Recommendation method, device, electronic device and storage medium

Abstract

The present application provides a recommendation method, device, electronic device and storage medium, which obtain the number of times a user's clothes are washed within a preset time period; determine target clothes from the clothes based on the number of times they are washed; input an image of the target clothes into a pre-established neural network model to determine characteristic attributes of the target clothes; determine the user's clothing style based on the characteristic attributes; and recommend clothes to the user based on the clothing style. Since the number of times a user's clothes are washed is related to the user's dressing habits, the user's favorite clothing style can be determined more accurately, thereby making more accurate clothing recommendations to the user based on the clothing style.

Description

Technical Field

[0001] The present application relates to the field of recommendation technology, and in particular to a recommendation method, device, electronic device and storage medium. Background Art

[0002] At present, in terms of clothing recommendations, the user-based collaborative filtering algorithm should be more widely used to recommend clothing to the current user. The user-based collaborative filtering algorithm must first find other users similar to the new user based on the user's historical behavior information. At the same time, based on the evaluation information of other clothing by these similar users, it predicts the clothing style that the current user may like, and then recommends it to the user. It can be seen that the user-based collaborative filtering algorithm determines the clothing style that the user may like through other users, and will be related to other users. There may be a problem of inaccurate determination of the clothing style that the user likes, resulting in an inability to make more accurate recommendations for the user. Summary of the Invention

[0003] In response to the above problems, the present application provides a recommendation method, device, electronic device and storage medium, which can more accurately determine the user's clothing style, thereby making more accurate clothing recommendations for the user based on the clothing style.

[0004] This application provides a recommended method, including:

[0005] Get the number of times the user's clothes have been washed within a preset time period;

[0006] determining target laundry from the laundry based on the number of washes;

[0007] Inputting the image of the target clothing into a pre-established neural network model to determine characteristic attributes of the target clothing;

[0008] determining the user's clothing style based on the characteristic attributes;

[0009] Recommend clothing to the user based on the clothing style.

[0010] In some embodiments, determining target laundry from the laundry based on the number of washes includes:

[0011] sorting the clothes based on the number of washing times to obtain a sorting result;

[0012] Target laundry is determined from the laundry based on the ranking result.

[0013] In some embodiments, the feature attributes include: one or more of: color feature, pattern feature, material feature, sleeve length feature, garment length feature, skirt length feature, trouser length feature, lapel feature and neckline feature.

[0014] In some embodiments, determining the user's clothing style based on the characteristic attributes includes:

[0015] Determine the vector corresponding to each feature attribute;

[0016] The vectors corresponding to each feature attribute are concatenated to obtain a style type label vector representing the user's clothing style.

[0017] In some embodiments, recommending clothing to the user based on the clothing style includes:

[0018] Calculating the Euclidean distance between the style type label vector and the style type label of the clothing to be recommended;

[0019] Determining target recommended clothing based on the Euclidean distance and the distance threshold;

[0020] The target recommended clothing is recommended to the user.

[0021] In some embodiments, obtaining the number of times the user's clothes are washed within a preset time period includes:

[0022] Get the washing information of the washing machine within the preset time;

[0023] The number of times the user's clothes are to be washed within a preset time period is determined based on the laundry information of the clothes.

[0024] In some embodiments, the method further comprises:

[0025] Acquire a sample data set, wherein the sample data in the sample data set includes: images of clothing and characteristic attribute labels of the clothing;

[0026] Training is performed based on the sample data set to obtain a neural network model, wherein the neural network model takes the image of the clothing as input and the characteristic attribute label of the clothing as output.

[0027] The present invention provides a recommendation device, including:

[0028] An acquisition module is used to obtain the number of times the user's clothes have been washed within a preset time period;

[0029] a first determining module, configured to determine target clothing from the clothing based on the number of washes;

[0030] A neural network module, configured to input the image of the target clothing into a pre-established neural network model to determine characteristic attributes of the target clothing;

[0031] a second determining module, configured to determine the user's clothing style based on the characteristic attributes;

[0032] A recommendation module is used to recommend clothing to the user based on the clothing style.

