Intelligent commodity recommendation method and system

Abstract

The invention discloses an intelligent commodity recommendation method and system. According to the method, browsing behaviors of a user on a home page of an applet are monitored in real time, and when user browsing or interaction time exceeds a set threshold value and no commodity is additionally purchased, a commodity recommendation mechanism is triggered. The recommendation logic selects the commodities based on the preset dimension, and it is ensured that the recommended commodities conform to user preferences and have actual purchaseability. After the user clicks the recommended commodity, the user directly enters the order confirmation page, the purchase process is simplified, the purchase conversion rate is improved, and the method effectively improves the order transaction rate, optimizes the user experience, and promotes the sales of the commodity through the precise monitoring of the user behavior, the optimization of the recommendation logic and the simplification of the purchase process.

Description

Technical Field

[0001] This application relates to the field of product recommendation technology, and in particular to an intelligent product recommendation method and system. Background Technology

[0002] In today's digital shopping era, it's commonplace for users to select goods through various mini-programs. However, according to extensive data analysis, users spend an average of about 40 seconds browsing within a mini-program, with some users exhibiting prolonged browsing time, weak purchase intentions, and indecisiveness during the browsing process. This not only hinders the improvement of mini-program order rates but also prevents the effective resolution of product sales issues within the platform.

[0003] Traditional product recommendation methods often lack targeting and timeliness, failing to provide effective recommendations at crucial moments when users are hesitant, and thus failing to guide users to place an order. Summary of the Invention

[0004] Based on this, embodiments of this application provide an intelligent product recommendation method and system that can monitor user behavior in real time, accurately grasp recommendation opportunities, and quickly convert users' intention to place orders, thereby increasing order conversion rates, optimizing the user's shopping experience, and promoting product sales.

[0005] Firstly, a smart product recommendation method is provided, which includes:

[0006] The system monitors users' browsing behavior on the mini-program homepage in real time, starting the timer from the first time the user swipes the screen. If the user switches to another primary page during browsing, the timer pauses and resumes when the user returns to the homepage, until the set time threshold is met and no items are added to the shopping cart.

[0007] When a user's browsing or interaction time exceeds a set threshold and no products are added to the shopping cart, the product recommendation mechanism is triggered, and products are selected based on preset dimensions and recommended to the user in a set position on the mini-program homepage.

[0008] When a user clicks on the corresponding product recommendation window, the order confirmation page for that product is displayed directly to the user.

[0009] Optionally, the preset dimensions include two dimensions: local best-selling items and city best-selling items; wherein, local best-selling items are the 10 best-selling items in the vending machine in the past 14 days, and city best-selling items are the 10 best-selling items in the current city.

[0010] Optionally, the method of selecting products to recommend to users in a predetermined position on the homepage of the mini-program based on preset dimensions also includes:

[0011] If there are no recommended products in either of the two dimensions, then no recommendation will be triggered;

[0012] If there are recommended products in both dimensions, then select one product from the local best-selling products and one product from the city best-selling products for recommendation.

[0013] Optionally, when a user clicks on the corresponding product recommendation window, the order confirmation page for the corresponding product is directly displayed to the user, further including:

[0014] When the product recommendation window appears, users can still swipe up and down or perform other interactive actions without affecting their normal operation.

[0015] Users can close the pop-up window by clicking the close button in the upper right corner of the product recommendation window. Once closed, it will not be recommended again until the mini-program is destroyed in the background or reopened after a long period of time.

[0016] Optionally, when a user's browsing or interaction time exceeds a set threshold and no items are added to the shopping cart, a product recommendation mechanism is triggered, specifically including:

[0017] Real-time query of product inventory data, and classification of products by tags, categories and price ranges;

[0018] Recommended products are personalized based on the user's browsing and purchasing history.

[0019] Optionally, when a user clicks on the corresponding product recommendation window, the order confirmation page for that product is directly displayed to the user, specifically including:

[0020] Capture the user's click on a recommended product and trigger the order generation process through a front-end event listener;

[0021] The order confirmation page automatically populates information about recommended products, including product name, price, and specifications; and provides user-editable options, including quantity adjustment and delivery address selection.

[0022] The order confirmation page includes a checkout button and automatically checks inventory status. If inventory is insufficient, the user is prompted and suggestions for alternative products are provided.

