Information recommendation method and device, equipment, storage medium and program product

Abstract

The embodiment of the invention provides an information recommendation method and device, equipment, a storage medium and a program product, and relates to the field of artificial intelligence. The method comprises the following steps: in response to demand information input by a user, obtaining N target clothing parts matched with the demand information from a clothing information library; performing pairwise combination processing on the N target garment parts to obtain N part combinations; determining a sales index, a price index and a collocation index of the component combination; and generating recommendation information of the user according to the sales volume index, the price index and the collocation index of each component combination. According to the method, the clothing information can be accurately recommended to the user.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence, and in particular to an information recommendation method, device, equipment, storage medium and program product. Background Art

[0002] Clothing has become a necessary daily necessity for people. In the process of providing clothing customization services, clothing information can be recommended to users, such as clothing pictures, styles, wearing occasions, etc., so that users can quickly determine customized clothing that meets their needs.

[0003] Furthermore, there is an urgent need for a method that can recommend accurate clothing information to users, thereby increasing the accuracy of clothing customization services. Summary of the invention

[0004] The embodiments of the present application provide an information recommendation method, apparatus, device, storage medium and program product to solve the technical problem of being unable to recommend accurate clothing information to users.

[0005] In a first aspect, an embodiment of the present application provides an information recommendation method, comprising:

[0006] In response to the demand information input by the user, N target clothing parts matching the demand information are obtained from the clothing information database; wherein the demand information represents the clothing customization demand of the user; the clothing information database includes clothing parts in a plurality of customized clothing; and N is an integer greater than or equal to 1;

[0007] Combining the N target clothing parts in pairs to obtain N part combinations;

[0008] Determine the sales index, price index and matching index of the component combination; wherein the sales index represents the sales situation of the current component combination; the price index represents the customized price situation of the current component combination; the matching index represents the clothing component matching effect of the current component combination;

[0009] The recommendation information for the user is generated according to the sales index, price index and matching index of each of the component combinations; wherein the recommendation information includes information of each target clothing component in at least one of the component combinations.

[0010] In a possible implementation manner, determining the sales index, price index, and matching index of the component combination includes:

[0011] Determining a sales index of the component combination according to the sales volume of each target clothing component in the component combination;

[0012] Determining a price index of the component combination according to the customized prices of each target clothing component in the component combination; wherein the customized price is the customized price of the customized clothing to which the target clothing component belongs;

[0013] The matching index of the component combination is determined according to the sales ranking of each target clothing component in the component combination; wherein the sales ranking represents the ranking of the target clothing component in the sales list including all clothing components; and each clothing component in the sales list is sorted from large to small according to sales.

[0014] In a possible implementation, determining the sales index of the component combination according to the sales volume of each target clothing component in the component combination includes:

[0015] The sales index of the component combination is determined based on the cumulative sales, minimum sales and maximum sales of the component combination; wherein the cumulative sales is the sum of the sales of all target clothing components in the component combination; the minimum sales is the sales of the target clothing component with the smallest sales in the component combination; and the maximum sales is the sales of the target clothing component with the largest sales in the component combination.

[0016] In a possible implementation, determining a price index of the component combination according to the customized price of each target garment component in the component combination includes:

[0017] The price index of the component combination is determined according to the cumulative price, minimum price and maximum price of the component combination; wherein the cumulative price is the sum of the customized prices of all target clothing components in the component combination; the minimum price is the customized price of the target clothing component with the smallest customized price in the component combination; and the maximum price is the customized price of the target clothing component with the largest customized price in the component combination.

[0018] In a possible implementation, determining the matching index of the component combination according to the sales ranking of each target clothing component in the component combination includes:

[0019] Determine the number of hot components of the component combination according to the sales ranking of each target clothing component in the component combination; wherein the number of hot components is the total number of hot clothing components in the component combination; the hot clothing component is a target clothing component whose sales ranking is less than or equal to a preset ranking;

[0020] According to the number of popular components of the component combination, a matching index of the component combination is determined.

[0021] In a possible implementation manner, determining the matching index of the component combination according to the number of popular components of the component combination includes:

[0022] If it is determined that the number of popular components of the component combination is greater than or equal to a preset number, then determining that the matching index of the component combination represents that the clothing component matching effect of the component combination is good;

[0023] If it is determined that the number of popular components of the component combination is less than the preset number, it is determined that the matching index of the component combination represents that the clothing component matching effect of the component combination is not good.

[0024] In a possible implementation, generating the user's recommendation information according to the sales index, price index, and matching index of each component combination includes:

[0025] Determine a recommended value of the component combination according to the sales index, price index and matching index of the component combination; wherein the recommended value represents the degree of matching between the component combination and the customization requirements of the user;

[0026] The recommendation information for the user is generated according to the recommendation value of each of the component combinations.

[0027] In a possible implementation manner, determining the recommended value of the component combination according to the sales index, price index, and matching index of the component combination includes:

[0028] Determine the praise index and search index of the component combination; wherein the praise index represents the user praise of each target clothing component in the component combination; the search index represents the user search status of each target clothing component in the component combination;

[0029] The recommendation value of the component combination is determined according to the sales index, price index, matching index, positive review index and search index of the component combination.

