Sorting recommendation method and device for commodity search results

Abstract

The invention provides a sorting recommendation method and device for commodity search results. The sorting recommendation method for the commodity search results comprises the following steps: receiving a first search keyword from a first account; based on the first search keyword, a first commodity list with a preset number of commodities is output on a user interface of the shopping mall, and the first commodity list has a first sequence; in response to a preset operation for the first commodity list, a second commodity list is generated, and the second commodity list has a second sequence different from the first sequence; training the deep neural network by taking the first search keyword as input and the second commodity list as output; and receiving a second search keyword from the first account, processing the second search keyword by using the trained deep neural network, and outputting a third commodity list with a preset number of commodities associated with the second search keyword. According to the method and the device, personalized search result sorting suitable for specific users is realized, and the commodity search experience of the users can be greatly improved.

Description

Technical Field

[0001] The present disclosure relates to the field of information technology, and in particular to a method and device for ranking and recommending product search results. Background Art

[0002] With the development of Internet technology, online shopping malls have experienced rapid development. At present, product search results can be sorted according to various parameters, such as price. However, this sorting method of product search results may not be what users want. Therefore, the current sorting method of product search results cannot meet the needs of users. It is necessary to obtain the sorting method of search results expected by users, and then sort the product search results in the way expected by users when users search for products in the future. Summary of the invention

[0003] In order to solve the existing problems, the present disclosure provides a method and device for ranking and recommending product search results, 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 ranking and recommending product search results, the method comprising: receiving a first search keyword from a first account; based on the first search keyword, outputting a first product list with a preset number of products on a user interface of a shopping mall, wherein the preset number of products are associated with the first search keyword, and the first product list has a first ranking; in response to a preset operation on the first product list, generating a second product list, the second product list including the preset number of products and having a second ranking different from the first ranking; training a deep neural network with the first search keyword as input and the second product list as output; receiving a second search keyword from the first account, processing it using the trained deep neural network, and outputting a third product list with the preset number of products associated with the second search keyword.

[0006] An embodiment of the present disclosure provides a device for ranking and recommending product search results, the device for ranking and recommending product search results comprising: a keyword receiving module, configured to receive a first search keyword from a first account; a list output module, configured to output a first product list having a preset number of products on a user interface of a shopping mall based on the first search keyword, wherein the preset number of products are associated with the first search keyword, and the first product list has a first ranking; a list adjustment module, configured to generate a second product list in response to a preset operation on the first product list, wherein the second product list includes the preset number of products and has a second ranking different from the first ranking; a training module, configured to train a deep neural network with the first search keyword as input and the second product list as output; wherein the list output module is further configured to receive a second search keyword from the first account, process it using the trained deep neural network, and output a third product list having a preset number of products associated with the second search keyword.

[0007] In some embodiments, the present disclosure provides a device for ranking and recommending product search results, including: 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 method for ranking and recommending product search results.

[0008] The method for ranking and recommending product search results disclosed in the present invention achieves personalized search result ranking suitable for specific users by associating the user's specific account with the search result ranking, that is, the search result ranking can be completely different for the same search keyword of different users, which can greatly improve the user's product search experience compared to the universal search result feedback. In addition, the method for ranking and recommending product search results disclosed in the present invention can interpret the user's desired ranking method based on the user's operation on the product list, and then sort the product search results in the user's desired way in the subsequent product search process, thereby improving the user 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 The present invention is a flowchart of a method for ranking and recommending product search results according to an embodiment of the present invention.

[0011] Figure 2 It is a schematic diagram of a first product list according to an embodiment of the present disclosure.

[0012] Figure 3 4 is a schematic diagram of a second commodity list according to an embodiment of the present disclosure.

[0013] Figure 4 It is a partial module of a device for ranking and recommending commodity search results in another embodiment of the present disclosure.

[0014] Figure 5 It is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0015] 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.

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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.

[0021] Figure 1A flowchart of a method for ranking and recommending product search results of an embodiment of the present disclosure is provided. The method for ranking and recommending product search results of the present disclosure may include step S101, receiving a first search keyword from a first account. In some embodiments, the first account is an account of a specific user of a shopping mall, and when the user searches for products in the mall, the search is usually performed by searching for keywords, such as "computer".

[0022] In some embodiments, the method of the present disclosure may further include step S102, based on the first search keyword, outputting a first commodity list having a preset number of commodities on the user interface of the shopping mall, wherein the preset number of commodities is associated with the first search keyword, and the first commodity list has a first ranking. In some embodiments, when a user searches for a commodity, the search results are usually sorted by popularity, price, sales volume, etc. In some embodiments, for the convenience of display, the preset number is an integer between 5 and 10. In some embodiments, the preset number is an integer between 6 and 8. In some embodiments, the shopping mall is a cross-border e-commerce mall. Figure 2 It is a schematic diagram of a first product list according to an embodiment of the present disclosure.