[0023] Secondly, an intelligent product recommendation system is provided, which includes:

[0024] The monitoring module is used to monitor users' browsing behavior on the homepage of the mini program in real time. The timer starts from the first time the user swipes the screen. If the user switches to another first-level page during browsing, the timer is paused. The timer resumes when the user returns to the homepage, until the set time threshold is met and no products are added to the shopping cart.

[0025] The recommendation module is used to trigger a product recommendation mechanism when a user's browsing or interaction time exceeds a set threshold and no products are added to the shopping cart. The module selects products based on preset dimensions and recommends them to the user in a set position on the mini-program's homepage.

[0026] The confirmation module is used to directly display the order confirmation page for the corresponding product to the user when the user clicks on the corresponding product recommendation window.

[0027] Thirdly, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the intelligent product recommendation method described in any of the first aspects above.

[0028] Fourthly, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the intelligent product recommendation method described in any of the first aspects above.

[0029] Fifthly, a computer program product is provided, on which a computer program is stored, and when the computer program is executed by a processor, it implements the intelligent product recommendation method described in any of the first aspects above.

[0030] The beneficial effects of the technical solutions provided in this application include at least the following:

[0031] (1) By accurately monitoring users’ browsing behavior on the homepage of the mini program, products can be recommended in a timely manner when users are hesitant, and users can be directly guided to the order confirmation page, simplifying the purchase process and effectively improving the user’s purchase conversion rate.

[0032] (2) The recommendation window is displayed in a flexible manner, which does not affect the user's normal browsing operation. The user can close the recommendation pop-up at any time, which avoids the recommendation from interfering with the user and improves the user's shopping experience. Attached Figure Description

[0033] To more clearly illustrate the embodiments of this application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0034] Figure 1 A flowchart illustrating the steps of an intelligent product recommendation method provided in this application embodiment;

[0035] Figure 2 This is a diagram illustrating the architecture of an intelligent product recommendation system provided in an embodiment of this application.

[0036] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0038] In the description of this application, the terms "comprising," "having," and any variations thereof are intended to cover non-exclusive inclusion, such as a process, method, system, product, or apparatus that includes a series of steps or units, not necessarily limited to those steps or units that are expressly listed, but may also include other steps or units that are not expressly listed but are inherent to these processes, methods, products, or apparatuses, or steps or units added based on further optimizations conceived in this application.

[0039] Through extensive data analysis, it was found that users spend an average of about 40 seconds browsing in the mini-program. For some users who spend a lot of time browsing, have a weak intention to place an order, or hesitate during the browsing process, product recommendations are made to these users to increase the order rate. If the recommended products are successfully converted, it can also solve the sales problem of products within the machine to a certain extent.

[0040] If a user does not add any items to their cart within a set time during browsing, the page will recommend products to the user in an appropriate location and in an appropriate manner. To improve conversion rates, products selected through the recommendation mode will directly trigger the payment process, avoiding lengthy procedures that may cause users to abandon the payment.

[0041] Please refer to Figure 1 The diagram illustrates a flowchart of an intelligent product recommendation method provided in an embodiment of this application. This method is applied in vending machines and aims to improve the order completion rate. The method may include the following steps:

[0042] S1 monitors the user's browsing behavior on the mini-program homepage in real time. The timer starts from the first time the user swipes the screen. If the user switches to another first-level page during browsing, the timer is paused. The timer resumes when the user returns to the homepage, until the set time threshold is met and no items are added to the shopping cart.

[0043] S2: When a user's browsing or interaction time exceeds a set threshold and no products are added to the shopping cart, the product recommendation mechanism is triggered. Products are selected based on preset dimensions and recommended to the user in a set position on the mini-program's homepage.

[0044] Specifically, the preset dimensions include two dimensions: local best-selling items and city best-selling items. The local best-selling items dimension refers to the 10 best-selling items in the vending machine in the past 14 days, and the city best-selling items dimension refers to the 10 best-selling items in the current city.

[0045] The system selects products from preset dimensions and recommends them to users in designated positions on the mini-program homepage. It also includes the following steps: if there are no products to recommend under two dimensions, no recommendation will be triggered; if there are products to recommend under both dimensions, one product will be selected from the local best-selling products and one product from the city best-selling products for recommendation.

[0046] S3: When the user clicks on the corresponding product recommendation window, the order confirmation page for the corresponding product is displayed directly to the user.