[0030] In a possible implementation, generating the recommendation information of the user according to the recommendation value of each of the component combinations includes:

[0031] A preset number of component combinations with high recommendation values ​​are determined from the component combinations to be the component combinations in the recommendation information.

[0032] In a possible implementation, obtaining N target clothing parts matching the demand information from a clothing information database includes:

[0033] Performing language recognition processing on the demand information to obtain key information corresponding to the demand information; wherein the key information represents clothing attributes of the customized clothing required by the user;

[0034] According to the key information, the N target clothing parts are obtained from the clothing information library; wherein the clothing parts have label information; the label information represents the clothing attributes of the custom clothing to which the clothing parts belong; the target clothing parts are clothing parts whose clothing attributes represented by the label information are consistent with the clothing attributes represented by the key information.

[0035] In a possible implementation manner, the key information includes season information, style information, gender information, and budget information;

[0036] The tag information includes a season tag, a style tag, a gender tag, and a price tag; the price tag represents the customized price of the customized clothing to which the clothing component belongs.

[0037] In a second aspect, an embodiment of the present application provides an information recommendation device, including:

[0038] An acquisition module, configured to acquire N target clothing components matching the demand information from a clothing information database in response to the demand information input by the user; wherein the demand information represents the clothing customization demand of the user; the clothing information database includes clothing components in a plurality of customized clothing; and N is an integer greater than or equal to 1;

[0039] A processing module, used for performing pairwise combination processing on the N target clothing parts to obtain N part combinations;

[0040] A determination module is used to determine the sales index, price index and matching index of the component combination; wherein the sales index represents the sales situation of the current component combination; the price index represents the customized price situation of the current component combination; and the matching index represents the clothing component matching effect of the current component combination;

[0041] The recommendation module is used to generate recommendation information for the user according to the sales index, price index and matching index of each component combination; wherein the recommendation information includes information of each target clothing component in at least one of the component combinations.

[0042] In a possible implementation, the determination module is specifically used to: determine the sales index of the component combination according to the sales volume of each target clothing component in the component combination; determine the price index of the component combination according to the customized price of each target clothing component in the component combination; wherein the customized price is the customized price of the customized clothing to which the target clothing component belongs; determine the matching index of the component combination according to the sales ranking of each target clothing component in the component combination; wherein the sales ranking represents the ranking of the target clothing component in the sales list including all clothing components; and the clothing components in the sales list are sorted from large to small according to sales volume.

[0043] In a possible implementation, the determination module is specifically used to determine the sales index of the component combination based on the cumulative sales, minimum sales and maximum sales of the component combination; wherein the cumulative sales is the sum of the sales of all target clothing components in the component combination; the minimum sales is the sales of the target clothing component with the smallest sales in the component combination; and the maximum sales is the sales of the target clothing component with the largest sales in the component combination.

[0044] In a possible implementation, the determination module is further specifically used to: determine the price index of the component combination based on the cumulative price, minimum price and maximum price of the component combination; wherein the cumulative price is the sum of the customized prices of all target clothing components in the component combination; the minimum price is the customized price of the target clothing component with the smallest customized price in the component combination; and the maximum price is the customized price of the target clothing component with the largest customized price in the component combination.

[0045] In a possible implementation, the determination module is further specifically used to: determine the number of popular components of the component combination according to the sales ranking of each target clothing component in the component combination; wherein the number of popular components is the total number of popular clothing components in the component combination; the popular clothing component is a target clothing component whose sales ranking is less than or equal to a preset ranking; and determine the matching index of the component combination according to the number of popular components of the component combination.

[0046] In a possible implementation, the determination module is further specifically used to: if it is determined that the number of popular components of the component combination is greater than or equal to a preset number, then determine that the matching index of the component combination represents that the clothing components of the component combination have a good matching effect; if it is determined that the number of popular components of the component combination is less than the preset number, then determine that the matching index of the component combination represents that the clothing components of the component combination have a poor matching effect.

[0047] In a possible implementation, the recommendation module is specifically used to: determine a recommendation value of the component combination based on a sales index, a price index, and a matching index of the component combination; wherein the recommendation value represents a degree of match between the component combination and a user's customization requirements; and generate recommendation information for the user based on the recommendation value of each component combination.

[0048] In a possible implementation, the recommendation module is specifically used to: determine the praise index and search index of the component combination; wherein the praise index represents the user praise situation of each target clothing component in the component combination; the search index represents the user search situation of each target clothing component in the component combination; determine the recommendation value of the component combination based on the sales index, price index, matching index, praise index and search index of the component combination.

[0049] In a possible implementation manner, the recommendation module is further specifically configured to: determine a preset number of component combinations with high recommendation values ​​from the component combinations, as the component combinations in the recommendation information.

