instructions that can be executed by at least one processor 601, and the instructions are executed by the at least one processor 601 to realize: Obtain the dish ID of the recommended dish selected by the user; in response to the user's upload operation, bind the graphic information uploaded by the user with the dish ID to form recommended content; upload the recommended content to the server for the server to execute: according to the user Analyze the user’s ordering habits based on the historical order data; select the target recommended content from the pre-collected recommended content according to the ordering habits, where the recommended content is used to recommend dishes and provide an ordering channel for the dishes; push the target recommended content to The client logged in by the user is displayed
- the electronic device 600 includes: one or more processors 601 and a memory 602, and one processor 601 is taken as an example in FIG. 6.
- the processor 601 and the memory 602 may be connected through a bus or in other ways. In FIG. 6, the connection through a bus is taken as an example.
- the memory 602, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules.
- the processor 601 executes various functional applications and data processing of the device by running non-volatile software programs, instructions, and modules stored in the memory 602, that is, realizes the above-mentioned dish recommendation method.
- the memory 602 may include a program storage area and a data storage area, where the program storage area may store an operating system and an application program required by at least one function; the storage data area may store a list of options and the like.
- the memory 602 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices.
- the memory 602 may optionally include a memory 602 remotely provided with respect to the processor 601, and these remote memories 602 may be connected to an external device through a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.
- One or more modules are stored in the memory 602, and when executed by one or more processors 601, the dish recommendation method in any of the foregoing method embodiments is executed.
- the seventh embodiment of the present application relates to an electronic device.
- the electronic device 700 includes: at least one processor 701; and a memory 702 communicatively connected with the at least one processor 701; and, a scanning device
- the communication component 703 connected in communication, the communication component 703 receives and sends data under the control of the processor 701; wherein the memory 702 stores instructions that can be executed by at least one processor 701, and the instructions are executed by the at least one processor 701 to realize: Obtain the dish ID of the recommended dish selected by the user; in response to the user's upload operation, bind the graphic information uploaded by the user with the dish ID to form recommended content; upload the recommended content to the server for the server to execute: according to the user Analyze the user’s ordering habits based on the historical order data; select the target recommended content from the pre-collected recommended content according to the ordering habits, where the recommended content is used to recommend dishes and provide an ordering channel for the dishes; push the target recommended content to The client logged in by the user is displayed.
