Commodity recommendation method and device for shopping mall

Abstract

The invention provides a commodity recommendation method and device for a shopping mall. The commodity recommendation method comprises the following steps: receiving first image data and first text data; outputting second image data based on the first image data and the first text data; in response to a preset operation for the second image content, generating third image data; taking the first image data and the first text data as input, taking the third image data as output, taking a minimization loss function as a supervision network, and training the deep neural network; and outputting fifth image data by using the trained deep neural network and taking the fourth image data and the second text data as input. According to the commodity recommendation method of the shopping mall, by receiving the scene image data uploaded by the user, the user can be intelligently helped to recommend the target commodity, and the target commodity is displayed on the image uploaded by the user in the form of the content block, so that the user can more truly see the matching degree between the purchased commodity and the scene, and the user experience is improved. And the shopping experience of the user is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of information technology, and in particular to a product recommendation method and device for a shopping mall. Background Art

[0002] With the development of Internet technology, online shopping malls have experienced rapid development. At present, in shopping malls, products are usually searched by keywords or pictures. The products searched by this product search method usually only include the products themselves. Customers cannot intuitively feel whether the searched products match the scene or environment used to place the products, and thus cannot provide customers with sufficient reference. In addition, the degree of intelligence is insufficient, which affects the user experience. Further improvements in this regard are expected. Summary of the invention

[0003] To solve the existing problems, the present invention provides a method and device for recommending goods in a shopping mall, a terminal and a storage medium.

[0004] The present disclosure adopts the following technical solutions.

[0005] An embodiment of the present disclosure provides a method for recommending goods in a shopping mall, the method comprising: receiving first image data and first text data; outputting second image data based on the first image data and the first text data, wherein the second image data comprises first image content of the first image data and second image content corresponding to the first text data, the second image content comprises goods on sale in the shopping mall, the first image content comprises a display scene of the goods on sale, the second image content is displayed in the form of an independent content block, and the content block is operable to replace the displayed image content; generating third image data in response to a preset operation on the second image content; training a deep neural network with the first image data and the first text data as input, the third image data as output, and a minimized loss function as a supervision network to obtain a trained deep neural network; using the trained deep neural network, taking fourth image data and second text data as input, outputting fifth image data, wherein the fifth image data comprises the third image content of the fourth image data and the fourth image content corresponding to the second text data.

[0006] In some embodiments, the present disclosure provides a product recommendation device for a shopping mall, comprising: a data receiving module configured to receive first image data and first text data; a data output module configured to output second image data based on the first image data and the first text data, wherein the second image data includes first image content of the first image data and second image content corresponding to the first text data, the second image content includes the goods on sale in the shopping mall, the first image content includes the display scene of the goods on sale, and the second image content is displayed in the form of an independent content block, and the content block is operable to replace the displayed image content; a data generation module configured to generate third image data in response to a preset operation on the second image content; a training module configured to take the first image data and the first text data as input, take the third image data as output, and take the minimization loss function as the supervision network to train a deep neural network to obtain a trained deep neural network; wherein the data output module is further configured to use the trained deep neural network to take fourth image data and second text data as input to output fifth image data, wherein the fifth image data includes the third image content of the fourth image data and the fourth image content corresponding to the second text data.

[0007] In some embodiments, the present disclosure provides a product recommendation device for a shopping mall, comprising: at least one memory and at least one processor; wherein the memory is used to store program code, and the processor is used to call the program code stored in the memory to execute the above-mentioned product recommendation method for the shopping mall.

[0008] The product recommendation method of the shopping mall disclosed in the present invention can intelligently help users recommend target products by receiving scene image data uploaded by users, and the target products are displayed in the form of content blocks on the images uploaded by users, so that users can more realistically see the matching degree between the purchased products and the scenes, thereby improving the user's shopping experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying 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.

[0010] Figure 1 It is a flow chart of a product recommendation method of a shopping mall according to an embodiment of the present disclosure.