[0050] In a possible implementation, the acquisition module is specifically used to: perform language recognition processing on the demand information to obtain key information corresponding to the demand information; wherein the key information represents the clothing attributes of the customized clothing required by the user; according to the key information, obtain the N target clothing parts from the clothing information library; wherein the clothing parts have label information; the label information represents the clothing attributes of the customized clothing to which the clothing parts belong; the target clothing parts are clothing parts whose clothing attributes represented by the label information are consistent with the clothing attributes represented by the key information.

[0051] In a possible implementation, the key information includes season information, style information, gender information and budget information; the tag information includes season tags, style tags, gender tags and price tags; the price tags represent the custom price of the customized clothing to which the clothing component belongs.

[0052] In a third aspect, an embodiment of the present application provides an electronic device, including: a memory, a processor;

[0053] The memory stores computer-executable instructions;

[0054] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0055] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementations of the first aspect.

[0056] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect.

[0057] The information recommendation method, apparatus, device, storage medium and program product provided in the embodiments of the present application obtain multiple component combinations by combining each clothing component that meets the user's needs in pairs, and determine the optimal one or more component combinations based on the sales situation, customized price situation and clothing component matching effect of each component combination, and recommend these component combinations to the user; thereby, accurate clothing information can be recommended to the user. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0059] Figure 1 A schematic diagram of an application scenario provided for this application;

[0060] Figure 2 A flowchart of an information recommendation method provided in an embodiment of the present application;

[0061] Figure 3 A flowchart of another information recommendation method provided in an embodiment of the present application;

[0062] Figure 4 A schematic diagram of an example of a shirt component of a shirt provided in an embodiment of the present application;

[0063] Figure 5 A schematic diagram of the structure of an information recommendation device provided in an embodiment of the present application;

[0064] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0065] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0066] Here, exemplary embodiments will be described in detail, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Instead, they are only examples of devices and methods consistent with some aspects of the present application.

[0067] First, the terms involved in this application are explained:

[0068] Agent: It is an important concept in artificial intelligence and computer science, referring to an entity that can perceive the environment and take actions to achieve a certain goal. An agent can be a software program or a robot.

[0069] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0070] In addition, this application involves big data analysis of user information (including but not limited to personal biometrics, identity data, consumption data, asset data, electronic terminal operation data, etc.), and the use of artificial intelligence technology for automated decision-making, and a technical solution for making decisions that have a significant impact on personal rights and interests based on the results of automated decision-making. The application provides users with corresponding operation entrances for them to choose to agree or reject the results of automated decision-making; if the user chooses to reject, the expert decision-making process will be entered.

[0071] It should be noted that the information recommendation method, device, equipment, storage medium and program product of the present application can be used in the field of artificial intelligence, and can also be used in any field other than artificial intelligence. The application field of the information recommendation method, device, equipment, storage medium and program product of the present application is not limited.

[0072] Figure 1 A schematic diagram of an application scenario provided by this application. Figure 1 As shown, the specific application scenario of the present application includes an electronic device 101 and a user 102. The user 102 can input customization requirements through the electronic device 101, and the electronic device 101 recommends clothing information to the user 102 according to the customization requirements of the user 102.

[0073] In one example, the user's style is determined based on the user's historical browsing data and historical order data, and information about clothing that matches the user's style is displayed to the user.

[0074] However, in the above method, the information about the user's style is too single, and it is impossible to accurately understand the user's needs, and thus it is impossible to recommend accurate clothing information to the user.

[0075] In view of the above technical problems, the present application proposes the following technical concept: by combining each clothing component that meets the user's needs in pairs, a plurality of component combinations are obtained, and according to the sales situation, customized price situation and clothing component matching effect of each component combination, the optimal one or more component combinations are determined, and these component combinations are recommended to the user. In this way, the problem that the prior art cannot recommend accurate clothing information to the user can be solved.

[0076] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0077] Figure 2 A flowchart of an information recommendation method provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the method includes:

[0078] 201. In response to demand information input by a user, N target clothing components matching the demand information are obtained from a clothing information database; wherein the demand information represents the user's clothing customization demand; the clothing information database includes clothing components in a plurality of customized clothing; and N is an integer greater than or equal to 1.

[0079] Exemplarily, the execution subject of this embodiment may be an electronic device. For the convenience of subsequent description, the electronic device is hereinafter referred to as the device. The device may be a physical device or a virtual device for executing the information recommendation method. The user inputs the user's clothing customization needs through the device, and the device responds to the user's input operation and calls a clothing information library, which includes clothing parts in multiple customized clothing. According to the user's clothing customization needs, N clothing parts that match the user's clothing customization needs are found from the clothing information library, that is, target clothing parts, where N is an integer greater than or equal to 1; for example, for inputting the text information "I want to customize a set of shirts", the device finds pictures of all shirt parts that constitute the shirt from the clothing information library.

[0080] 202. Perform pairwise combination processing on the N target clothing parts to obtain N part combinations.

[0081] Exemplarily, the device performs pairwise combination processing on the N acquired target clothing parts to obtain N part combinations; for example, three pictures of shirt parts are acquired, namely shirt part 1, shirt part 2, and shirt part 3, and the pictures of the three shirt parts are combined in pairs to obtain pictures corresponding to the three part combinations of shirt part 1-2, shirt part 2-3, and shirt part 1-3.