- the electronic device 700 includes: one or more processors 701 and a memory 702.
- One processor 701 is taken as an example in FIG. 7.
- the processor 701 and the memory 702 may be connected through a bus or in other ways. In FIG. 7, the connection through a bus is taken as an example.
- the memory 702, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules.
- the processor 701 executes various functional applications and data processing of the device by running non-volatile software programs, instructions, and modules stored in the memory 702, that is, realizes the above-mentioned method for uploading recommended content.
- the memory 702 may include a program storage area and a data storage area.
- the program storage area may store an operating system and an application program required by at least one function; the storage data area may store a list of options and the like.
- the memory 702 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices.
- the memory 702 may optionally include a memory 702 remotely provided with respect to the processor 701, and these remote memories 702 may be connected to an external device through a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.
- One or more modules are stored in the memory 702, and when executed by one or more processors 701, the recommended content upload method in any of the foregoing method embodiments is executed.
- the eighth embodiment of the present application relates to a non-volatile storage medium, which is used to store a computer-readable program, and the computer-readable program is used for a computer to execute some or all of the above method embodiments.
- the program is stored in a storage medium and includes several instructions to enable a device ( It may be a single-chip microcomputer, a chip, etc.) or a processor (processor) to execute all or part of the steps of the methods in the embodiments of the present application.
- the aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks or optical disks and other media that can store program codes. .
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Finance (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Accounting & Taxation (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Human Resources & Organizations (AREA)
- Health & Medical Sciences (AREA)
- Development Economics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Information Transfer Between Computers (AREA)
Abstract
一种菜品推荐方法、推荐文章上传方法、装置及电子设备，所述菜品推荐方法通过根据用户的历史下单数据分析用户的下单习惯（S101）；根据下单习惯从预先收集的推荐内容中选择目标推荐内容（S102），其中，推荐内容用于推荐菜品并为菜品提供下单通道；将目标推荐内容推送到用户的客户端进行显示（S103）。
Description
交叉引用