[0011] Figure 2 A schematic diagram showing a content block displaying second image content in second image data according to an embodiment of the present disclosure is shown.

[0012] Figure 3 It is a partial module of a commodity recommendation device of a shopping mall according to another embodiment of the present disclosure.

[0013] Figure 4 It is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0014] Embodiments of the present disclosure will be described in more detail below 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 being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0015] It should be understood that the various steps described in the method embodiments of the present disclosure can be performed in sequence 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.

[0016] The term "including" and its variations used herein are open inclusions, 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"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0017] It should be noted that the concepts such as "first" and "second" mentioned in the present 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.

[0018] 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 it should be understood as “one or more” unless otherwise clearly indicated in the context.

[0019] 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.

[0020] Figure 1A flowchart of a method for recommending goods in a shopping mall according to an embodiment of the present disclosure is provided. The method for recommending goods in a shopping mall according to the present disclosure may include step S101, receiving first image data and first text data. In some embodiments, the first image data is an image uploaded by a user, and the first text data is a keyword searched by the user in the shopping mall. In some embodiments, there is a control on the homepage of the shopping mall for users to upload image data. In some embodiments, the first text data may be text data converted from user voice. For example, a user may take an image of his or her own living room and upload it to the shopping mall, while inputting text data such as "TV" or "table".

[0021] In some embodiments, the method of the present disclosure may further include step S102, outputting second image data based on the first image data and the first text data. In some embodiments, the second image data includes the first image content of the first image data and the second image content corresponding to the first text data, the second image content includes the goods on sale in the shopping mall, and the first image content includes the display scene of the goods on sale. In some embodiments, for example, the first image content includes a TV table or a TV cabinet, and the second image content includes a TV. For example, if a user uploads an image and text data "TV" around the TV cabinet in his living room, then in the output second image data, by identifying the possible location of the TV in the first image data, the second image content corresponding to "TV" is added on the basis of the first image data uploaded by the user. For example, in the output second image data, a TV on sale in the shopping mall is placed on the TV cabinet.

[0022] In some embodiments, the second image content is displayed in the form of an independent content block, and the content block is operable to replace the displayed image content. In some embodiments, for example, the user can drag the content block to place the TV in another position, while the first image content on the second image data remains unchanged. In some embodiments, the displayed image content can be replaced by sliding the content displayed in the content block. For example, the user can display different commercial TVs on sale by sliding the content block displaying the TV, so that the user can choose a TV that better matches the living room environment or scene. It should be understood that the above embodiments are merely exemplary and are not intended to limit the present disclosure.

[0023] In some embodiments, the method of the present disclosure may further include step S103, generating third image data in response to a preset operation on the second image content. For example, a user may drag the content block where the second image content is located to change the display position of the second image content. For example, a user may slide (e.g., slide up, slide down, slide left, slide right, etc.) the content block where the second image content is located to change the specific commodity on sale corresponding to the second image content. In some embodiments, a user may, for example, click on the content block where the second image content is located, and the details page of the second image content may be displayed. After the user performs the operation, the third image data may be generated. The difference between the third image data and the second image data is that the display position of the content block of the second image content and the specific commodity displayed may be different.

[0024] In some embodiments, the method of the present disclosure may further include step S104, taking the first image data and the first text data as input, taking the third image data as output, and minimizing the loss function as the supervision network, training the deep neural network to obtain a trained deep neural network. In some embodiments, taking the first image data and the first text data input by the user as input, taking the satisfactory third image data adjusted by the user as output, training the deep neural network, and iteratively training the supervision network to obtain a trained deep neural network.

[0025] In some embodiments, the method disclosed herein may further include step S105, using a trained deep neural network, taking the fourth image data and the second text data as input, and outputting the fifth image data, wherein the fifth image data includes the third image content of the fourth image data and the fourth image content corresponding to the second text data. In some embodiments, the fourth image data is similar to the first image data, and is an image input by the user, and the second text data is similar to the first text data, and is text data input by the user. In some embodiments, the second text data may be text data converted from voice data. In some embodiments, the fifth image data is based on the third image content of the fourth image data, and the fourth image content corresponding to the second text data is added, for example, a TV for sale is placed on the TV cabinet in the user's living room image. By using a trained deep neural network, the recommendation of the goods for sale of the output fifth image data is made more intelligent, the matching degree is improved, and it is more convenient for users to select target goods for sale, thereby improving the shopping experience and satisfaction of users.