[0082] 203. Determine the sales index, price index and matching index of the component combination; wherein the sales index represents the sales situation of the current component combination; the price index represents the customized price situation of the current component combination; and the matching index represents the clothing component matching effect of the current component combination.

[0083] Exemplarily, for each component combination, the device can determine the current sales situation of the component combination, that is, the sales volume index, such as whether the sales situation is good, medium, or bad, through the sales data of the customized clothing corresponding to the component combination in the past period of time; at the same time, the device can determine the current customization price situation of the component combination, that is, the price index, such as whether the customization price is expensive, medium, or cheap, through the customization price data of the customized clothing corresponding to the component combination in the past period of time; and the device can determine the clothing component matching effect of the current component combination, that is, the matching index, such as whether the clothing component matching effect is good, average, or bad, through the sales data of the customized clothing corresponding to the component combination in the past period of time.

[0084] 204. Generate user recommendation information based on the sales index, price index, and matching index of each component combination; wherein the recommendation information includes information of each target clothing component in at least one component combination.

[0085] Exemplarily, the device can analyze the sales index, price index and matching index of each component combination, determine one or more component combinations that can be recommended from all component combinations, and then generate recommendation information to recommend to the user; for example, the device can first determine the component combinations with good sales, medium prices and good matching effects, and display pictures of each target clothing component in these component combinations.

[0086] In this embodiment, an information recommendation method is provided, by combining clothing components that meet user needs in pairs to obtain multiple component combinations, and determining the best one or more component combinations based on the sales situation, customized price situation and clothing component matching effect of each component combination, and recommending these component combinations to the user; thereby, accurate clothing information can be recommended to the user.

[0087] Figure 3 A flowchart of another information recommendation method provided in an embodiment of the present application is shown as follows: Figure 3 As shown, the method includes:

[0088] 301. In response to demand information input by a user, perform language recognition processing on the demand information to obtain key information corresponding to the demand information; wherein the key information represents clothing attributes of the customized clothing required by the user.

[0089] In one example, the key information includes season information, style information, gender information, and budget information; the tag information includes a season tag, a style tag, a gender tag, and a price tag; and the price tag represents the custom price of the customized clothing to which the clothing component belongs.

[0090] Exemplarily, a user inputs the user's clothing customization needs through the device, and the device responds to the user's demand information and performs language recognition processing on the demand information based on language processing technology to obtain the clothing attributes of the customized clothing required by the user, that is, key information. The key information may include season information, style information, gender information and budget information; the season information represents which season the customized clothing is suitable for, the style information represents the style of the customized clothing, the gender information represents whether the customized clothing is suitable for men or women, and the budget information represents the cost of customized clothing that the user can provide.

[0091] For example, a user inputs a demand text: I want to customize a set of winter business-style men's shirts, and my budget is between 100-500 yuan. You can use prompt words to guide the user to input the demand. The prompt words are as follows: I want to customize a set of {season, such as winter} {style, such as business style} {gender, such as men's} shirts, and my budget is {budget price, such as between 100 yuan and 500 yuan}; use the intelligent agent + large language model technology to identify the user's intention, and identify key information into four items: season, style, gender, and budget. The intelligent agent adds prompt word template information based on the user's input demand information, such as As shown below: Known information: Styles include: business style, casual style, business casual, classic style, mature and stable style, lady style, neutral style, and others (expandable later). Answer format: - Season: ** - Style: ** - Gender: ** - Budget: **. Answer requirements: - Please output the four items of season, style, gender, and budget according to the user's shirt demand description. - Avoid mentioning that you have gained knowledge from known information. - Please ensure that the output is consistent with the description in the known information. - Please ensure that only the four information of season, style, gender, and budget are output. - Please use the same language as the question to answer. Shirt customization requirements: {user input information}.

[0092] 302. According to the key information, N target clothing parts are obtained from the clothing information database; wherein the clothing parts have label information; the label information represents the clothing attributes of the customized clothing to which the clothing parts belong; and the target clothing parts are clothing parts whose clothing attributes represented by the label information are consistent with the clothing attributes represented by the key information.

[0093] For example, Figure 4 A schematic diagram of an example of a shirt component of a shirt provided in an embodiment of the present application, such as Figure 4As shown, the device calls a preset clothing information library, and each clothing component in the clothing information library has label information to characterize the clothing attributes of the custom clothing to which the clothing component belongs, wherein the label information of each clothing component includes a season label, a style label, a gender label, and a price label; each price label characterizes the custom price of the customized clothing to which the clothing component belongs; then the device can retrieve all the clothing components that meet the conditions according to the preset style label, season label, gender label, and price label of the shirt component by configuring the intelligent agent process, wherein multiple clothing components can be retrieved for the same component. Furthermore, semantic recognition can be performed through a large model to help users quickly select clothing components of custom clothing that meet their needs, thereby improving the accuracy and efficiency of information recommendation.