[0047] In this embodiment of the application, when the product recommendation window appears, the user can still swipe up and down or perform other interactive actions without affecting the user's normal operation;

[0048] Users can close the pop-up window by clicking the close button in the upper right corner of the product recommendation window. Once closed, it will not be recommended again until the mini-program is destroyed in the background or reopened after a long period of time.

[0049] When a user's browsing or interaction time exceeds a set threshold and no items are added to the shopping cart, a product recommendation mechanism is triggered. This includes: real-time querying of product inventory data and categorizing products by tags, categories, and price ranges; and personalized sorting of recommended products based on the user's historical browsing and purchasing behavior.

[0050] When a user clicks on a recommended product, the system directly displays the order confirmation page for that product. This includes: capturing the user's click action and triggering the order generation process via a front-end event listener; automatically populating the recommended product information on the order confirmation page, including product name, price, and specifications; providing user-editable options, including quantity adjustment and delivery address selection; providing a checkout button on the order confirmation page; and automatically checking inventory status. If inventory is insufficient, the system prompts the user and provides alternative product suggestions.

[0051] The specific implementation process of the above method is given below:

[0052] 1. When a user is in continuous browsing mode on the homepage of the mini program, that is, scrolling up and down to browse products, if the browsing or interaction time exceeds 10 seconds (where 10 seconds is a backend configuration item, exceeding 10 seconds means that the timer starts from the first scroll and is triggered after 10 seconds) but no products are added to the shopping cart, product recommendations will appear here.

[0053] 2. Product recommendations will only appear on the homepage (i.e., the product list page); Special case explanation: If a user swipes for 8 seconds on the homepage and then switches to another primary page such as the shopping cart or my page, the timer will continue to run when the user returns to the homepage, until 10 seconds have elapsed before the recommendation pops up again.

[0054] 3. Only one product is recommended at a time. After clicking on the recommended product, the user will be taken directly to the order confirmation page. In other words, only one product can be purchased at a time in the recommendation mode.

[0055] 4. The recommendation logic takes products from the existing "Add to Cart" logic in the two dimensions of "Local Hot Selling" and "City Hot Selling", but still needs to follow the above-mentioned rule of recommending a maximum of 1 product at a time, and the recommended product must be a product that is currently in stock on the machine; if there are no products to recommend in the two dimensions, the recommendation will not be triggered.

[0056] 5. When the recommendation module appears, users can still swipe up and down or perform other interactive actions, in principle, without affecting the user's normal operation; and users can close the pop-up window through the "close" button in the upper right corner of the floating window; after closing the pop-up window, it will no longer be recommended this time (this time refers to: during the active period of the mini program, that is, whether it is running in the background or the user continues to browse after purchasing; if the mini program is destroyed in the background or reopened after a long time, this logic will start running again).

[0057] 6. After clicking the "Proceed to Checkout" button, you will be taken to the "Confirm Order" page. The subsequent checkout process will remain unchanged.

[0058] In optional embodiments of this application, the recommendation logic includes:

[0059] Globally recommended switch control;

[0060] Mini-program recommends switch control;

[0061] Calculate the items available for sale in stock on this machine;

[0062] Calculate the number of packaged beverages currently in stock and on sale;

[0063] Bestselling computer equipment: The 10 best-selling products in the past 14 days;

[0064] Calculate the best-selling products in a city: the 10 products with the highest ranking in the current city tag.

[0065] Please refer to Figure 2 The diagram illustrates a block diagram of an intelligent product recommendation system provided in an embodiment of this application. The system may include:

[0066] The monitoring module is used to monitor users' browsing behavior on the homepage of the mini program in real time. The timer starts from the first time the user swipes the screen. If the user switches to another first-level page during browsing, the timer is paused. The timer resumes when the user returns to the homepage, until the set time threshold is met and no products are added to the shopping cart.

[0067] The recommendation module is used to trigger a product recommendation mechanism when a user's browsing or interaction time exceeds a set threshold and no products are added to the shopping cart. The module selects products based on preset dimensions and recommends them to the user in a set position on the mini-program's homepage.

[0068] The confirmation module is used to directly display the order confirmation page for the corresponding product to the user when the user clicks on the corresponding product recommendation window.