[0033] An embodiment of the present application provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, any one of the above-mentioned recommendation methods is performed.

[0034] An embodiment of the present application provides a storage medium, which stores a computer program that can be executed by one or more processors and can be used to implement any of the above-mentioned recommendation methods.

[0035] The present application provides a recommendation method, device, electronic device and storage medium, which obtain the number of times a user's clothes are washed within a preset time period; determine target clothes from the clothes based on the number of times they are washed; input an image of the target clothes into a pre-established neural network model to determine characteristic attributes of the target clothes; determine the user's clothing style based on the characteristic attributes; and recommend clothes to the user based on the clothing style. Since the number of times a user's clothes are washed is related to the user's dressing habits, the user's favorite clothing style can be determined more accurately, thereby making more accurate clothing recommendations to the user based on the clothing style. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Hereinafter, the present application will be described in more detail based on embodiments with reference to the accompanying drawings.

[0037] Figure 1 A schematic diagram of an implementation flow of a recommended method provided in an embodiment of the present application;

[0038] Figure 2 A schematic diagram of the structure of a neural network model provided in an embodiment of the present application;

[0039] Figure 3 A flowchart of a recommended method provided in an embodiment of the present application;

[0040] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0041] In the drawings, like components are given like reference numerals, and the drawings are not drawn to scale. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0043] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0044] If similar descriptions of "first\second\third" appear in the application documents, the following explanation will be added. In the following description, the terms "first\second\third" are only used to distinguish similar objects and do not represent a specific order for the objects. It can be understood that "first\second\third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0046] To address the problems in the related art, embodiments of the present application provide a recommendation method, which is applied to an electronic device, such as a computer, a mobile terminal, etc. The functions implemented by the recommendation method provided in embodiments of the present application can be implemented by a processor of the electronic device calling program code, wherein the program code can be stored in a computer storage medium.

[0047] This application embodiment provides a recommendation method. Figure 1 A schematic diagram of a recommended method for implementing the present invention is provided in the embodiment of the present invention. Figure 1 As shown, including:

[0048] Step S101, obtaining the number of times the user's clothes are washed within a preset time period.

[0049] In the embodiment of the present application, the preset time period can be configured, for example, it can be configured to one month, three months, etc. The clothing may include: tops, bottoms, skirts, mix and match, etc., and the mix and match includes tops and bottoms.

[0050] In the embodiment of the present application, the number of washing times refers to the number of times the laundry device washes the clothes. The washing device may be a washing machine.

[0051] In an embodiment of the present application, the user can input the number of times the user's clothes are to be washed within a preset time period through an input device. For example, the number of times the user's clothes are to be washed within a preset time period can be input through a mobile phone APP.

[0052] In some embodiments, the electronic device can obtain laundry information within a preset time period and use the laundry information to determine the number of times the user's clothes have been washed within the preset time period. In embodiments of the present application, the washing machine can capture images of the clothes during the washing process, and the electronic device can count the images of the clothes to determine the number of times the user's clothes have been washed.

[0053] Step S102: determining target clothing from the clothing based on the number of washes.

[0054] In the embodiment of the present application, by sorting based on the number of washes, clothes with more washes can be obtained from the sorting results, thereby determining the target clothes.