[0094] 303. Perform pairwise combination processing on the N target clothing parts to obtain N part combinations.

[0095] Exemplarily, this step may refer to step 202 and will not be described in detail here.

[0096] 304. Determine the sales index of the component combination according to the sales volume of each target clothing component in the component combination.

[0097] Exemplarily, for each component combination, the device may determine the current sales index of the component combination through the sales data of each target clothing component in the component combination over a period of time in the past, such as the sum of the sales data of each target clothing component.

[0098] In one example, step 304 includes: determining the sales index of the component combination based on the cumulative sales, minimum sales and maximum sales of the component combination; wherein the cumulative sales is the sum of the sales of all target clothing components in the component combination; the minimum sales is the sales of the target clothing component with the smallest sales in the component combination; and the maximum sales is the sales of the target clothing component with the largest sales in the component combination.

[0099] Exemplarily, for each component combination, the device determines the sum of the sales of all target clothing components in each component combination based on the sales of each target clothing component in each component combination, i.e., the cumulative sales, and determines the sales of the target clothing component with the smallest sales in each component combination, i.e., the minimum sales, and determines the sales of the target clothing component with the largest sales in each component combination, i.e., the maximum sales. The device calculates and processes the cumulative sales, minimum sales, and maximum sales of each component combination based on a preset algorithm to obtain the sales index of each component combination; for example, the sales index X can be calculated by the formula X=(X0-X1) / (X2-X1); wherein X0 is the cumulative sales; X1 is the minimum sales; and X2 is the maximum sales.

[0100] 305. Determine a price index of the component combination according to the customized prices of each target clothing component in the component combination; wherein the customized price is the customized price of the customized clothing to which the target clothing component belongs.

[0101] Exemplarily, for each component combination, the device can determine the current price index of the component combination through the customized price data of the corresponding customized clothing of each target clothing component in the component combination over the past period of time, such as the sum of the customized price data of each target clothing component.

[0102] In one example, step 305 includes: determining a price index of the component combination based on the cumulative price, minimum price and maximum price of the component combination; wherein the cumulative price is the sum of the customized prices of all target clothing components in the component combination; the minimum price is the customized price of the target clothing component with the smallest customized price in the component combination; and the maximum price is the customized price of the target clothing component with the largest customized price in the component combination.

[0103] Exemplarily, for each component combination, the device determines the sum of the customized prices of all target clothing components in each component combination, i.e., the cumulative price, based on the customized prices of each target clothing component in each component combination, and determines the customized price of the target clothing component with the smallest sales volume in each component combination, i.e., the minimum price, and determines the customized price of the target clothing component with the largest sales volume in each component combination, i.e., the maximum price. The device calculates and processes the cumulative price, minimum price, and maximum price of each component combination based on a preset algorithm to obtain a price index for each component combination; for example, the price index J can be calculated by the formula J=(J0-J1) / (J2-J1); wherein J0 is the cumulative price; J1 is the minimum price; and J2 is the maximum price.

[0104] 306. Determine a matching index of the component combination according to the sales ranking of each target clothing component in the component combination; wherein the sales ranking represents the ranking of the target clothing component in the sales list including all clothing components; and the clothing components in the sales list are sorted from large to small according to sales.

[0105] For example, for each clothing component in the clothing information database, each clothing component can be sorted from large to small according to sales volume to obtain a sales list. According to the sales volume of each target clothing component, the sales ranking of each target clothing component can be determined. For each component combination, the device can determine the matching index of the current component combination through the sales ranking of each target clothing component in the component combination, such as the sum of the sales rankings of each target clothing component. Furthermore, by combining customized sales volume, combining user customization needs with hot-selling styles for user recommendations, the user can be accurately recommended suitable clothing information without the need for the user to have professional knowledge background in clothing, helping the user to make customized style recommendations, and improving the user experience.

[0106] In one example, step 306 includes the following steps:

[0107] The first step of step 306 is to determine the number of popular components of the component combination according to the sales ranking of each target clothing component in the component combination; wherein the number of popular components is the total number of popular clothing components in the component combination; and the popular clothing component is a target clothing component whose sales ranking is less than or equal to a preset ranking.

[0108] The second step of step 306 is to determine the matching index of the component combination according to the number of popular components in the component combination.

[0109] Exemplarily, for each component combination, according to the sales ranking of each target clothing component in each component combination, the target clothing component with a sales ranking less than or equal to a preset ranking is determined as a hot clothing component, and the total number of hot clothing components in each component combination, i.e., the number of hot components, is calculated, and based on a preset algorithm, the number of hot components of each component combination is calculated to obtain the matching index of each component combination.

[0110] In one example, the second step of step 306 includes the following steps:

[0111] In the first step, if it is determined that the number of popular components of the component combination is greater than or equal to a preset number, then it is determined that the matching index of the component combination represents that the clothing component matching effect of the component combination is good.

[0112] In the second step, if the number of hot components of the component combination is determined to be less than the preset number, then the matching index of the component combination is determined to represent that the matching effect of the clothing components of the component combination is not good.