[0026] Therefore, by adopting the product recommendation method of the shopping mall disclosed in the present invention, users can visually experience the state of the target product on sale after being placed in the scene displayed in the uploaded image, which is more valuable for reference than simply looking at the product on sale itself, improves the user's shopping experience, and helps to reduce the return rate.

[0027] In some embodiments, the second image data also includes a fifth image content that matches the first image content, wherein the fifth image content includes another item on sale in the shopping mall. In some embodiments, in addition to displaying the second image content corresponding to the first text data in the second image data based on the first image content, one or more other related items on sale can also be recommended based on the scene of the first image content. For example, assuming that a user uploads an image and text data "TV" around the TV cabinet in the living room, in addition to adding a content block showing the TV at the TV cabinet position on the image, other accessories can also be recommended, such as vases or other small ornaments. If the user feels that the small ornament matches or matches the corresponding scene, he or she will also be interested in purchasing the corresponding item on sale. This recommendation for a specific scene can increase the probability of a transaction with the user.

[0028] In some embodiments, the second image content and the fifth image content are highlighted. In some embodiments, the highlighting can be performed by displaying a box or highlighting. In some embodiments, the highlighting includes highlighting. In some embodiments, the highlighting includes using different fill colors as the background. In some embodiments, by highlighting the second image content and the fifth image content, it is convenient for users to identify which products are recommended by the mall for the image scene uploaded by the user.

[0029] In some embodiments, similar to the second image content, the fifth image content is displayed in the form of another independent content block, and the other content block is operable to change the displayed image content. For example, the user can drag the other content block, the user can slide the other content block to change the specific sale items displayed therein, and the user can also click the delete control or drag out the display space to delete the other content block.

[0030] Figure 2 A schematic diagram of a content block displaying the second image content in the second image data is shown. The content block is highlighted in a bold rectangular frame, and the user can drag the content block to change the display position of the content block, for example, drag to the left or drag to the right, etc. In addition, the user can slide the area in the content block, and the specific goods on sale displayed in the content block can be changed. For example, the user may not be satisfied with the TV displayed in the current content block. At this time, the user can slide to change a different TV to facilitate the user's selection. In addition, the user can click on the content block to display the details of the currently displayed goods on sale. By combining the display of the goods on sale with the images uploaded by the user, the user can visually feel the matching degree between the purchased goods and the corresponding display scene, thereby improving the user's shopping experience.

[0031] The embodiment of the present disclosure also provides a product recommendation device 400 for a shopping mall. Figure 3 The product recommendation device 400 of a shopping mall according to some embodiments is shown. The product recommendation device 400 of the shopping mall includes a data receiving module 401, a data output module 402, a data generating module 403 and a training module 404. In some embodiments, the data receiving module 401 is configured to receive first image data and first text data. In some embodiments, the data output module 402 is configured to output second image data based on the first image data and the first text data, wherein the second image data includes the first image content of the first image data and the second image content corresponding to the first text data, the second image content includes the goods on sale in the shopping mall, the first image content includes the display scene of the goods on sale, and the second image content is displayed in the form of an independent content block, and the content block can be operated to replace the displayed image content. In some embodiments, the data generating module 403 is configured to generate third image data in response to a preset operation for the second image content. In some embodiments, the training module 404 is configured to take the first image data and the first text data as input, take the third image data as output, and take the minimization loss function as the supervision network to train the deep neural network to obtain a trained deep neural network. In some embodiments, the data output module is also configured to utilize a trained deep neural network, take the fourth image data and the second text data as input, and output fifth image data, wherein the fifth image data includes the third image content of the fourth image data and the fourth image content corresponding to the second text data.