本申请引用于2019年09月20日递交的名称为“菜品推荐方法、推荐内容上传方法、装置及电子设备”的第201910891531.7号中国专利申请，其通过引用被全部并入本申请。
本申请涉及信息处理技术领域，特别涉及一种菜品推荐方法、推荐文章上传方法、装置及电子设备。
随着科技及社会的不断进步，外卖行业得以快速发展，用户只需要在线上进行下单，就能享受到订单的外送服务，无需用户外出，为用户节约了不少时间。为了帮助用户更快地从繁多的外卖商铺中找到自己感兴趣的商铺，在外卖应用的首页，往往会依据商铺与用户的距离、商家的活动信息等的综合排名来向用户推送一些外卖商家。
发明人发现相关技术中至少存在如下问题：对于用户来说这些推荐信息的质量不高，用户仍然要花很多时间才能最终选择出下单的菜品。
发明内容
本申请实施例的目的在于提供一种菜品推荐方法、推荐文章上传方法、装置及电子设备，能够向用户准确推荐用户可能感兴趣的菜品，针对性强，同时可提高订单转化率。
为解决上述技术问题，本申请的实施例提供了一种菜品推荐方法，包括：根据用户的历史下单数据分析用户的下单习惯；根据下单习惯从预先收集的推荐内容中选择目标推荐内容，其中，推荐内容用于推荐菜品并为菜品提供下单通道；将目标推荐内容推送到用户的客户端进行显示。
本申请的实施例还提供了一种推荐内容的上传方法，其中，包括：获取 用户选择的推荐菜品的菜品身份标识；响应于用户的上传操作，将用户上传的图文信息与菜品身份标识绑定，形成推荐内容；将推荐内容上传到服务器，以供服务器执行：根据用户的历史下单数据分析用户的下单习惯；根据下单习惯从预先收集的推荐内容中选择目标推荐内容，其中，推荐内容用于推荐菜品并为菜品提供下单通道；将目标推荐内容推送到用户登录的客户端进行显示。
本申请的实施例还提供了一种菜品推荐装置，包括：分析模块，用于根据用户的历史下单数据分析用户的下单习惯；选择模块，用于根据下单习惯从预先收集的推荐内容中选择目标推荐内容，其中，推荐内容用于推荐菜品并为菜品提供下单通道；推送模块，用于将目标推荐内容推送到用户的客户端进行显示。
本申请的实施例还提供了一种电子设备，包括存储器和处理器，存储器存储计算机程序，处理器运行程序时执行根据用户的历史下单数据分析用户的下单习惯；根据下单习惯从预先收集的推荐内容中选择目标推荐内容，其中，推荐内容用于推荐菜品并为菜品提供下单通道；将目标推荐内容推送到用户的客户端进行显示。
本申请的实施例还提供了一种推荐内容的上传装置，包括：获取模块，用于获取用户选择的推荐菜品的菜品身份标识；绑定模块，用于响应于用户的上传操作，将用户上传的图文信息与菜品身份标识绑定，形成推荐内容；上传模块，用于将推荐内容上传到服务器，以供服务器执行：根据用户的历史下单数据分析用户的下单习惯；根据下单习惯从预先收集的推荐内容中选择目标推荐内容，其中，推荐内容用于推荐菜品并为菜品提供下单通道；将目标推荐内容推送到用户登录的客户端进行显示。
本申请的实施例还提供了电子设备，包括存储器和处理器，存储器存储计算机程序，处理器运行程序时执行：获取用户选择的推荐菜品的菜品身份标识；响应于用户的上传操作，将用户上传的图文信息与菜品身份标识绑定，形成推荐内容；将推荐内容上传到服务器，以供服务器执行：根据用户的历史下单数据分析用户的下单习惯；根据下单习惯从预先收集的推荐内容中选择目标推荐内容，其中，推荐内容用于推荐菜品并为菜品提供下单通道；将目标推荐内容推送到用户登录的客户端进行显示。
本申请的实施例还提供了一种非易失性存储介质，用于存储计算机可读程序，计算机可读程序用于供计算机执行如上的菜品推荐方法或推荐内容的上 传方法。
另外，目标推荐内容中包括推荐菜品的图文信息，图文信息绑定有菜品的菜品身份标识，下单通道包括与菜品的菜品身份标识对应的菜品操作页面；在将目标推荐内容推送到用户的客户端进行显示之后，还包括：响应于用户对图文信息中的图片的触发操作，依据菜品身份标识调用对应的菜品操作页面，为用户提供菜品的下单服务。
另外，用户的下单习惯至少包括待标记内容的标识信息，待标记内容包括被用户浏览的次数达到第一预设阈值而没有产生下单动作的推荐内容；在根据下单习惯从预先收集的推荐内容中选择目标推荐内容之前，还包括：标记待标记内容的状态为不可选状态；根据下单习惯从预先收集的推荐内容中选择目标推荐内容，具体为：根据下单习惯从预先收集的状态为可选状态的推荐内容中选择目标推荐内容。
另外，根据下单习惯从预先收集的推荐内容中选择目标推荐内容，具体包括：根据下单习惯确定目标菜品；若预先收集的推荐内容中存在推荐目标菜品的内容，则从推荐目标菜品的内容中确定至少一篇内容作为目标推荐内容。
另外，所述确定至少一篇推荐内容作为所述目标推荐内容具体包括：基于推荐内容的作者的信用度来确定所述目标推荐内容。
另外，作者的信用度的获取方式包括：统计预先收集的推荐内容的订单转化率；根据订单转化率确定各个推荐内容的作者的信用度，其中，订单转化率越高，信用度越高。
另外，用户的下单习惯包括下单菜品的品类；若预先收集的推荐内容中不存在推荐目标菜品的内容，则从推荐的菜品的品类与目标菜品的品类相同的推荐内容中确定至少一篇内容作为目标推荐内容。
另外，用户的下单习惯还包括历史下单菜品的好评率；从推荐目标菜品的内容中确定至少一篇内容作为目标推荐内容，具体包括：从推荐目标菜品的内容中选择推荐菜品的好评率最高的推荐内容作为目标推荐内容。
图1是根据本申请第一实施例提供的菜品推荐方法流程图；
图2是根据本申请第二实施例提供的菜品推荐方法流程图；
图3是根据本申请第三实施例提供的推荐内容的上传方法流程图；
图4是根据本申请第四实施例中的菜品推荐装置结构示意图；
图5是根据本申请第五实施例中的推荐内容的上传装置结构示意图；
图6是根据本申请第六实施例中的电子设备结构示意图；
图7是根据本申请第七实施例中的电子设备结构示意图。
为使本申请实施例的目的、技术方案和优点更加清楚，下面将结合附图对本申请的各实施例进行详细的阐述。然而，本领域的普通技术人员可以理解，在本申请各实施例中，为了使读者更好地理解本申请而提出了许多技术细节。但是，即使没有这些技术细节和基于以下各实施例的种种变化和修改，也可以实现本申请所要求保护的技术方案。以下各个实施例的划分是为了描述方便，不应对本申请的具体实现方式构成任何限定，各个实施例在不矛盾的前提下可以相互结合相互引用。
本申请的第一实施例涉及一种菜品推荐方法，如图1所示，下面对本实施例的菜品推荐方法的实现细节进行具体的说明，以下内容仅为方便理解提供的实现细节，并非实施本方案的必须。
步骤S101，根据用户的历史下单数据分析用户的下单习惯。
具体地说，在服务器中，保存有各个用户的历史下单数据，包括用户的历史配送地址、下单菜品、菜品价格、配送时间、下单时间以及用户评价等等。服务器可基于这些数据，分析出用户的下单习惯，例如，根据用户的历史下单菜品，可以分析出用户常点的菜品、用户喜欢的菜品类型，根据用户的历史下单的菜品价格可以分析出用户常下单的菜品的价格区间，根据用户的历史记录中的订单配送时间和下单时间以及用户评价可以分析出用户对配送时间的要求(即订单通常需要的配送时间)等。
步骤S102，根据下单习惯从预先收集的推荐内容中选择目标推荐内容。