[0023] In some embodiments, the method of the present disclosure may further include step S103, in response to a preset operation on the first product list, generating a second product list, wherein the second product list includes a preset number of products and has a second order different from the first order. Figure 3 is a schematic diagram of the second product list of an embodiment of the present disclosure. In some embodiments, the preset operation includes adjusting the order of the first sort of the first product list. In some embodiments, adjusting the order of the first sort of the first product list includes dragging the products in the first product list to adjust the order of the corresponding products. In some embodiments, for example, the search results page displays 10 products in the first sort. At this time, the user can drag the products in the sequence to change the order of the products, which is convenient for the user to compare the products and determine which product to buy in the end. Generally, the search results of existing shopping malls do not support users to change the order of search results, but only allow users to slide to browse products. In the present disclosure, by allowing users to change the order of the product list of search results, users are facilitated to compare products.

[0024] In some embodiments, the method of the present disclosure may further include step S104, training the deep neural network with the first search keyword as input and the second product list as output. In some embodiments, it is desirable to understand the logic of the second sorting of the second product list after the user changes it, so as to better serve the customer. In some embodiments, supervision may be performed with a minimum loss function to obtain a trained deep neural network.

[0025] In some embodiments, the method of the present disclosure may further include step S105, receiving the second search keyword from the first account, processing it using the trained deep neural network, and outputting a third product list having a preset number of products associated with the second search keyword. In some embodiments, since the internal logic of the second sorting of the second product list after the user operation change has been previously trained by the deep neural network, after receiving the second search keyword from the first account, the sorting of the third product list of products output by the trained deep neural network will better reflect the user's personal characteristics and meet the user's personalized sorting method, thereby better improving the user's shopping experience.

[0026] Therefore, by associating the user's specific account with the search result ranking, personalized search result ranking suitable for specific users is achieved. That is, the search result ranking for the same search keyword of different users can be completely different. Compared with universal search result feedback, it can greatly improve the user's product search experience.

[0027] In some embodiments, the first account is an account for the user to log in to the shopping mall. In some embodiments, the first account includes an account of another application (eg, some social applications) bound to the shopping mall.

[0028] The embodiment of the present disclosure also provides a device 400 for ranking and recommending product search results. Figure 4 The ranking recommendation device 400 of the commodity search results according to some embodiments is shown. The ranking recommendation device 400 of the commodity search results includes a keyword receiving module 401, a list output module 402, a list adjustment module 403 and a training module 404. In some embodiments, the keyword receiving module 401 is configured to receive a first search keyword from a first account. In some embodiments, the list output module 402 is configured to output a first commodity list having a preset number of commodities on a user interface of a shopping mall based on the first search keyword, wherein the preset number of commodities is associated with the first search keyword, and the first commodity list has a first ranking. In some embodiments, the list adjustment module 403 is configured to generate a second commodity list in response to a preset operation for the first commodity list, the second commodity list including a preset number of commodities and having a second ranking different from the first ranking. In some embodiments, the training module 404 is configured to train a deep neural network with the first search keyword as input and the second commodity list as output. In some embodiments, the list output module is also configured to receive a second search keyword from the first account, process it using the trained deep neural network, and output a third commodity list having a preset number of commodities associated with the second search keyword.

[0029] It should be understood that the contents described about the method for ranking and recommending product search results are also applicable to the device 400 for ranking and recommending product search results herein, and for the sake of simplicity, a detailed description is not given here.

[0030] In some embodiments, the preset operation includes adjusting the order of the first sort of the first product list. In some embodiments, adjusting the order of the first sort of the first product list includes dragging the products in the first product list to adjust the order of the corresponding products. In some embodiments, the preset number is an integer from 5 to 10. In some embodiments, the preset number is an integer from 6 to 8. In some embodiments, the shopping mall is a cross-border e-mall. In some embodiments, the first account is an account for the user to log in to the shopping mall. In some embodiments, the first account includes an account of another application bound to the shopping mall.

[0031] In addition, the present disclosure also provides a terminal, including: 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 method for ranking and recommending product search results.

[0032] In addition, the present disclosure also provides a computer storage medium, which stores program code, and the program code is used to execute the above-mentioned method for ranking and recommending product search results.

[0033] The above describes the method and device for ranking and recommending product search results 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.

[0034] Reference below Figure 5 , 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 5 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.

[0035] like Figure 5As 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.

[0036] 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 5 The 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.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0041] 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.

[0042] 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).

