Product recommendation method and device, electronic equipment and storage medium
By combining the characteristic information of target users and marketers and using a neural network model to calculate recommendation scores and adjust the order, the problem of marketers' preferences not being taken into account in marketing scenarios is solved, and the transaction rate of product recommendations is improved.
Patent Information
- Application Number
- CN202410281726.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-03-12
- Publication Date
- 2025-09-19
AI Technical Summary
In the marketing scenario based on shopping guides, existing technologies fail to effectively consider the selection preferences of marketers, resulting in poor recommendation effects and affecting the transaction rate.
By determining the purchase probability of target users and the recommendation probability of marketers, combined with the current scenario, a neural network model is used to calculate the recommendation score and adjust the recommendation order, comprehensively considering the preferences of users and marketers.
It improves the conversion rate of product recommendations and enhances the effectiveness of recommendations by more accurately matching user needs with marketers’ recommendation behaviors.
Smart Images
Figure CN120672412A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of computer technology, and in particular to a product recommendation method, device, electronic device, and storage medium. Background Art
[0002] In a sales-guide-based marketing scenario, users directly interact with marketers (e.g., sales guides) rather than the products themselves. In this scenario, when purchasing a product, users make a purchase based on the marketer's recommendation. Summary of the Invention
[0003] The present disclosure provides a product recommendation method, device, electronic device and storage medium.
[0004] The present disclosure adopts the following technical solutions.
[0005] In some embodiments, the present disclosure provides a product recommendation method, comprising:
[0006] Determining a purchase probability of the target user purchasing the target product based on the target user's characteristic information and the target product's characteristic information;
[0007] Determining a recommendation probability of the marketing person recommending the target product based on the marketing person's characteristic information and the characteristic information of the target product;
[0008] A recommendation result for the target product is determined based on the purchase probability and the recommendation probability of the target product.
[0009] In some embodiments, the present disclosure provides a product recommendation device, comprising:
[0010] a first calculation unit, configured to determine a purchase probability of the target user purchasing the target product based on the characteristic information of the target user and the characteristic information of the target product;
[0011] a second calculation unit, configured to determine a recommendation probability of the marketing personnel recommending the target product based on the characteristic information of the marketing personnel and the characteristic information of the target product;
[0012] A control unit is used to determine a recommendation result for the target product based on the purchase probability and the recommendation probability of the target product.
[0013] In some embodiments, the present disclosure provides an electronic device comprising: at least one memory and at least one processor;
[0014] The memory is used to store program codes, and the processor is used to call the program codes stored in the memory to execute the above method.
[0015] In some embodiments, the present disclosure provides a computer-readable storage medium for storing program code, which, when executed by a processor, prompts the processor to perform the above method.
[0016] The target product recommendation method provided by the embodiment of the present disclosure not only takes into account the purchase preferences of target users, but also takes into account the selection preferences of marketers themselves, thereby achieving better recommendation effects. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] The above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.
[0018] Figure 1 4 is a flowchart of a product recommendation method according to an embodiment of the present disclosure.
[0019] Figure 2 Schematic diagram of a product recommendation method according to an embodiment of the present disclosure.
[0020] Figure 3 Schematic diagram of a first neural network according to an embodiment of the present disclosure.
[0021] Figure 4 is a schematic diagram of a second neural network according to an embodiment of the present disclosure.
[0022] Figure 5 Schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION
[0023] It is understandable that before using the technical solutions disclosed in the various embodiments of this disclosure, the type, scope of use, usage scenarios, etc. of the personal information involved in this disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.
[0024] For example, in response to a user's active request, a prompt message is sent to the user to clearly inform the user that the operation requested will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the electronic device, application, server, storage medium, or other software or hardware that performs the operations of the disclosed technical solution based on the prompt message.
[0025] As an optional but non-limiting implementation, in response to receiving a user's active request, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. Furthermore, the pop-up window may also contain a selection control for the user to select "agree" or "disagree" to provide personal information to the electronic device.