具体地说，收集的各个推荐内容用于推荐菜品并为推荐的菜品提供下单通道；预先收集的推荐内容中，每个推荐内容可以包含所要推荐的菜品的图文信息，这些图文信息中绑定有所要推荐的菜品的菜品身份标识，为推荐的菜品提供的下单通道包括与推荐的菜品的菜品身份标识对应的菜品操作页面，该菜品操作页面可以是菜品对应的菜品下单页面，也可能是将菜品加入购物车的操 作对应的操作页面。
在具体实施中，用户的下单习惯中可以包括用户的常点菜品以及用户下单的菜品的品类，根据用户的下单习惯中的用户常点菜品，可以确定出向用户推荐的目标菜品，再从预先收集的推荐内容中确定至少一篇目标推荐内容；在选择目标推荐内容的过程中，若预先收集的推荐内容中存在推荐目标菜品的推荐内容，则从推荐目标菜品的推荐内容中确定至少一篇推荐内容作为目标推荐内容；若预先收集的推荐内容中不存在推荐目标菜品的内容，则从推荐的菜品的品类与目标菜品的品类相同的推荐内容中确定至少一篇内容作为目标推荐内容。当预先收集的推荐内容中不存在推荐目标菜品的内容时，选择推荐与目标菜品同品类的菜品的推荐内容推荐给用户，可尽量保证推荐的内容是用户感兴趣的内容。
另外，在实际实施中，推荐内容可以是推荐文章，各个推荐文章的文章作者可以通过各种终端设备将编辑好的内容进行上传，在选择目标推荐内容时，便从各个作者上传的推荐内容中进行选择，而且，不同的作者信用度不同，在选择目标推荐菜品时，可基于推荐内容的作者的信用度来确定目标推荐内容，例如，选择的目标推荐内容的作者为各推荐目标菜品的内容的作者中信用度最高的作者。从多篇推荐内容中选择目标推荐内容时，选择信用度最高的作者所写的推荐内容，使得用户尽可能看到可靠的推荐内容。在一个例子中，作者的信用度的获取方式包括：统计预先收集的推荐内容的订单转化率；根据订单转化率确定各个推荐内容的作者的信用度，其中，订单转化率越高，信用度越高。提高订单转化率高的作者的信用度，使得该作者所写的内容有更大概率被推送给用户，从而引导用户快速下单。
在一个例子中，用户还可对文章作者进行关注，那么，在向用户推荐目标推荐内容时，就可直接推荐用户关注的作者上传的文章，这样，不但使得推荐文章为用户感兴趣的文章，而且，扩大了用户可选菜品的范围，增加量用户选择的多样性。
在一个例子中，用户的下单习惯还包括历史下单菜品的好评率；从推荐目标菜品的内容中确定至少一篇内容作为目标推荐内容，具体为：从推荐目标菜品的内容中选择推荐菜品的好评率最高的推荐内容作为目标推荐内容。评率高的菜品对应的推荐内容作为目标推荐内容推荐给用户，能够尽量保证用户体验。
步骤S103，将目标推荐内容推送到用户的客户端进行显示。
具体地说，在选择出目标推荐内容后，可将该推荐内容推送到用户的客户端首页进行显示，在客户端首页显示推荐内容，能够吸引用户的注意，使得用户能够看到这个推荐内容。在实际实施中，由于目标推荐内容中包括目标菜品的图文信息，而目标菜品的图文信息绑定了推荐的菜品的菜品身份标识，在将目标推荐内容推送到用户的客户端进行显示之后，若用户点击了该推荐内容中的图片，即用户有下单需求，此时可以响应于用户对图文信息的触发操作，依据菜品身份标识调用对应的菜品操作页面，为用户提供菜品的下单服务。
本实施例相对现有技术而言，通过根据用户的历史下单数据分析用户的下单习惯，可以使得用户的下单习惯的分析结果准确可靠；通过根据下单习惯从预先收集的推荐内容中选择目标推荐内容，并将目标推荐内容推送到用户的客户端进行显示，使得选择的目标推荐内容更符合用户的下单习惯，且可使得最终推荐给用户的内容是用户感兴趣的菜品信息，从而大大增加用户对该菜品进行下单的概率；而且，由于推荐内容是用于向用户推荐菜品并为该菜品提供下单通道的，为用户对相应的菜品进行下单操作提供了一个快捷的途径，可为用户节约下单的时间。
本申请的第二实施例涉及一种菜品推荐方法，本实施例涉及的菜品推荐方法流程图如图2所示，下面进行具体说明。
步骤S201，根据用户的历史下单数据分析用户的下单习惯。
具体地说，在本实施例中，根据用户的历史下单数据分析得到的用户的下单习惯还可包括用户多次浏览却未进行下单操作的推荐内容，这些推荐内容可作为待标记的推荐内容，也就是说，可以将被用户浏览的次数达到某个预设阈值而没有产生下单动作的推荐内容的内容挑选出来，这个预设阈值可以根据实际情况进行调整。
步骤S202，标记待标记内容的状态为不可选状态。
在具体实施中，可以对各个推荐内容添加标志位，可选状态的推荐内容的标志位统一设置为1，标记待标记内容的状态为不可选状态，即，将待标记内容的标志位置零。
步骤S203，根据下单习惯从预先收集的状态为可选状态的推荐内容中选择目标推荐内容。
在实际实施中，即从标志位为1的推荐内容中选择目标推荐内容。
步骤S204，将目标推荐内容推送到用户的客户端进行显示。
步骤S204与第一实施例中的步骤S103大致相同，为避免重复，这里不再赘述。
本实施例相对现有技术而言，当用户浏览某个推荐内容而没有进行下单操作，表明用户对这篇推荐内容并不感兴趣，将该推荐内容的状态标记为不可选状态，而在选择目标推荐内容时，仅仅从状态为可选状态的推荐内容中选择目标推荐内容，即，不会将用户不感兴趣的内容再次推荐给用户，这样，可以省去不必要的计算步骤，并且能使用户更快地找到感兴趣的菜品。
本申请的第三实施例涉及一种推荐内容的上传方法，本实施例的流程图如图3所示，下面进行具体说明。
步骤S301,获取用户选择的推荐菜品的菜品身份标识。
具体地说，用户在需要上传推荐内容时，需要先选择要要推荐的菜品，在实际实施中，终端设备可提供各个店铺中的菜品的相关词条供用户选择，当用户点击某个菜品的词条后，终端设备即可获取到与用户点击的词条对应的菜品身份标识。
步骤S302，响应于用户的上传操作，将用户上传的图文信息与菜品身份标识绑定，形成推荐内容。
具体地说，用户可对选择的菜品进行图文描述，并将这些描述信息上传到终端设备的指定位置上，当检测到用户点击上传操作后，终端设备便可将用户上传的图文信息与菜品身份标识绑定，形成推荐内容。
步骤S303，将推荐内容上传到服务器。
具体地说，在生成推荐内容之后，终端设备可将该推荐内容进行打包，并发送给相应的服务器，供该服务器执行第一实施例或第二实施例中提到的推荐内容的上传方法。
在实际实施中，在将推荐内容上传到服务器之前，可先判断用户的信用度是否达到预设阈值，若是，则再执行将推荐内容上传到服务器，否则给出提示信息。仅允许信用度高的用户上传推荐内容，可进一步保证推荐内容的可靠性。
在一个例子中，终端设备可提供一个上传推荐内容的编辑页面，用户可在这个页面中的菜品选择区选择某个店铺的一个菜品，其中该菜品的图片或小视频(可以直接使用该店铺内展示的图片或小视频，或者可以是用户自己拍摄 的)会显示在该菜品选择区，同时该菜品的图片或视频绑定了该店铺ID和该店铺内的该菜品ID；用户可在文章编辑区将编辑的文件内容导入；在检测到用户点击上传按钮后，终端设备便可生成推荐文章并将这个推荐文章发送给后台服务器，此时，该推荐内容已经绑定了相应的店铺ID和菜品ID，因此，在服务器将该推荐内容推送给下单用户后，下单用户点击推荐文章中的图片特定区域就可以直接从该店铺对该菜品进行下单操作。
不难发现，本实施例可与第一实施例或第二实施例相互配合使用，从而实现给用户推荐个性化的菜品。