[0032] It should be understood that the contents described about the product recommendation method for a shopping mall are also applicable to the product recommendation device 400 for a shopping mall herein, and for the sake of simplicity, a detailed description is not given here.

[0033] In some embodiments, the first text data and the second text data are text data converted from voice data. In some embodiments, the second image data also includes a fifth image content that matches the first image content, wherein the fifth image content includes another item on sale in the shopping mall. In some embodiments, the second image content and the fifth image content are highlighted. In some embodiments, the fifth image content is displayed in the form of another independent content block, and the other content block is operable to replace the displayed image content. In some embodiments, highlighting includes highlighting. In some embodiments, highlighting includes using different fill colors as the background. In some embodiments, the first image content includes a TV table and the second image content includes a TV.

[0034] In addition, the present disclosure also provides a product recommendation device for a shopping mall, comprising: at least one memory and at least one processor; wherein the memory is used to store program code, and the processor is used to call the program code stored in the memory to execute the above-mentioned product recommendation method for the shopping mall.

[0035] In addition, the present disclosure also provides a computer storage medium, which stores program codes, and the program codes are used to execute the above-mentioned product recommendation method of the shopping mall.

[0036] The above describes the shopping mall product recommendation method and device of the present disclosure based on the embodiments and application examples. In addition, the present disclosure also provides a terminal and a storage medium, which are described below.

[0037] Reference below Figure 4 , which shows a schematic diagram of the structure of an electronic device (such as a terminal device or a server) 500 suitable for implementing the embodiment of the present disclosure. The terminal device in the embodiment of the present disclosure may include but is not limited to mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0038] like Figure 4 As shown, the electronic device 500 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0039] Typically, the following devices may be connected to the I / O interface 505: an input device 506 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 507 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 508 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 509. The communication device 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 4The electronic device 500 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0040] 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 product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by the processing device 501, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0041] 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 above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, 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 containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than computer readable storage media, which may send, propagate or transmit 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 appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0042] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may 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.

[0043] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0044] 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.

[0045] 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 separate software package, partially on the user's computer and partially on a remote computer, or entirely on a 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., via the Internet using an Internet service provider).

[0046] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square 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 square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0047] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0048] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, 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 products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0049] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may 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.

[0050] According to one or more embodiments of the present disclosure, a method for recommending goods in a shopping mall is provided, the method comprising: receiving first image data and first text data; outputting second image data based on the first image data and the first text data, wherein the second image data comprises first image content of the first image data and second image content corresponding to the first text data, the second image content comprises goods on sale in the shopping mall, the first image content comprises a display scene of the goods on sale, the second image content is displayed in the form of an independent content block, and the content block is operable to replace the displayed image content; generating third image data in response to a preset operation on the second image content; training a deep neural network with the first image data and the first text data as input, the third image data as output, and a minimized loss function as a supervisory network to obtain a trained deep neural network; using the trained deep neural network, taking fourth image data and second text data as input, outputting fifth image data, wherein the fifth image data comprises the third image content of the fourth image data and the fourth image content corresponding to the second text data.

[0051] According to one or more embodiments of the present disclosure, the first text data and the second text data are text data converted from voice data.

[0052] According to one or more embodiments of the present disclosure, the second image data further includes fifth image content matching the first image content, wherein the fifth image content includes another commodity on sale in the shopping mall.

[0053] According to one or more embodiments of the present disclosure, the second image content and the fifth image content are highlighted.

[0054] According to one or more embodiments of the present disclosure, the fifth image content is displayed in the form of another independent content block, and the other content block is operable to replace the displayed image content.

[0055] According to one or more embodiments of the present disclosure, the highlighting includes highlighting.

[0056] According to one or more embodiments of the present disclosure, the highlighting includes using a different fill color as the background.

[0057] According to one or more embodiments of the present disclosure, the first image content includes a TV table, and the second image content includes a TV.