[0026] It is understandable that the above notification and user authorization process are merely illustrative and do not limit the implementation of the present disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of the present disclosure.
[0027] It is understandable that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) must comply with the requirements of relevant laws, regulations and relevant provisions.
[0028] The following describes embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.
[0029] It should be understood that the various steps described in the method embodiments of the present disclosure can be performed sequentially and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.
[0030] As used herein, the term "including" and its variations are open-ended, i.e., "including but not limited to." The term "based on" means "based, at least in part, on." The term "one embodiment" means "at least one embodiment," the term "another embodiment" means "at least one additional embodiment," and the term "some embodiments" means "at least some embodiments." Other terms are defined in the following description.
[0031] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.
[0032] It should be noted that the modification of “one” mentioned in the present disclosure is illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as “one or more”.
[0033] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.
[0034] The solution provided by the embodiment of the present disclosure will be described in detail below with reference to the accompanying drawings.
[0035] When marketers recommend products, users directly interact with the marketer rather than the product itself. This approach typically uses a user's past purchase behavior to recommend products similar to those previously purchased. This approach doesn't consider the marketer's preferences. When presented with a product, the marketer might not recommend it to the user due to personal reasons, such as difficulty introducing the product or lack of understanding. Instead, they might recommend other products. This results in the system failing to actually introduce the recommended product to the user, impacting the conversion rate.
[0036] like Figure 1 As shown, Figure 1 4 is a flowchart of a target product recommendation method according to an embodiment of the present disclosure, which includes the following steps.
[0037] S11. Determine the purchase probability of the target user purchasing the target product based on the characteristic information of the target user and the characteristic information of the target product.
[0038] In some embodiments, the method proposed in the present disclosure can be used to determine the products recommended to the marketer and the order in which they are recommended. The method proposed in the present disclosure can be used in the marketer's terminal. The target user may be the user currently served by the marketer, that is, the marketer recommends products to the target user, and the products recommended to the marketer and the order in which they are recommended are determined by this method. The characteristic information of the target user may include the product preference information and shopping habits of the target user. The characteristic information of the target user can be obtained through the target user's historical purchase records and browsing records. The target products here are the products to be recommended to the marketer, and the number of target products can be one or more. The characteristic information of the target products may include information such as the target product type, target product price and / or target product quality. Different target products each have an independent corresponding purchase probability, and the purchase probability corresponding to the target product represents the possibility of the target user purchasing the target product.
[0039] S12. Determine the recommendation probability of the marketing personnel recommending the target product based on the characteristic information of the marketing personnel and the characteristic information of the target product.
[0040] In some embodiments, the execution order of step S11 and step S12 is not limited, and either one can be executed first or simultaneously. In some embodiments, the characteristic information of the marketing personnel includes: attribute information and recommendation information; the attribute information includes: the attributes of the marketing personnel themselves; for example, it may include: one or more of the information such as the type, work experience and preferences of the marketing personnel. The recommendation information includes: statistical information on the historical recommendation behavior of the marketing personnel. For example, it may include: products that the marketing personnel has recently recommended to users, types of products that the marketing personnel has recently recommended to users, and / or the probability of the marketing personnel recommending to users the products recommended by the terminal to the marketing personnel, etc. Through the characteristic information of the marketing personnel and the characteristic information of the target product, the possibility of the marketing personnel recommending the target product, that is, the recommendation probability of the target product, can be determined. Different target products each have their corresponding recommendation probabilities independently. Through step S12, the possibility (recommendation probability) of the marketing personnel recommending each target product can be determined.
[0041] S13. Determine a recommendation result for the target product based on the purchase probability and the recommendation probability of the target product.
[0042] In some embodiments, when a marketer recommends a target product to a target user, whether the target product will be purchased by the user depends on both the user's preferences and whether the marketer recommends the target product. Therefore, in some embodiments of the present disclosure, for a target product, not only the purchase probability of the target user purchasing the target product is considered, but also the recommendation probability of the marketer recommending the target product to the target user is considered. This determines the target product recommendation result (e.g., whether the target product is recommended, the order in which the target products are recommended, etc.), which helps to increase the target product's transaction probability.