[0113] Exemplarily, the device determines the number of popular components of each component combination. If it is determined that the number of popular components of the component combination is greater than or equal to a preset number, then the clothing component matching effect of the component combination is determined to be good; if it is determined that the number of popular components of the component combination is less than the preset number, then the clothing component matching effect of the component combination is determined to be poor; for example, the combination of each component with other components in the top ten customized shirts ranked by sales volume is the best matching solution. If the matching of the current component combination is the best matching solution, the matching index is 1; if the matching of the current component combination is not the best matching solution, the matching index is 0.

[0114] 307. Determine a recommended value of the component combination according to the sales index, price index, and matching index of the component combination; wherein the recommended value represents the degree of matching between the component combination and the customization requirements of the user.

[0115] Exemplarily, the device calculates and processes the sales index, price index and matching index of each component combination obtained based on a preset recommendation algorithm to obtain a recommended value for each component combination; wherein the recommended value represents the degree of match between the component combination and the customized requirements of the user, and the higher the recommended value, the higher the degree of match between the component combination and the customized requirements of the user; for example, the recommended value T of each component combination can be obtained by calculation using the formula T=∑(α*sales index+β*price index+ε*matching index), wherein α, β and ε are weight parameters of the sales index, price index and matching index respectively.

[0116] In one example, step 307 includes the following steps:

[0117] The first step of step 307 is to determine the praise index and search index of the component combination; wherein the praise index represents the user praise of each target clothing component in the component combination; and the search index represents the user search of each target clothing component in the component combination.

[0118] The second step of step 307 is to determine the recommended value of the component combination according to the sales index, price index, matching index, positive review index and search index of the component combination.

[0119] Exemplarily, after obtaining the sales index, price index, and matching index of each component combination, in order to further improve the accuracy of the recommended value of the obtained component combination, the device can also first determine the user praise of each target clothing component in the component combination, that is, the praise index, for example, determine the praise index according to the number of praises, and at the same time, determine the user search situation of each target clothing component in the component combination, that is, the search index, for example, determine the search index according to the number of clicks and searches. Then, based on a preset recommendation algorithm such as a weighted sum algorithm, the device calculates and processes the sales index, price index, matching index, praise index, and search index of each component combination to obtain the recommended value of each component combination.

[0120] 308. Generate user recommendation information based on the recommendation values ​​of each component combination.

[0121] Exemplarily, the device organizes the information of each component combination and the corresponding recommended values ​​into recommendation information based on the recommended value of each component combination, and recommends it to the user, who then selects customized clothing components based on each recommended value in the recommended information.

[0122] In one example, step 308 includes: determining a preset number of component combinations with high recommendation values ​​from various component combinations as the component combinations in the recommendation information.

[0123] Exemplarily, the device arranges the component combinations from high to low according to the recommended value of each component combination, and determines a preset number of component combinations that are ranked high as the component combinations in the recommended information.

[0124] In this embodiment, on the basis of the above embodiment, each clothing component that meets the user's needs is combined in pairs, and the best component combinations are recommended to the user based on the sales index, price index and matching index of each component combination. Not only can the information of suitable clothing be recommended to the user, but also the user can be helped to make customized style suggestions without the need for the user to have professional knowledge background in clothing, thereby improving the user experience.

[0125] Figure 5 A schematic diagram of the structure of an information recommendation device provided in an embodiment of the present application is shown in FIG. Figure 5 As shown, the device comprises:

[0126] The acquisition module 401 is used to obtain N target clothing components matching the demand information from the clothing information database in response to the demand information input by the user; wherein the demand information represents the clothing customization demand of the user; the clothing information database includes clothing components in a plurality of customized clothing; and N is an integer greater than or equal to 1;

[0127] A processing module 402 is used to perform pairwise combination processing on the N target clothing parts to obtain N part combinations;

[0128] The determination module 403 is used to determine the sales index, price index and matching index of the component combination; wherein the sales index represents the sales situation of the current component combination; the price index represents the customized price situation of the current component combination; and the matching index represents the clothing component matching effect of the current component combination;

[0129] The recommendation module 404 is used to generate recommendation information for the user according to the sales index, price index and matching index of each component combination; wherein the recommendation information includes information of each target clothing component in at least one component combination.

[0130] In a possible implementation, the determination module 403 is specifically used to: determine the sales index of the component combination according to the sales volume of each target clothing component in the component combination; determine the price index of the component combination according to the customized price of each target clothing component in the component combination; wherein the customized price is the customized price of the customized clothing to which the target clothing component belongs; determine the matching index of the component combination according to the sales ranking of each target clothing component in the component combination; wherein the sales ranking represents the ranking of the target clothing component in the sales list including all clothing components; and the clothing components in the sales list are sorted from large to small according to sales volume.

[0131] In a possible implementation, the determination module 403 is specifically used to: determine the sales index of the component combination based on the cumulative sales, minimum sales and maximum sales of the component combination; wherein the cumulative sales is the sum of the sales of all target clothing components in the component combination; the minimum sales is the sales of the target clothing component with the smallest sales in the component combination; and the maximum sales is the sales of the target clothing component with the largest sales in the component combination.