[0058] According to one or more embodiments of the present disclosure, a product recommendation device for a shopping mall is provided, the product recommendation device for a shopping mall comprising: a data receiving module, configured to receive first image data and first text data; a data output module, configured to output second image data based on the first image data and the first text data, wherein the second image data comprises first image content of the first image data and second image content corresponding to the first text data, the second image content comprises products on sale in the shopping mall, the first image content comprises a display scene of the products on sale, and the second image content is displayed in the form of an independent content block, and the content block is operable to replace the displayed image content; a data generation module, configured to generate third image data in response to a preset operation for the second image content; a training module, configured to take the first image data and the first text data as input, the third image data as output, and minimize the loss function as a supervised network to train the deep neural network to obtain a trained deep neural network; wherein the data output module is also configured to use the trained deep neural network, take the fourth image data and the second text data as input, and output the fifth image data, wherein the fifth image data includes the third image content of the fourth image data and the fourth image content corresponding to the second text data.

[0059] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. 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 a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

[0060] 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 are 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.

[0061] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. A method for recommending goods in a shopping mall, characterized in that: The product recommendation method of the shopping mall includes: receiving first image data and first text data; Based on the first image data and the first text data, output second image data, wherein the second image data includes first image content of the first image data and second image content corresponding to the first text data, the second image content includes commodities on sale in a shopping mall, the first image content includes a display scene of the commodities on sale, and the second image content is displayed in the form of an independent content block, and the content block is operable to change the displayed image content; generating third image data in response to a preset operation on the second image content; Taking the first image data and the first text data as input, taking the third image data as output, and minimizing the loss function as a supervision network, training a deep neural network to obtain a trained deep neural network; Utilizing the trained deep neural network, taking the fourth image data and the second text data as input, outputting fifth image data, wherein the fifth image data includes the third image content of the fourth image data and the fourth image content corresponding to the second text data.

2. The method for recommending goods in a shopping mall according to claim 1, characterized in that: The first text data and the second text data are text data converted from voice data.

3. The method for recommending goods in a shopping mall according to claim 1, characterized in that: The second image data also includes fifth image content matching the first image content, wherein the fifth image content includes another commodity on sale in the shopping mall.

4. The method for recommending goods in a shopping mall according to claim 3, characterized in that: The second image content and the fifth image content are highlighted.

5. The method for recommending goods in a shopping mall according to claim 4, characterized in that: The fifth image content is displayed in the form of another independent content block, and the other content block is operable to replace the displayed image content.

6. The method for recommending goods in a shopping mall according to claim 4, characterized in that: The highlighting includes highlighting.

7. The method for recommending goods in a shopping mall according to claim 4, characterized in that: The highlighting includes using a different fill color as the background.

8. The method for recommending goods in a shopping mall according to claim 1, characterized in that: The first image content includes a TV table, and the second image content includes a TV.

9. A product recommendation device for a shopping mall, characterized in that: The commodity recommendation device of the shopping mall includes: A data receiving module, configured to receive first image data and first text data; a data output module configured to output second image data based on the first image data and the first text data, wherein the second image data includes first image content of the first image data and second image content corresponding to the first text data, the second image content includes commodities on sale in a shopping mall, the first image content includes a display scene of the commodities on sale, and the second image content is displayed in the form of an independent content block, and the content block is operable to replace the displayed image content; a data generating module, configured to generate third image data in response to a preset operation on the second image content; A training module is configured to take the first image data and the first text data as input, take the third image data as output, and use a minimization loss function as a supervision network to train the deep neural network to obtain a trained deep neural network; Among them, the data output module is also configured to use the trained deep neural network, take the fourth image data and the second text data as input, and output the fifth image data, wherein the fifth image data includes the third image content of the fourth image data and the fourth image content corresponding to the second text data.

10. A product recommendation device for a shopping mall, comprising: at least one memory and at least one processor; The at least one memory is used to store program codes, and the at least one processor is used to call the program codes stored in the at least one memory to execute the product recommendation method for a shopping mall according to any one of claims 1 to 8.

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