[0043] In some embodiments of the present disclosure, in a scenario where marketers recommend target products, a method for recommending target products to marketers is proposed. For example, the target products and order to be recommended to marketers can be determined, which not only takes into account the purchase preferences of target users, but also takes into account the selection preferences of the marketers themselves, thereby making the recommendation effect better.
[0044] In some embodiments of the present disclosure, determining the recommendation result of the target product based on the purchase probability and the recommendation probability of the target product includes: determining the recommendation result of the target product based on the purchase probability, the recommendation probability and the current scenario of the target product.
[0045] In some embodiments, the current scenario refers to the current scene. Specifically, it can be categorized as a travel scenario, a dedicated shopping scenario, a strolling scenario, a specific holiday scenario, etc. In some embodiments, the probability of a target user purchasing a product is affected by the scenario. When traveling or shopping, the probability of a target user purchasing a product is higher than during normal times. When strolling, the probability of a target user purchasing a product is lower. In specific holiday scenarios, the probability of purchasing holiday-related products increases, while the probability of purchasing non-holiday-related products decreases. Therefore, the current scenario affects the final transaction probability of the target product. When determining the recommendation results for each target product, the current scenario needs to be considered to determine the recommendation results for each target product.
[0046] In some embodiments, determining a recommendation result for a target product based on the purchase probability, the recommendation probability, and the current scenario of the target product includes: determining that the recommendation score of the target product is equal to: x a ×y b , where x is the purchase probability of the target product, y is the recommendation probability of the target product, a and b are the first scenario parameters determined according to the current scenario, and b is the second scenario parameter; based on the recommendation scores of each, the recommendation result of the target product is determined.
[0047] In some embodiments, a formula for recommendation scoring is provided. The recommendation score is calculated using this formula. a and b are influenced by the current scenario; in different scenarios, a and b can be the same or different. Target products with high recommendation scores are prioritized for marketers. In some embodiments, target products with purchase probabilities or recommendation scores below a preset value may not be recommended to avoid compromising the target user's user experience. The recommendation scores for each target product are sorted in descending order to determine whether each target product is recommended and in what order.
[0048] In some embodiments of the present disclosure, determining the recommendation results of the target product includes: determining a first recommendation order of the target product; adjusting the first recommendation order to obtain a second recommendation order according to preset adjustment rules; and recommending the target product according to the second recommendation order.
[0049] In some embodiments, for each target product, a recommendation order can be determined based on a recommendation score or other method to obtain a first recommendation order. This first recommendation order may not be the final recommendation order, and preset adjustment rules may be pre-set. This may be because a first recommendation order determined solely based on purchase probability and recommendation probability may result in a poor user experience. For example, if a user is accustomed to purchasing a certain type of target product, this type of target product may be recommended in large quantities, creating an information siege. On the other hand, if there is a discrepancy between the marketer's preferences and the user's preferences, because the marketer's preferences lead to a high recommendation probability, the user may not be interested in marketing. Therefore, products that were recommended to the marketer within a recent first period (e.g., the last three days) but have not been purchased for more than a certain number of times (e.g., three times) can be removed and not recommended to the marketer. This is because these target products are likely to be of interest to the marketer but not to the user, which can easily lead to user conflicts. On the other hand, products of the same product type can be dispersed to prevent the same product from being recommended to the marketer repeatedly. After the first recommendation order is adjusted according to the preset adjustment rules, a new recommendation order for each target product is obtained, i.e., a second recommendation order. The target products can then be recommended to the marketer according to the second recommendation order.
[0050] In some embodiments of the present disclosure, the characteristic information of the target user and the characteristic information of the target product are input into a pre-trained first neural network to obtain the purchase probability of the target user purchasing the target product. Figure 3 As shown, the first neural network takes as input user features (user_feature) and product features (item_feature), and outputs the probability that the user corresponding to the input user features will purchase the product corresponding to the input product features. The first neural network can be a binary classification model, which outputs the probability after calculation using a multi-layer neural network. During training, the first neural network can be trained using historical records of different users purchasing different products.