[0132] In a possible implementation, the determination module 403 is further specifically used to: determine a price index of the component combination based on the cumulative price, minimum price and maximum price of the component combination; wherein the cumulative price is the sum of the customized prices of all target clothing components in the component combination; the minimum price is the customized price of the target clothing component with the smallest customized price in the component combination; and the maximum price is the customized price of the target clothing component with the largest customized price in the component combination.

[0133] In a possible implementation, the determination module 403 is further specifically used to: determine the number of popular components of the component combination according to the sales ranking of each target clothing component in the component combination; wherein the number of popular components is the total number of popular clothing components in the component combination; the popular clothing component is the target clothing component whose sales ranking is less than or equal to the preset ranking; and determine the matching index of the component combination according to the number of popular components of the component combination.

[0134] In a possible implementation, the determination module 403 is further specifically used to: if it is determined that the number of popular components of the component combination is greater than or equal to a preset number, then the matching index of the component combination is determined to represent that the clothing component matching effect of the component combination is good; if it is determined that the number of popular components of the component combination is less than the preset number, then the matching index of the component combination is determined to represent that the clothing component matching effect of the component combination is not good.

[0135] In a possible implementation, the recommendation module 404 is specifically used to: determine a recommendation value of a component combination based on a sales index, a price index, and a matching index of the component combination; wherein the recommendation value represents the degree of match between the component combination and the user's customization requirements; and generate user recommendation information based on the recommendation value of each component combination.

[0136] In a possible implementation, the recommendation module 404 is specifically used to: determine the praise index and search index of the component combination; wherein the praise index represents the user praise of each target clothing component in the component combination; the search index represents the user search status of each target clothing component in the component combination; and determine the recommendation value of the component combination based on the sales index, price index, matching index, praise index and search index of the component combination.

[0137] In a possible implementation manner, the recommendation module 404 is further specifically configured to: determine a preset number of component combinations with high recommendation values ​​from various component combinations as the component combinations in the recommendation information.

[0138] In a possible implementation, the acquisition module 401 is specifically used to: perform language recognition processing on the demand information to obtain key information corresponding to the demand information; wherein the key information represents the clothing attributes of the customized clothing required by the user; according to the key information, obtain N target clothing parts from the clothing information library; wherein the clothing parts have label information; the label information represents the clothing attributes of the customized clothing to which the clothing parts belong; the target clothing parts are clothing parts whose clothing attributes represented by the label information are consistent with the clothing attributes represented by the key information.

[0139] In a possible implementation, the key information includes season information, style information, gender information and budget information; the tag information includes season tags, style tags, gender tags and price tags; the price tag represents the custom price of the customized clothing to which the clothing component belongs.

[0140] The device of this embodiment can execute the technical solution in the above method. Its specific implementation process and technical principles are the same and will not be repeated here.

[0141] It should be noted that it should be understood that the division of the various modules of the above device is only a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated. And these modules can all be implemented in the form of software called by processing elements; they can also be all implemented in the form of hardware; some modules can also be implemented in the form of software called by processing elements, and some modules can be implemented in the form of hardware. Each module can be a separately established processing element, or it can be integrated in a chip of the above device. In addition, it can also be stored in the memory of the above device in the form of program code, and called and executed by a processing element of the above device. The functions of each module. In addition, all or part of these modules can be integrated together, or they can be implemented independently. The processing element here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each module above can be completed by an integrated logic circuit of hardware in the processor element or instructions in the form of software.

[0142] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown in FIG. Figure 6 As shown, the electronic device includes: a memory 501, a processor 502; the memory 501; and a memory for storing instructions executable by the processor 502.

[0143] The processor 502 is configured to execute the method provided in the above embodiment.

[0144] The electronic device further includes a receiver 503 and a transmitter 504. The receiver 503 is used to receive instructions and data sent by other devices, and the transmitter 504 is used to send instructions and data to external devices.

[0145] The specific implementation process of the processor can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0146] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0147] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.

[0148] An embodiment of the present application also provides a chip for executing instructions, which is used to execute the technical solution of the processing method in the above embodiment.

[0149] The embodiment of the present application also provides a computer-readable storage medium, in which a computer-executable instruction is stored. When the computer-executable instruction is executed on a computer, the computer executes the technical solution of the processing method of the above embodiment.

[0150] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0151] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0152] An embodiment of the present application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When at least one processor executes the computer program, the technical solution of the processing method in the above embodiment can be implemented.

[0153] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0154] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0155] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0156] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0157] Those skilled in the art will appreciate that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed.

[0158] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. An information recommendation method, characterized in that: include: In response to the demand information input by the user, N target clothing parts matching the demand information are obtained from the clothing information database; wherein the demand information represents the clothing customization demand of the user; the clothing information database includes clothing parts in a plurality of customized clothing; and N is an integer greater than or equal to 1; Combining the N target clothing parts in pairs to obtain N part combinations; Determine the sales index, price index and matching index of the component combination; wherein the sales index represents the sales situation of the current component combination; the price index represents the customized price situation of the current component combination; the matching index represents the clothing component matching effect of the current component combination; The recommendation information for the user is generated according to the sales index, price index and matching index of each of the component combinations; wherein the recommendation information includes information of each target clothing component in at least one of the component combinations.