本申请的第四实施例涉及一种菜品推荐装置，如图4所示，该装置包括：分析模块401，用于根据用户的历史下单数据分析用户的下单习惯；选择模块402，用于根据下单习惯从预先收集的推荐内容中选择目标推荐内容，其中，推荐内容用于推荐菜品并为菜品提供下单通道；推送模块403，用于将目标推荐内容推送到用户的客户端进行显示。
在一个例子中，目标推荐内容中包括推荐菜品的图文信息，图文信息绑定有菜品的菜品身份标识，下单通道包括与菜品的菜品身份标识对应的菜品操作页面；在将目标推荐内容推送到用户的客户端进行显示之后，还包括：响应于用户对图文信息中的图片的触发操作，依据菜品身份标识调用对应的菜品操作页面，为用户提供菜品的下单服务。
在一个例子中，用户的下单习惯至少包括待标记内容的标识信息，待标记内容包括被用户浏览的次数达到第一预设阈值而没有产生下单动作的推荐内容；在根据下单习惯从预先收集的推荐内容中选择目标推荐内容之前，还包括：标记待标记内容的状态为不可选状态；根据下单习惯从预先收集的推荐内容中选择目标推荐内容，具体为：根据下单习惯从预先收集的状态为可选状态的推荐内容中选择目标推荐内容。
在一个例子中，用户的下单习惯，至少包括用户的历史下单菜品以及下单次数；根据下单习惯从预先收集的推荐内容中选择目标推荐内容，具体包括：根据下单习惯确定目标菜品；若预先收集的推荐内容中存在推荐目标菜品的内容，则从推荐目标菜品的内容中确定至少一篇内容作为目标推荐内容。
在一个例子中，确定至少一篇推荐内容作为所述目标推荐内容具体包括：基于推荐内容的作者的信用度来确定所述目标推荐内容。
在一个例子中，作者的信用度的获取方式包括：统计预先收集的推荐内容的订单转化率；根据订单转化率确定各个推荐内容的作者的信用度，其中，订单转化率越高，信用度越高。
在一个例子中，用户的下单习惯包括下单菜品的品类；若预先收集的推荐内容中不存在推荐目标菜品的内容，则从推荐的菜品的品类与目标菜品的品类相同的推荐内容中确定至少一篇内容作为目标推荐内容。
在一个例子中，用户的下单习惯还包括历史下单菜品的好评率；从推荐目标菜品的内容中确定至少一篇内容作为目标推荐内容，具体包括：从推荐目标菜品的内容中选择推荐菜品的好评率最高的推荐内容作为目标推荐内容。
不难发现，本实施例为与第一至第二实施例中任一实施例相对应的系统实施例，本实施例可与第一至第二实施例中任一实施例互相配合实施。第一至第二实施例中提到的相关技术细节在本实施例中依然有效，为了减少重复，这里不再赘述。相应的，本实施例中提到的相关技术细节也可应用在第一实施例至第二实施例中。
本申请的第五实施例涉及一种推荐内容的上传装置，如图5所示，该装置包括：获取模块501，用于获取用户选择的推荐菜品的菜品身份标识；绑定模块502，用于响应于用户的上传操作，将用户上传的图文信息与菜品身份标识绑定，形成推荐内容；上传模块503，用于将推荐内容上传到服务器，以供服务器执行：根据用户的历史下单数据分析用户的下单习惯；根据下单习惯从预先收集的推荐内容中选择目标推荐内容，其中，推荐内容用于推荐菜品并为菜品提供下单通道；将目标推荐内容推送到用户登录的客户端进行显示。
在一个例子中，推荐内容的上传装置还包括判断模块，判断模块用于判断用户的信用度是否达到第二预设阈值，若是，则再执行将推荐内容上传到服务器，否则给出提示信息。
不难发现，本实施例为与第三实施例相对应的系统实施例，本实施例可与第三实施例互相配合实施。第三实施例中提到的相关技术细节在本实施例中依然有效，为了减少重复，这里不再赘述。相应的，本实施例中提到的相关技术细节也可应用在第三实施例中。
值得一提的是，实施例四至实施例五中所涉及到的各模块均为逻辑模块，在实际应用中，一个逻辑单元可以是一个物理单元，也可以是一个物理单元的一部分，还可以以多个物理单元的组合实现。此外，为了突出本申请的创新部 分，本实施例中并没有将与解决本申请所提出的技术问题关系不太密切的单元引入，但这并不表明本实施例中不存在其它的单元。
本申请的第六实施例涉及一种电子设备，如图6所示，该电子设备600包括：至少一个处理器601；以及，与至少一个处理器601通信连接的存储器602；以及，与扫描装置通信连接的通信组件603，通信组件603在处理器601的控制下接收和发送数据；其中，存储器602存储有可被至少一个处理器601执行的指令，指令被至少一个处理器601执行以实现：获取用户选择的推荐菜品的菜品身份标识；响应于用户的上传操作，将用户上传的图文信息与菜品身份标识绑定，形成推荐内容；将推荐内容上传到服务器，以供服务器执行：根据用户的历史下单数据分析用户的下单习惯；根据下单习惯从预先收集的推荐内容中选择目标推荐内容，其中，推荐内容用于推荐菜品并为菜品提供下单通道；将目标推荐内容推送到用户登录的客户端进行显示。
具体地，该电子设备600包括：一个或多个处理器601以及存储器602，图6中以一个处理器601为例。处理器601、存储器602可以通过总线或者其他方式连接，图6中以通过总线连接为例。存储器602作为一种非易失性计算机可读存储介质，可用于存储非易失性软件程序、非易失性计算机可执行程序以及模块。处理器601通过运行存储在存储器602中的非易失性软件程序、指令以及模块，从而执行设备的各种功能应用以及数据处理，即实现上述一种菜品推荐方法。
存储器602可以包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需要的应用程序；存储数据区可存储选项列表等。此外，存储器602可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件、或其他非易失性固态存储器件。在一些实施例中，存储器602可选包括相对于处理器601远程设置的存储器602，这些远程存储器602可以通过网络连接至外接设备。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
一个或者多个模块存储在存储器602中，当被一个或者多个处理器601执行时，执行上述任意方法实施例中的菜品推荐方法。
本申请的第七实施例涉及一种电子设备，如图7所示，该电子设备700包括：至少一个处理器701；以及，与至少一个处理器701通信连接的存储器702；以及，与扫描装置通信连接的通信组件703，通信组件703在处理器701 的控制下接收和发送数据；其中，存储器702存储有可被至少一个处理器701执行的指令，指令被至少一个处理器701执行以实现：获取用户选择的推荐菜品的菜品身份标识；响应于用户的上传操作，将用户上传的图文信息与菜品身份标识绑定，形成推荐内容；将推荐内容上传到服务器，以供服务器执行：根据用户的历史下单数据分析用户的下单习惯；根据下单习惯从预先收集的推荐内容中选择目标推荐内容，其中，推荐内容用于推荐菜品并为菜品提供下单通道；将目标推荐内容推送到用户登录的客户端进行显示。
具体地，该电子设备700包括：一个或多个处理器701以及存储器702，图7中以一个处理器701为例。处理器701、存储器702可以通过总线或者其他方式连接，图7中以通过总线连接为例。存储器702作为一种非易失性计算机可读存储介质，可用于存储非易失性软件程序、非易失性计算机可执行程序以及模块。处理器701通过运行存储在存储器702中的非易失性软件程序、指令以及模块，从而执行设备的各种功能应用以及数据处理，即实现上述一种推荐内容的上传方法。