[0051] In some embodiments of the present disclosure, the characteristic information of the marketing personnel and the characteristic information of the target product are input into a pre-trained second neural network to obtain the recommendation probability of the marketing personnel recommending the target product. Figure 4As shown, the input to the second neural network may include the marketer's feature information and the product's feature information (item_feature). The marketer's feature information may include their own attributes (sale_user_static_feature) and statistical information on their historical recommendation behavior (sale_user_stat_feature). The second neural network can be a binary model, outputting the recommendation probability of the marketer recommending the input product. During training, different marketers' behavioral records of whether they recommended the product to users after receiving the terminal's recommendation are used for training.
[0052] This disclosure proposes a specific embodiment, such as Figure 2 As shown, a user purchase rate prediction model (first neural network) and a marketer selection rate prediction model (second neural network) are used to calculate the target user's purchase probability of the target product and the marketer's recommendation probability of the target product, respectively. These two models are then combined with specific scenarios to generate a recommendation score, resulting in a first recommendation order. This is then adjusted according to a rule strategy (preset adjustment strategy) to ultimately achieve a second recommendation order as the recommendation result. The method proposed in this disclosure can improve the transaction rate of target products.
[0053] The present disclosure also provides a target product recommendation device, comprising:
[0054] a first calculation unit, configured to determine a purchase probability of the target user purchasing the target product based on the characteristic information of the target user and the characteristic information of the target product;
[0055] a second calculation unit, configured to determine a recommendation probability of the marketing personnel recommending the target product based on the characteristic information of the marketing personnel and the characteristic information of the target product;
[0056] A control unit is used to determine a recommendation result for the target product based on the purchase probability and the recommendation probability of the target product.
[0057] In some embodiments, the characteristic information of the marketing personnel includes: attribute information and recommendation information;
[0058] The attribute information includes: the attributes of the marketing personnel themselves;
[0059] The recommendation information includes: statistical information of the marketer's historical recommendation behavior.
[0060] In some embodiments, determining a recommendation result for the target product based on the purchase probability and the recommendation probability of the target product includes:
[0061] A recommendation result for the target product is determined based on the purchase probability, the recommendation probability, and the current scenario of the target product.
[0062] In some embodiments, determining a recommendation result for the target product based on the purchase probability, the recommendation probability, and the current scenario of the target product includes:
[0063] Determine the recommendation score of the target product is equal to: x a ×y b , where x is the purchase probability of the target product, y is the recommendation probability of the target product, a and b are the first scenario parameters determined according to the current scenario, and b is the second scenario parameter;
[0064] Determine a recommendation result for the target product based on the recommendation score of the target product.
[0065] In some embodiments, determining the recommendation result of the target product includes:
[0066] Determining a first recommendation order for the target product;
[0067] According to a preset adjustment rule, the first recommendation order is adjusted to obtain a second recommendation order;
[0068] The target product is recommended according to the second recommendation order.
[0069] In some embodiments, the preset adjustment rules include one or more of the following:
[0070] Remove products that were recommended to the marketer within the most recent first period but were not purchased for more than the first time;
[0071] Separate products that belong to the same product type.
[0072] In some embodiments, the characteristic information of the target user and the characteristic information of the target product are input into a pre-trained first neural network to obtain the purchase probability of the target user purchasing the target product; and / or, the characteristic information of the marketing staff and the characteristic information of the target product are input into a pre-trained second neural network to obtain the recommendation probability of the marketing staff recommending the target product.
[0073] For the embodiments of the device, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely illustrative, wherein the modules described as separation modules may or may not be separate. Some or all of the modules can be selected according to actual needs to achieve the purpose of the present embodiment. Those of ordinary skill in the art can understand and implement them without paying creative work.
[0074] The method and apparatus of the present disclosure are described above based on the embodiments and application examples. In addition, the present disclosure also provides an electronic device and a computer-readable storage medium, which are described below.