[0069] Specific limitations regarding the intelligent product recommendation system can be found in the limitations of the intelligent product recommendation method described above, and will not be repeated here. Each module in the aforementioned intelligent product recommendation system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0070] In one embodiment, an electronic device is provided, which may be a computer, and its internal structure diagram may be as follows: Figure 3 As shown, the electronic device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The database of the computer device is used for intelligent product recommendation data. The network interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements an intelligent product recommendation method.

[0071] Those skilled in the art will understand that, Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0072] In one embodiment of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the above-described intelligent product recommendation method.

[0073] In one embodiment of this application, a computer program product is provided, including a computer program / instructions, which, when executed by a processor, implements the steps of the above-described intelligent product recommendation method.

[0074] The computer-readable storage medium and computer program product provided in this embodiment are similar in implementation principle and technical effect to the above method embodiments, and will not be repeated here.

[0075] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in M ​​forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0076] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0077] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A smart product recommendation method, characterized in that, The method includes: The system monitors users' browsing behavior on the mini-program homepage in real time, starting the timer from the first time the user swipes the screen. If the user switches to another primary page during browsing, the timer pauses and resumes when the user returns to the homepage, until the set time threshold is met and no items are added to the shopping cart. When a user's browsing or interaction time exceeds a set threshold and no products are added to the shopping cart, the product recommendation mechanism is triggered, and products are selected based on preset dimensions and recommended to the user in a set position on the mini-program homepage. When a user clicks on the corresponding product recommendation window, the order confirmation page for that product is displayed directly to the user.

2. The intelligent product recommendation method according to claim 1, characterized in that, The preset dimensions include two dimensions: local best-selling items and city best-selling items. The local best-selling items dimension refers to the 10 best-selling items in the vending machine in the past 14 days, and the city best-selling items dimension refers to the 10 best-selling items in the current city.

3. The intelligent product recommendation method according to claim 2, characterized in that, The system selects products based on preset dimensions and recommends them to users in designated locations on the mini-program's homepage. This also includes: If there are no recommended products in either of the two dimensions, then no recommendation will be triggered; If there are recommended products in both dimensions, then select one product from the local best-selling products and one product from the city best-selling products for recommendation.

4. The intelligent product recommendation method according to claim 1, characterized in that, When a user clicks on a product recommendation window, the system directly displays the order confirmation page for that product to the user, which also includes: When the product recommendation window appears, users can still swipe up and down or perform other interactive actions without affecting their normal operation. Users can close the pop-up window by clicking the close button in the upper right corner of the product recommendation window. Once closed, it will not be recommended again until the mini-program is destroyed in the background or reopened after a long period of time.

5. The intelligent product recommendation method according to claim 1, characterized in that, When a user's browsing or interaction time exceeds a set threshold and no items are added to their shopping cart, a product recommendation mechanism is triggered, specifically including: Real-time query of product inventory data, and classification of products by tags, categories and price ranges; Recommended products are personalized based on the user's browsing and purchasing history.

6. The intelligent product recommendation method according to claim 1, characterized in that, When a user clicks on a product recommendation window, the system directly displays the order confirmation page for that product to the user, including: Capture the user's click on a recommended product and trigger the order generation process through a front-end event listener; The order confirmation page automatically populates information about recommended products, including product name, price, and specifications; and provides user-editable options, including quantity adjustment and delivery address selection. The order confirmation page includes a checkout button and automatically checks inventory status. If inventory is insufficient, the user is prompted and suggestions for alternative products are provided.

7. An intelligent product recommendation system, characterized in that, The system includes: The monitoring module is used to monitor users' browsing behavior on the homepage of the mini program in real time. The timer starts from the first time the user swipes the screen. If the user switches to another first-level page during browsing, the timer is paused. The timer resumes when the user returns to the homepage, until the set time threshold is met and no products are added to the shopping cart. The recommendation module is used to trigger a product recommendation mechanism when a user's browsing or interaction time exceeds a set threshold and no products are added to the shopping cart. The module selects products based on preset dimensions and recommends them to the user in a set position on the mini-program's homepage. The confirmation module is used to directly display the order confirmation page for the corresponding product to the user when the user clicks on the corresponding product recommendation window.

8. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program that, when executed by the processor, implements the intelligent product recommendation method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the intelligent product recommendation method as described in any one of claims 1 to 6.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the intelligent product recommendation method according to any one of claims 1 to 6.

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