存储器702可以包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需要的应用程序；存储数据区可存储选项列表等。此外，存储器702可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件、或其他非易失性固态存储器件。在一些实施例中，存储器702可选包括相对于处理器701远程设置的存储器702，这些远程存储器702可以通过网络连接至外接设备。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
一个或者多个模块存储在存储器702中，当被一个或者多个处理器701执行时，执行上述任意方法实施例中的推荐内容的上传方法。
上述产品可执行本申请实施例所提供的方法，具备执行方法相应的功能模块和有益效果，未在本实施例中详尽描述的技术细节，可参见本申请实施例所提供的方法。
本申请的第八实施例涉及一种非易失性存储介质，用于存储计算机可读程序，计算机可读程序用于供计算机执行上述部分或全部的方法实施例。
即，本领域技术人员可以理解，实现上述实施例方法中的全部或部分步骤是可以通过程序来指令相关的硬件来完成，该程序存储在一个存储介质中，包括若干指令用以使得一个设备(可以是单片机，芯片等)或处理器(processor) 执行本申请各个实施例方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
本领域的普通技术人员可以理解，上述各实施例是实现本申请的具体实施例，而在实际应用中，可以在形式上和细节上对其作各种改变，而不偏离本申请的精神和范围。
Claims (25)
- 一种菜品推荐方法，包括：根据用户的历史下单数据分析所述用户的下单习惯；根据所述下单习惯从预先收集的推荐内容中选择目标推荐内容，其中，所述推荐内容用于推荐菜品并为所述菜品提供下单通道；将所述目标推荐内容推送到所述用户的客户端进行显示。
- 根据权利要求1所述的菜品推荐方法，其中，所述目标推荐内容中包括推荐菜品的图文信息，所述图文信息绑定有所述菜品的菜品身份标识，所述下单通道包括与所述菜品的菜品身份标识对应的菜品操作页面；在所述将所述目标推荐内容推送到所述用户的客户端进行显示之后，还包括：响应于所述用户对所述图文信息的触发操作，依据所述菜品身份标识调用对应的菜品操作页面，为所述用户提供所述菜品的下单服务。
- 根据权利要求1所述的菜品推荐方法，其中，所述用户的下单习惯至少包括待标记内容的标识信息，所述待标记内容包括被所述用户浏览的次数达到第一预设阈值而没有产生下单动作的推荐内容；在所述根据所述下单习惯从预先收集的推荐内容中选择目标推荐内容之前，还包括：标记所述待标记内容的状态为不可选状态；所述根据所述下单习惯从预先收集的推荐内容中选择目标推荐内容，具体为：根据所述下单习惯从预先收集的状态为可选状态的推荐内容中选择目标推荐内容。
- 根据权利要求1所述的菜品推荐方法，其中，所述根据所述下单习惯从预先收集的推荐内容中选择目标推荐内容，具体包括：根据所述下单习惯确定目标菜品；若所述预先收集的推荐内容中存在推荐所述目标菜品的推荐内容，则从所述推荐所述目标菜品的推荐内容中确定至少一篇推荐内容作为所述目标推荐内容。
- 根据权利要求4所述的菜品推荐方法，其中，所述确定至少一篇推荐内 容作为所述目标推荐内容具体包括：基于所述推荐内容的作者的信用度来确定所述目标推荐内容。
- 根据权利要求5所述的菜品推荐方法，其中，所述作者的信用度的获取方式包括：统计所述预先收集的推荐内容的订单转化率；根据所述订单转化率确定各个所述推荐内容的作者的信用度，其中，订单转化率越高，信用度越高。
- 根据权利要求4所述的菜品推荐方法，其中，所述用户的下单习惯包括下单菜品的品类；若所述预先收集的推荐内容中不存在推荐所述目标菜品的内容，则从推荐的菜品的品类与所述目标菜品的品类相同的推荐内容中确定至少一篇内容作为所述目标推荐内容。
- 根据权利要求4所述的菜品推荐方法，其中，所述用户的下单习惯包括所述历史下单菜品的好评率；所述从所述推荐所述目标菜品的内容中确定至少一篇内容作为所述目标推荐内容，具体包括：从所述推荐所述目标菜品的内容中选择推荐菜品的好评率最高的推荐内容作为所述目标推荐内容。
- 一种推荐内容的上传方法，包括：获取用户选择的推荐菜品的菜品身份标识；响应于所述用户的上传操作，将所述用户上传的图文信息与所述菜品身份标识绑定，形成推荐内容；将所述推荐内容上传到服务器，以供所述服务器执行：根据用户的历史下单数据分析所述用户的下单习惯；根据所述下单习惯从预先收集的推荐内容中选择目标推荐内容，其中，所述推荐内容用于推荐菜品并为所述菜品提供下单通道；将所述目标推荐内容推送到所述用户登录的客户端进行显示。
- 根据权利要求9所述的推荐内容的上传方法，其中，在所述将所述推荐内容上传到服务器之前，还包括：判断所述用户的信用度是否达到第二预设阈值，若是，则再执行所述将所述推荐内容上传到服务器，否则给出提示信息。
- 一种菜品推荐装置，包括：分析模块，用于根据用户的历史下单数据分析所述用户的下单习惯；选择模块，用于根据所述下单习惯从预先收集的推荐内容中选择目标推荐内容，其中，所述推荐内容用于推荐菜品并为所述菜品提供下单通道；推送模块，用于将所述目标推荐内容推送到所述用户的客户端进行显示。
- 根据权利要求11所述的菜品推荐装置，其中，所述目标推荐内容中包括推荐菜品的图文信息，所述图文信息绑定有所述菜品的菜品身份标识，所述下单通道包括与所述菜品的菜品身份标识对应的菜品操作页面；在所述将所述目标推荐内容推送到所述用户的客户端进行显示之后，还包括：响应于所述用户对所述图文信息中的图片的触发操作，依据所述菜品身份标识调用对应的菜品操作页面，为所述用户提供所述菜品的下单服务。
- 根据权利要求11所述的菜品推荐装置，其中，所述用户的下单习惯至少包括待标记内容的标识信息，所述待标记内容包括被所述用户浏览的次数达到第一预设阈值而没有产生下单动作的推荐内容；在所述根据所述下单习惯从预先收集的推荐内容中选择目标推荐内容之前，还包括：标记所述待标记内容的状态为不可选状态；所述根据所述下单习惯从预先收集的推荐内容中选择目标推荐内容，具体为：根据所述下单习惯从预先收集的状态为可选状态的推荐内容中选择目标推荐内容。
- 根据权利要求11所述的菜品推荐装置，其中，所述用户的下单习惯，至少包括所述用户的历史下单菜品以及下单次数；所述根据所述下单习惯从预先收集的推荐内容中选择目标推荐内容，具体包括：根据所述下单习惯确定目标菜品；若所述预先收集的推荐内容中存在推荐所述目标菜品的内容，则从所述推荐所述目标菜品的内容中确定至少一篇内容作为所述目标推荐内容。
- 根据权利要求14所述的菜品推荐装置，其中，所述确定至少一篇推荐内容作为所述目标推荐内容具体包括：基于所述推荐内容的作者的信用度来确 定所述目标推荐内容。