[0075] Reference below Figure 5 , which shows a schematic diagram of the structure of an electronic device (e.g., a terminal device or server) 800 suitable for implementing embodiments of the present disclosure. The terminal device in the embodiments of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. The electronic device shown in the figure is merely an example and should not limit the functionality and scope of use of the embodiments of the present disclosure.
[0076] The electronic device 800 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage device 808 into a random access memory (RAM) 803. Various programs and data required for the operation of the electronic device 800 are also stored in the RAM 803. The processing device 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.
[0077] Typically, the following devices may be connected to the I / O interface 805: an input device 806 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 807 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 808 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 809. The communication device 809 may allow the electronic device 800 to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows the electronic device 800 with various devices, it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have alternatively.
[0078] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program target product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network via the communication device 809, or installed from the storage device 808, or installed from the ROM 802. When the computer program is executed by the processing device 801, the above-mentioned functions defined in the method of the embodiment of the present disclosure are performed.
[0079] It should be noted that the computer-readable medium mentioned above in the present disclosure may 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 electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.
[0080] In some embodiments, the client and server can communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.
[0081] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.
[0082] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the method of the present disclosure.
[0083] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).
[0084] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program target products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.
[0085] The units involved in the embodiments described in this disclosure may be implemented in software or hardware, wherein the name of a unit does not necessarily limit the unit itself.
[0086] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard target products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.
[0087] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
[0088] According to one or more embodiments of the present disclosure, a product recommendation method is provided, comprising:
[0089] Determining a purchase probability of the target user purchasing the target product based on the target user's characteristic information and the target product's characteristic information;
[0090] determining a recommendation probability of the marketing person recommending the target product based on the characteristic information of the marketing person and the characteristic information of the target product;
[0091] A recommendation result for the target product is determined according to the purchase probability and the recommendation probability of the target product.
[0092] According to one or more embodiments of the present disclosure, a product recommendation method is provided, wherein the characteristic information of the marketing personnel includes: attribute information and recommendation information;
[0093] The attribute information includes: the attributes of the marketing personnel themselves;
[0094] The recommendation information includes: statistical information of the marketer's historical recommendation behavior.
[0095] According to one or more embodiments of the present disclosure, a product recommendation method is provided, which determines a recommendation result for a target product based on the purchase probability and the recommendation probability of the target product, including:
[0096] A recommendation result for the target product is determined based on the purchase probability, the recommendation probability, and the current scenario of the target product.
[0097] According to one or more embodiments of the present disclosure, a product recommendation method is provided, which determines a recommendation result for a target product based on the purchase probability, the recommendation probability, and the current scenario of the target product, including:
[0098] Determine the recommendation score of the target product is equal to: x a ×y b , where x is the purchase probability of the target product, y is the recommendation probability of the target product, a and b are the first scenario parameters determined according to the current scenario, and b is the second scenario parameter;
[0099] Determine a recommendation result for the target product based on the recommendation score of the target product.
[0100] According to one or more embodiments of the present disclosure, a product recommendation method is provided, wherein determining a recommendation result for a target product includes:
[0101] Determining a first recommendation order for the target product;
[0102] According to a preset adjustment rule, the first recommendation order is adjusted to obtain a second recommendation order;
[0103] The target product is recommended according to the second recommendation order.
[0104] According to one or more embodiments of the present disclosure, a product recommendation method is provided, wherein the preset adjustment rules include one or more of the following:
[0105] Remove products that were recommended to the marketer within the most recent first period but were not purchased for more than the first time;
[0106] Separate products that belong to the same product type.
[0107] According to one or more embodiments of the present disclosure, a product recommendation method is provided, wherein the characteristic information of the target user and the characteristic information of the target product are input into a pre-trained first neural network to obtain the purchase probability of the target user purchasing the target product; and / or,
[0108] The characteristic information of the marketing personnel and the characteristic information of the target product are input into a pre-trained second neural network to obtain a recommendation probability of the marketing personnel recommending the target product.