[0055] For example, Table 1 is a schematic table of the ranking results of the top 3 of a top provided in an embodiment of the present application. As shown in Table 1,

[0056] Table 1 is a schematic table of the ranking results of the top 3 of a kind of top

[0057] Jacket Cleaning times Top 1 upper_num1 Top 2 upper_num2 Top 3 upper_num3

[0058] Table 2 is a schematic table of the top 3 ranking results of a pair of bottoms provided in an embodiment of the present application, as shown in Table 2:

[0059] Table 2 is a schematic table of the ranking results of the top 3 of a type of bottoms

[0060] Bottoms Cleaning times Bottoms 1 down_num1 Bottoms 2 down_num2 Bottoms 3 down_num3

[0061] Table 3 is a schematic table of the top 3 ranking results of a mashup provided in an embodiment of the present application, as shown in Table 3:

[0062] Table 3 is a schematic diagram of the sorting results of a mix

[0063]

[0064]

[0065] Sort the washing times of tops, bottoms, and mixed and matched items, and select the top 3 items with the highest washing times to obtain the target items. When mixed and matched, the washing times are the times the tops and bottoms are washed at the same time.

[0066] In the embodiment of the present application, the target clothing may include: a top, a bottom, or a mixed match.

[0067] Step S103: inputting the image of the target clothing into a pre-established neural network model to determine characteristic attributes of the target clothing.

[0068] In an embodiment of the present application, feature attributes may include: one or more of: color features, pattern features, material features, sleeve length features, clothing length features, skirt length features, trouser length features, lapel features and neckline features.

[0069] In an embodiment of the present application, sample data can be obtained, and the sample data in the sample data set includes: images of clothing and characteristic attribute labels of the clothing; training is performed based on the sample data set to obtain a neural network model, wherein the neural network model takes the images of clothing as input and the characteristic attribute labels of clothing as output.

[0070] In the embodiment of the present application, the backbone framework of the neural network model can use resnet101, and multiple task classifiers all use softmax classifiers.

[0071] For example, it is possible to establish Figure 2 The neural network model described. Figure 2 A schematic diagram of the structure of a neural network model provided in an embodiment of the present application is shown in FIG. Figure 2 As shown in the figure, the multi-task classifier is divided into 9 classifiers, which are used to identify different style attributes of clothing. The multi-task classifiers and their classification labels are as follows:

[0072]

[0073] In the embodiment of the present application, the label corresponding to each characteristic attribute can be identified, thereby determining the characteristic attribute.

[0074] Step S104: determining the user's clothing style based on the characteristic attributes.

[0075] In the embodiment of the present application, the clothing style can be the clothing style of the top, the clothing style of the bottom, or a mixed clothing style.

[0076] In the embodiment of the present application, a vector corresponding to each characteristic attribute can be determined; the vectors corresponding to each characteristic attribute are concatenated to obtain a style type label vector representing the user's clothing style.

[0077] In the embodiment of the present application, different characteristic attributes are represented by vectors. After the characteristic data is determined, the vectors of the characteristic attributes can be spliced ​​to obtain the clothing style. In other words, the clothing style can be represented by the spliced ​​vector.

[0078] Continuing with the above example, if it is the clothing style of a top or bottom, the nine vectors are concatenated into one to obtain the clothing style vector of the top or bottom. If it is a mixed clothing style, the clothing style vector of the top and the clothing style vector of the bottom are concatenated again.

[0079] Step S105: recommending clothing to the user based on the clothing style.

[0080] In an embodiment of the present application, the Euclidean distance between the style type label vector and the style type label of the clothing to be recommended can be calculated; the target recommended clothing can be determined based on the Euclidean distance and the distance threshold; and the target recommended clothing can be recommended to the user.

[0081] In the embodiment of the present application, the clothes to be recommended may be clothes on the Internet or clothes in the user's virtual wardrobe.

[0082] In the embodiment of the present application, if the Euclidean distance is less than the distance threshold, it is considered that the clothing is in line with the user's clothing style and can be recommended to the user.

[0083] In an embodiment of the present application, when making a recommendation, the recommended clothes can be displayed on a display screen.