- 根据权利要求15所述的菜品推荐装置，其中，所述作者的信用度的获取方式包括：统计所述预先收集的推荐内容的订单转化率；根据所述订单转化率确定各个所述推荐内容的作者的信用度，其中，订单转化率越高，信用度越高。
- 根据权利要求14所述的菜品推荐装置，其中，所述用户的下单习惯包括下单菜品的品类；若所述预先收集的推荐内容中不存在推荐所述目标菜品的内容，则从推荐的菜品的品类与所述目标菜品的品类相同的推荐内容中确定至少一篇内容作为所述目标推荐内容。
- 根据权利要求14所述的菜品推荐方法，其中，所述用户的下单习惯还包括所述历史下单菜品的好评率；所述从所述推荐所述目标菜品的内容中选择确定至少一篇内容作为所述目标推荐内容，具体包括：从所述推荐所述目标菜品的内容中选择推荐菜品的好评率最高的推荐内容作为所述目标推荐内容。
- 一种电子设备，包括存储器和处理器，所述存储器存储计算机程序，所述处理器运行程序时执行：根据用户的历史下单数据分析所述用户的下单习惯；根据所述下单习惯从预先收集的推荐内容中选择目标推荐内容，其中，所述推荐内容用于推荐菜品并为所述菜品提供下单通道；将所述目标推荐内容推送到所述用户的客户端进行显示。
- 根据权利要求19所述的电子设备，其中，所述处理器运行程序时执行如权利要求2至8中任一项所述的菜品推荐方法。
- 推荐内容的上传装置，包括：获取模块，用于获取用户选择的推荐菜品的菜品身份标识；绑定模块，用于响应于所述用户的上传操作，将所述用户上传的图文信息与所述菜品身份标识绑定，形成推荐内容；上传模块，用于将所述推荐内容上传到服务器，以供所述服务器执行：根据用户的历史下单数据分析所述用户的下单习惯；根据所述下单习惯从预先收集的推荐内容中选择目标推荐内容，其中，所述推荐内容用于推荐菜品并为所述菜品提供下单通道；将所述目标推荐内容推送到所述用户登录的客户端进行显示。
- 根据权利要求21所述推荐内容的上传装置，其中，所述推荐内容的上传装置还包括判断模块，所述判断模块用于判断所述用户的信用度是否达到第二预设阈值，若是，则再执行所述将所述推荐内容上传到服务器，否则给出提示信息。
- 一种电子设备，包括存储器和处理器，所述存储器存储计算机程序，所述处理器运行程序时执行：获取用户选择的推荐菜品的菜品身份标识；响应于所述用户的上传操作，将所述用户上传的图文信息与所述菜品身份标识绑定，形成推荐内容；将所述推荐内容上传到服务器，以供所述服务器执行：根据用户的历史下单数据分析所述用户的下单习惯；根据所述下单习惯从预先收集的推荐内容中选择目标推荐内容，其中，所述推荐内容用于推荐菜品并为所述菜品提供下单通道；将所述目标推荐内容推送到所述用户登录的客户端进行显示。
- 根据权利要求23所述的电子设备，其中，所述处理器运行程序时执行如权利要求10所述的推荐内容的上传方法。
- 一种非易失性存储介质，用于存储计算机可读程序，所述计算机可读程序用于供计算机执行如权利要求1至8中任一项所述的菜品推荐方法或权利要求9至10中任一项所述的推荐内容的上传方法。
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910891531.7A CN111310020A (zh) | 2019-09-20 | 2019-09-20 | 菜品推荐方法、推荐内容上传方法、装置及电子设备 |
| CN201910891531.7 | 2019-09-20 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| WO2021051744A1 true WO2021051744A1 (zh) | 2021-03-25 |
Family
ID=71159630
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| PCT/CN2020/076677 WO2021051744A1 (zh) | 2019-09-20 | 2020-02-25 | 菜品推荐方法、推荐内容上传方法、装置及电子设备 |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN111310020A (zh) |
| WO (1) | WO2021051744A1 (zh) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113139667B (zh) * | 2021-05-07 | 2024-02-20 | 深圳他米科技有限公司 | 基于人工智能的酒店房间推荐方法、装置、设备及存储介质 |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108038559A (zh) * | 2017-12-11 | 2018-05-15 | 张馨月 | 点菜系统 |
| CN108765209A (zh) * | 2018-05-30 | 2018-11-06 | 杨杰 | 一种智能点菜系统 |
| CN109801120A (zh) * | 2017-11-16 | 2019-05-24 | 阿里巴巴集团控股有限公司 | 线上业务的处理方法及装置 |
| WO2019128061A1 (zh) * | 2017-12-28 | 2019-07-04 | 深圳春沐源控股有限公司 | 食材存储方法和食材存储装置 |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8805339B2 (en) * | 2005-09-14 | 2014-08-12 | Millennial Media, Inc. | Categorization of a mobile user profile based on browse and viewing behavior |
| CN103763178B (zh) * | 2014-01-03 | 2016-10-05 | 腾讯科技（深圳）有限公司 | 一种信息推送方法、服务器、用户终端以及系统 |
| CN107092629A (zh) * | 2017-01-18 | 2017-08-25 | 北京小度信息科技有限公司 | 推荐方法及装置 |
| CN108550046B (zh) * | 2018-03-07 | 2021-03-02 | 创新先进技术有限公司 | 一种资源和营销推荐方法、装置及电子设备 |
| CN109033299A (zh) * | 2018-07-16 | 2018-12-18 | 深圳市谷熊网络科技有限公司 | 一种通过终端应用向用户推荐信息的方法、装置及设备 |
| CN109582898A (zh) * | 2018-10-25 | 2019-04-05 | 北京奇虎科技有限公司 | 一种新闻网页页面的生成方法及装置 |
| CN109783730A (zh) * | 2019-01-03 | 2019-05-21 | 深圳壹账通智能科技有限公司 | 产品推荐方法、装置、计算机设备和存储介质 |
| CN110264301A (zh) * | 2019-05-10 | 2019-09-20 | 拉扎斯网络科技（上海）有限公司 | 推荐方法、装置、电子设备和非易失性存储介质 |