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] According to one or more embodiments of the present disclosure, a method for ranking and recommending product search results is provided, characterized in that the method for ranking and recommending product search results includes: receiving a first search keyword from a first account; based on the first search keyword, outputting a first product list with a preset number of products on a user interface of a shopping mall, wherein the preset number of products is associated with the first search keyword, and the first product list has a first ranking; in response to a preset operation on the first product list, generating a second product list, the second product list including the preset number of products and having a second ranking different from the first ranking; training a deep neural network with the first search keyword as input and the second product list as output; receiving a second search keyword from the first account, processing it using the trained deep neural network, and outputting a third product list with the preset number of products associated with the second search keyword.

[0048] According to one or more embodiments of the present disclosure, the preset operation includes adjusting the order of the first ranking of the first commodity list.

[0049] According to one or more embodiments of the present disclosure, adjusting the order of the first ranking of the first product list includes dragging products in the first product list to adjust the order of corresponding products.

[0050] According to one or more embodiments of the present disclosure, the preset number is an integer between 5 and 10.

[0051] According to one or more embodiments of the present disclosure, the preset number is an integer between 6 and 8.

[0052] According to one or more embodiments of the present disclosure, the shopping mall is a cross-border e-mall.

[0053] According to one or more embodiments of the present disclosure, the first account is an account used by the user to log in to the shopping mall.

[0054] According to one or more embodiments of the present disclosure, the first account includes an account of another application bound to the shopping mall.

[0055] According to one or more embodiments of the present disclosure, a device for ranking and recommending product search results is provided, characterized in that the device for ranking and recommending product search results comprises: a keyword receiving module, configured to receive a first search keyword from a first account; a list output module, configured to output a first product list having a preset number of products on a user interface of a shopping mall based on the first search keyword, wherein the preset number of products are associated with the first search keyword, and the first product list has a first ranking; a list adjustment module, configured to generate a second product list in response to a preset operation on the first product list, wherein the second product list includes the preset number of products and has a second ranking different from the first ranking; a training module, configured to train a deep neural network with the first search keyword as input and the second product list as output; wherein the list output module is further configured to receive a second search keyword from the first account, process it using the trained deep neural network, and output a third product list having a preset number of products associated with the second search keyword.

[0056] 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.

[0057] 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.

[0058] 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 ranking and recommending product search results, characterized in that: The method for ranking and recommending product search results includes: receiving a first search keyword from a first account; Based on the first search keyword, outputting a first commodity list having a preset number of commodities on a user interface of a shopping mall, wherein the preset number of commodities are associated with the first search keyword, and the first commodity list has a first ranking; In response to a preset operation on the first commodity list, generating a second commodity list, the second commodity list including the preset number of commodities and having a second sorting order different from the first sorting order; Training a deep neural network using the first search keyword as input and the second product list as output; A second search keyword is received from the first account, processed using a trained deep neural network, and a third product list having a preset number of products associated with the second search keyword is output.

2. The method for ranking and recommending product search results according to claim 1, characterized in that: The preset operation includes adjusting the order of the first ranking of the first commodity list.

3. The method for ranking and recommending product search results according to claim 2, characterized in that: Adjusting the order of the first ranking of the first commodity list includes dragging commodities in the first commodity list to adjust the order of corresponding commodities.

4. The method for ranking and recommending product search results according to claim 1, characterized in that: The preset number is an integer between 5 and 10.

5. The method for ranking and recommending product search results according to claim 4, characterized in that: The preset number is an integer between 6 and 8.

6. The method for ranking and recommending product search results according to claim 1, characterized in that: The shopping mall described is a cross-border e-mall.

7. The method for ranking and recommending product search results according to claim 1, characterized in that: The first account is the account used by the user to log in to the shopping mall.

8. The method for ranking and recommending product search results according to claim 7, characterized in that: The first account includes an account of another application bound to the shopping mall.

9. A device for ranking and recommending product search results, characterized in that: The ranking and recommendation device for commodity search results comprises: A keyword receiving module, configured to receive a first search keyword from a first account; a list output module configured to output a first commodity list having a preset number of commodities on a user interface of a shopping mall based on the first search keyword, wherein the preset number of commodities are associated with the first search keyword, and the first commodity list has a first ranking; a list adjustment module, configured to generate a second product list in response to a preset operation on the first product list, wherein the second product list includes the preset number of products and has a second sorting order different from the first sorting order; a training module, configured to train a deep neural network using the first search keyword as input and the second product list as output; The list output module is further configured to receive a second search keyword from the first account, process it using a trained deep neural network, and output a third product list having a preset number of products associated with the second search keyword.

10. A device for ranking and recommending product search results, 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 method for ranking and recommending product search results according to any one of claims 1 to 8.

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