[0084] The present application provides a recommendation method, which obtains the number of times a user's clothes are washed within a preset time period; determines target clothes from the clothes based on the number of washes; inputs an image of the target clothes into a pre-established neural network model to determine characteristic attributes of the target clothes; determines the user's clothing style based on the characteristic attributes; and recommends clothes to the user based on the clothing style. Since the number of times a user's clothes are washed is related to the user's dressing habits, the user's favorite clothing style can be determined more accurately, thereby making more accurate clothing recommendations to the user based on the clothing style.

[0085] Based on the above embodiments, the present application further provides a recommendation method. Figure 3 A flowchart of a recommended method provided in an embodiment of the present application is shown in FIG. Figure 3 As shown, the method includes:

[0086] Step S11, obtaining the user's favorite clothing style.

[0087] In an embodiment of the present application, a virtual wardrobe and a washing machine can be linked. The virtual wardrobe stores all the user's clothing photos. When the user washes clothes, he can select the clothes to be washed from the virtual wardrobe, and the washing machine will set specific washing parameters for the clothes to be washed.

[0088] Through the laundry process of "virtual wardrobe and washing machine linkage", the user's laundry information within a period of time (the same as the preset time length in the above embodiment) can be obtained, and the number of times the clothes washed during this period are washed can be counted. There are two types of statistics, one is to count the number of times the tops are washed, and the other is to count the number of times the lower body clothes (the same as the lower clothes in the above embodiment) are washed.

[0089] Sort the clothes in order to get the top three clothes with the most washes, sort the mixed clothes again according to the combined wash times and select the top three clothes with the largest wash times to get the target clothes.

[0090] Step S12: Establish a clothing style model (the same as the neural network model in the above embodiment).

[0091] In the embodiment of this application, a neural network model is established to identify the characteristic attributes of clothing. The deep learning backbone framework uses resnet101, and the multi-task classifier (combination of multiple classifiers) all uses softmax classifier. For example, the multi-task classifier is divided into 9 classifiers, which respectively identify different style attributes of clothing. The multi-task classifier and its classification labels are as follows:

[0092]

[0093] Collect sample data sets, train the model, and use the trained model to determine feature attributes.

[0094] Step S13, calculating the user's favorite clothing style (the same as the clothing style in the above embodiment).

[0095] In this embodiment, the clothing image in S11 is fed into the neural network model in S12 to obtain clothing style labels (similar to the characteristic attributes in the above embodiment). Clothing styles can be divided into three categories: tops, bottoms, and mixed styles. Each clothing style includes nine categories, each of which contains different subcategories. These subcategories are combined into vectors to obtain clothing styles.

[0096] Step S14: Search for clothing combinations that match the user's style.

[0097] Calculate the Euclidean distance between the clothing style vector and the clothing style vector of the clothing to be recommended, and then set a distance threshold. If the distance is less than the threshold, it is considered to be in line with the user's style and is recommended to the user.

[0098] In the embodiment of the present application, the clothes to be recommended may be clothes on the Internet or clothes in a virtual wardrobe.

[0099] A recommendation method provided in an embodiment of the present application obtains useful information about the user's favorite clothing style based on the user's laundry habits, and infers the clothing style based on the user's daily habits. The information obtained is more credible and the recommendation is more accurate.

[0100] Based on the foregoing embodiments, the embodiments of the present application provide a recommended device, wherein the modules included in the device and the units included in each module can be implemented by a processor in a computer device; of course, they can also be implemented by a specific logic circuit; in the implementation process, the processor can be a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP), or a field programmable gate array (FPGA), etc.

[0101] This embodiment of the present application provides a recommendation device, which includes:

[0102] An acquisition module is used to obtain the number of times the user's clothes have been washed within a preset time period;

[0103] a first determining module, configured to determine target clothing from the clothing based on the number of washes;

[0104] A neural network module, configured to input the image of the target clothing into a pre-established neural network model to determine characteristic attributes of the target clothing;

[0105] a second determining module, configured to determine the user's clothing style based on the characteristic attributes;

[0106] A recommendation module is used to recommend clothing to the user based on the clothing style.