-
2019
- 2019-09-20 CN CN201910891531.7A patent/CN111310020A/zh active Pending
-
2020
- 2020-02-25 WO PCT/CN2020/076677 patent/WO2021051744A1/zh active Application Filing
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109801120A (zh) * | 2017-11-16 | 2019-05-24 | 阿里巴巴集团控股有限公司 | 线上业务的处理方法及装置 |
| CN108038559A (zh) * | 2017-12-11 | 2018-05-15 | 张馨月 | 点菜系统 |
| WO2019128061A1 (zh) * | 2017-12-28 | 2019-07-04 | 深圳春沐源控股有限公司 | 食材存储方法和食材存储装置 |
| CN108765209A (zh) * | 2018-05-30 | 2018-11-06 | 杨杰 | 一种智能点菜系统 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111310020A (zh) | 2020-06-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP6725549B2 (ja) | 通信セッションの状態の保存 | |
| US10789304B2 (en) | Method and system for measuring user engagement with content items | |
| EP3873065B1 (en) | Content recommendation method, mobile terminal, and server | |
| US9606712B1 (en) | Placement of user interface elements in a browser based on navigation input | |
| US11410113B2 (en) | Shipping preferences population systems and related methods | |
| CN109582872B (zh) | 一种信息推送方法、装置、电子设备及存储介质 | |
| US11212301B2 (en) | Method and system for detecting abnormal online user activity | |
| US10552291B2 (en) | Method and system for measuring user engagement with content items | |
| US20140201292A1 (en) | Digital business card system performing social networking commonality comparisions, professional profile curation and personal brand management | |
| CN109451333B (zh) | 一种弹幕显示方法、装置、终端及系统 | |
| JP2015518609A (ja) | 情報を提供する方法、ウェブサーバおよびウェブブラウザ | |
| US20170220671A1 (en) | System, method, and apparatus for collecting and processing user feedback on webpages | |
| CN110457610B (zh) | 信息推荐方法、装置、终端、服务器及存储介质 | |
| CN106603672A (zh) | 数据推荐方法、服务器及终端 | |
| WO2015117558A1 (en) | Promotion method, apparatus, and system based on microblog message | |
| US9569465B2 (en) | Image processing | |
| US9330409B2 (en) | Information processing apparatus, information processing method, and information processing program | |
| CN113205382A (zh) | 确定对象的方法、装置、设备、存储介质和程序产品 | |
| WO2020063844A1 (zh) | 一种推荐关联信息的方法及其终端、服务器 | |
| CN111259274B (zh) | 信息处理方法、装置、设备以及信息显示装置 | |
| CN110232581B (zh) | 一种为用户提供优惠券的方法和装置 | |
| WO2021051744A1 (zh) | 菜品推荐方法、推荐内容上传方法、装置及电子设备 | |
| US10217142B1 (en) | Selective solicitation of user feedback for digital goods markets | |
| CN109474884B (zh) | 数据处理方法及其系统 | |
| US20120284327A1 (en) | Apparatus and methods for providing real time advice online |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| 121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 20866400 Country of ref document: EP Kind code of ref document: A1 |
|
| NENP | Non-entry into the national phase |
Ref country code: DE |
|
| 122 | Ep: pct application non-entry in european phase |
Ref document number: 20866400 Country of ref document: EP Kind code of ref document: A1 |
|
| 122 | Ep: pct application non-entry in european phase |
Ref document number: 20866400 Country of ref document: EP Kind code of ref document: A1 |