[0109] According to one or more embodiments of the present disclosure, a device for recommending a target product is provided, comprising:
[0110] a first calculation unit, configured to determine a purchase probability of the target user purchasing the target product based on the characteristic information of the target user and the characteristic information of the target product;
[0111] a second calculation unit, configured to determine a recommendation probability of the marketing personnel recommending the target product based on the characteristic information of the marketing personnel and the characteristic information of the target product;
[0112] A control unit is used to determine a recommendation result for the target product based on the purchase probability and the recommendation probability of the target product.
[0113] According to one or more embodiments of the present disclosure, there is provided an electronic device, including: at least one memory and at least one processor;
[0114] The at least one memory is used to store program code, and the at least one processor is used to call the program code stored in the at least one memory to execute any one of the above methods.
[0115] According to one or more embodiments of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium is used to store program code, and when the program code is executed by a processor, the processor is prompted to perform the above method.
[0116] The above description is merely a preferred embodiment of the present disclosure and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also includes other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this disclosure.
[0117] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details have been included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.
[0118] Although the subject matter has been described in language specific to structural features and / or methodological logical acts, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are merely example forms of implementing the claims.
Claims
1. A product recommendation method, characterized in that: include: Determining a purchase probability of the target user purchasing the target product based on the target user's characteristic information and the target product's characteristic information; determining a recommendation probability of the marketing person recommending the target product based on the characteristic information of the marketing person and the characteristic information of the target product; A recommendation result for the target product is determined according to the purchase probability and the recommendation probability of the target product.
2. The method according to claim 1, characterized in that The characteristic information of the marketing personnel includes: attribute information and recommendation information; The attribute information includes: the attributes of the marketing personnel themselves; The recommendation information includes: statistical information of the marketer's historical recommendation behavior.
3. The method according to claim 1, characterized in that Determining a recommendation result for the target product based on the purchase probability and the recommendation probability of the target product includes: A recommendation result for the target product is determined based on the purchase probability, the recommendation probability, and the current scenario of the target product.
4. The method according to claim 3, characterized in that Determining a recommendation result for the target product based on the purchase probability, the recommendation probability, and the current scenario of the target product includes: Determine the recommendation score of the target product is equal to: x a ×y b , where x is the purchase probability of the target product, y is the recommendation probability of the target product, a and b are the first scenario parameters determined according to the current scenario, and b is the second scenario parameter; Determine a recommendation result for the target product based on the recommendation score of the target product.
5. The method according to claim 1, wherein Determining the recommendation result of the target product includes: Determining a first recommendation order for the target product; According to a preset adjustment rule, the first recommendation order is adjusted to obtain a second recommendation order; The target product is recommended according to the second recommendation order.
6. The method according to claim 5, characterized in that The preset adjustment rules include one or more of the following: Remove products that were recommended to the marketer within the most recent first period but were not purchased for more than the first time; Separate products that belong to the same product type.
7. The method according to claim 1, characterized in that Inputting the characteristic information of the target user and the characteristic information of the target product into a pre-trained first neural network to obtain a purchase probability of the target user purchasing the target product; and / or, The characteristic information of the marketing personnel and the characteristic information of the target product are input into a pre-trained second neural network to obtain a recommendation probability of the marketing personnel recommending the target product.
8. A target product recommendation device, characterized in that: include: a first calculation unit, configured to determine a purchase probability of the target user purchasing the target product based on the characteristic information of the target user and the characteristic information of the target product; a second calculation unit, configured to determine a recommendation probability of the marketing personnel recommending the target product based on the characteristic information of the marketing personnel and the characteristic information of the target product; A control unit is used to determine a recommendation result for the target product based on the purchase probability and the recommendation probability of the target product.
9. An electronic device comprising: at least one memory and at least one processor; The at least one memory is used to store program code, and the at least one processor is used to call the program code stored in the at least one memory to execute the method according to any one of claims 1 to 7. 10 . A computer-readable storage medium, configured to store program code, wherein when the program code is executed by a processor, the processor is prompted to execute the method according to claim 1 .