[0107] In some embodiments, determining target laundry from the laundry based on the number of washes includes:

[0108] sorting the clothes based on the number of washing times to obtain a sorting result;

[0109] Target laundry is determined from the laundry based on the ranking result.

[0110] In some embodiments, the feature attributes include: one or more of: color feature, pattern feature, material feature, sleeve length feature, garment length feature, skirt length feature, trouser length feature, lapel feature and neckline feature.

[0111] In some embodiments, determining the user's clothing style based on the characteristic attributes includes:

[0112] Determine the vector corresponding to each feature attribute;

[0113] The vectors corresponding to each feature attribute are concatenated to obtain a style type label vector representing the user's clothing style.

[0114] In some embodiments, recommending clothing to the user based on the clothing style includes:

[0115] Calculating the Euclidean distance between the style type label vector and the style type label of the clothing to be recommended;

[0116] Determining target recommended clothing based on the Euclidean distance and the distance threshold;

[0117] The target recommended clothing is recommended to the user.

[0118] In some embodiments, obtaining the number of times the user's clothes are washed within a preset time period includes:

[0119] Get the washing information of the washing machine within the preset time;

[0120] The number of times the user's clothes are to be washed within a preset time period is determined based on the laundry information of the clothes.

[0121] In some embodiments, the recommendation device is further configured to:

[0122] Acquire a sample data set, wherein the sample data in the sample data set includes: images of clothing and characteristic attribute labels of the clothing;

[0123] Training is performed based on the sample data set to obtain a neural network model, wherein the neural network model takes the image of the clothing as input and the characteristic attribute label of the clothing as output.

[0124] An embodiment of the present application provides an electronic device; Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown in FIG. Figure 4 As shown, the electronic device 700 includes: a processor 701, at least one communication bus 702, a user interface 703, at least one external communication interface 704, and a memory 705. The communication bus 702 is configured to enable communication between these components. The user interface 703 may include a display screen, and the external communication interface 704 may include a standard wired interface and a wireless interface. The processor 701 is configured to execute the recommendation method program stored in the memory to implement the steps of the recommendation method provided in the above embodiment.

[0125] This application provides a recommended method, including:

[0126] Get the number of times the user's clothes have been washed within a preset time period;

[0127] determining target laundry from the laundry based on the number of washes;

[0128] Inputting the image of the target clothing into a pre-established neural network model to determine characteristic attributes of the target clothing;

[0129] determining the user's clothing style based on the characteristic attributes;

[0130] Recommend clothing to the user based on the clothing style.

[0131] In some embodiments, determining target laundry from the laundry based on the number of washes includes:

[0132] sorting the clothes based on the number of washing times to obtain a sorting result;

[0133] Target laundry is determined from the laundry based on the ranking result.

[0134] In some embodiments, the feature attributes include: one or more of: color feature, pattern feature, material feature, sleeve length feature, garment length feature, skirt length feature, trouser length feature, lapel feature and neckline feature.

[0135] In some embodiments, determining the user's clothing style based on the characteristic attributes includes:

[0136] Determine the vector corresponding to each feature attribute;

[0137] The vectors corresponding to each feature attribute are concatenated to obtain a style type label vector representing the user's clothing style.

[0138] In some embodiments, recommending clothing to the user based on the clothing style includes:

[0139] Calculating the Euclidean distance between the style type label vector and the style type label of the clothing to be recommended;

[0140] Determining target recommended clothing based on the Euclidean distance and the distance threshold;

[0141] The target recommended clothing is recommended to the user.

[0142] In some embodiments, obtaining the number of times the user's clothes are washed within a preset time period includes:

[0143] Get the washing information of the washing machine within the preset time;

[0144] The number of times the user's clothes are to be washed within a preset time period is determined based on the laundry information of the clothes.

[0145] In some embodiments, the method further comprises:

[0146] Acquire a sample data set, wherein the sample data in the sample data set includes: images of clothing and characteristic attribute labels of the clothing;

[0147] Training is performed based on the sample data set to obtain a neural network model, wherein the neural network model takes the image of the clothing as input and the characteristic attribute label of the clothing as output.

[0148] In the embodiment of the present application, if the above-mentioned recommended method is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM, Read Only Memory), a magnetic disk or an optical disk. In this way, the embodiment of the present application is not limited to any specific combination of hardware and software.

[0149] Accordingly, an embodiment of the present application provides a storage medium on which a computer program is stored, characterized in that when the computer program is executed by a processor, the steps of the recommended method provided in the above embodiment are implemented.

[0150] The description of the above electronic device and storage medium embodiments is similar to the description of the above method embodiments and has similar beneficial effects as the method embodiments. For technical details not disclosed in the computer device and storage medium embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0151] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned serial numbers of the embodiments of the present application are for description only and do not represent the advantages and disadvantages of the embodiments.

[0152] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0153] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0154] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0155] In addition, all functional units in the embodiments of the present application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the above-mentioned integrated units can be implemented in the form of hardware or in the form of hardware plus software functional units.

[0156] Those skilled in the art will understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROMs), magnetic disks, optical disks, and other media that can store program codes.

[0157] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a controller to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks or optical disks.

[0158] The above is merely an embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A recommendation method, characterized in that: include: Get the number of times the user's clothes have been washed within a preset time period; determining target laundry from the laundry based on the number of washes; Inputting the image of the target clothing into a pre-established neural network model to determine characteristic attributes of the target clothing; determining the user's clothing style based on the characteristic attributes; Recommend clothing to the user based on the clothing style.

2. The method according to claim 1, characterized in that The determining target laundry from the laundry based on the number of washes includes: sorting the clothes based on the number of washing times to obtain a sorting result; Target laundry is determined from the laundry based on the ranking result.

3. The method according to claim 1, characterized in that The feature attributes include: one or more of: color feature, pattern feature, material feature, sleeve length feature, garment length feature, skirt length feature, trouser length feature, lapel feature and neckline feature.

4. The method according to claim 3, characterized in that The determining of the user's clothing style based on the characteristic attributes includes: Determine the vector corresponding to each feature attribute; The vectors corresponding to each feature attribute are concatenated to obtain a style type label vector representing the user's clothing style.

5. The method according to claim 4, characterized in that The recommending clothing for the user based on the clothing style includes: Calculating the Euclidean distance between the style type label vector and the style type label of the clothing to be recommended; Determining target recommended clothing based on the Euclidean distance and the distance threshold; The target recommended clothing is recommended to the user.

6. The method according to claim 1, characterized in that The obtaining of the number of times the user's clothes are washed within a preset time period includes: Get the washing information of the washing machine within the preset time; The number of times the user's clothes are to be washed within a preset time period is determined based on the laundry information of the clothes.

7. The method according to claim 1, characterized in that The method further comprises: Acquire a sample data set, wherein the sample data in the sample data set includes: images of clothing and characteristic attribute labels of the clothing; Training is performed based on the sample data set to obtain a neural network model, wherein the neural network model takes the image of the clothing as input and the characteristic attribute label of the clothing as output.

8. A recommendation device, characterized in that: include: An acquisition module is used to obtain the number of times the user's clothes have been washed within a preset time period; a first determining module, configured to determine target clothing from the clothing based on the number of washes; A neural network module, configured to input the image of the target clothing into a pre-established neural network model to determine characteristic attributes of the target clothing; a second determining module, configured to determine the user's clothing style based on the characteristic attributes; A recommendation module is used to recommend clothing to the user based on the clothing style.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the recommendation method according to any one of claims 1 to 7 is executed.

10. A storage medium, characterized in that: The computer program stored in the storage medium can be executed by one or more processors and can be used to implement the recommendation method according to any one of claims 1 to 7.

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