2. The method according to claim 1, characterized in that Determine the sales volume index, price index and matching index of the component combination, including: Determining a sales index of the component combination according to the sales volume of each target clothing component in the component combination; Determining a price index of the component combination according to the customized prices of each target clothing component in the component combination; wherein the customized price is the customized price of the customized clothing to which the target clothing component belongs; The matching index of the component combination is determined according to the sales ranking of each target clothing component in the component combination; wherein the sales ranking represents the ranking of the target clothing component in the sales list including all clothing components; and each clothing component in the sales list is sorted from large to small according to sales.

3. The method according to claim 2, characterized in that According to the sales volume of each target clothing component in the component combination, a sales volume index of the component combination is determined, including: The sales index of the component combination is determined based on the cumulative sales, minimum sales and maximum sales of the component combination; wherein the cumulative sales is the sum of the sales of all target clothing components in the component combination; the minimum sales is the sales of the target clothing component with the smallest sales in the component combination; and the maximum sales is the sales of the target clothing component with the largest sales in the component combination.

4. The method according to claim 2, characterized in that: According to the customized prices of each target garment component in the component combination, a price index of the component combination is determined, including: The price index of the component combination is determined according to the cumulative price, minimum price and maximum price of the component combination; wherein the cumulative price is the sum of the customized prices of all target clothing components in the component combination; the minimum price is the customized price of the target clothing component with the smallest customized price in the component combination; and the maximum price is the customized price of the target clothing component with the largest customized price in the component combination.

5. The method according to claim 2, characterized in that: According to the sales ranking of each target clothing component in the component combination, the matching index of the component combination is determined, including: Determine the number of hot components of the component combination according to the sales ranking of each target clothing component in the component combination; wherein the number of hot components is the total number of hot clothing components in the component combination; the hot clothing component is a target clothing component whose sales ranking is less than or equal to a preset ranking; According to the number of popular components of the component combination, a matching index of the component combination is determined.

6. The method according to claim 5, characterized in that According to the number of popular components of the component combination, a matching index of the component combination is determined, including: If it is determined that the number of popular components of the component combination is greater than or equal to a preset number, then determining that the matching index of the component combination represents that the clothing component matching effect of the component combination is good; If it is determined that the number of popular components of the component combination is less than the preset number, it is determined that the matching index of the component combination represents that the clothing component matching effect of the component combination is not good.

7. The method according to claim 1, characterized in that Generating the user's recommendation information according to the sales index, price index and matching index of each component combination, including: Determine a recommended value of the component combination according to the sales index, price index and matching index of the component combination; wherein the recommended value represents the degree of matching between the component combination and the customization requirements of the user; The recommendation information for the user is generated according to the recommendation value of each of the component combinations.

8. The method according to claim 7, characterized in that Determining the recommended value of the component combination according to the sales index, price index and matching index of the component combination includes: Determine the praise index and search index of the component combination; wherein the praise index represents the user praise of each target clothing component in the component combination; the search index represents the user search status of each target clothing component in the component combination; The recommendation value of the component combination is determined according to the sales index, price index, matching index, positive review index and search index of the component combination.

9. The method according to claim 7, characterized in that: Generating recommendation information for the user according to the recommendation value of each component combination, including: A preset number of component combinations with high recommendation values ​​are determined from the component combinations to be the component combinations in the recommendation information.

10. The method according to any one of claims 1 to 9, characterized in that Obtaining N target clothing parts matching the demand information from a clothing information database, including: Performing language recognition processing on the demand information to obtain key information corresponding to the demand information; wherein the key information represents clothing attributes of the customized clothing required by the user; According to the key information, the N target clothing parts are obtained from the clothing information library; wherein the clothing parts have label information; the label information represents the clothing attributes of the custom clothing to which the clothing parts belong; the target clothing parts are clothing parts whose clothing attributes represented by the label information are consistent with the clothing attributes represented by the key information.

11. The method according to claim 10, characterized in that The key information includes season information, style information, gender information and budget information; The tag information includes a season tag, a style tag, a gender tag, and a price tag; the price tag represents the customized price of the customized clothing to which the clothing component belongs.

12. An information recommendation device, characterized in that: include: An acquisition module, configured to acquire N target clothing components matching the demand information from a clothing information database in response to the demand information input by the user; wherein the demand information represents the clothing customization demand of the user; the clothing information database includes clothing components in a plurality of customized clothing; and N is an integer greater than or equal to 1; A processing module, used for performing pairwise combination processing on the N target clothing parts to obtain N part combinations; A determination module is used to determine the sales index, price index and matching index of the component combination; wherein the sales index represents the sales situation of the current component combination; the price index represents the customized price situation of the current component combination; and the matching index represents the clothing component matching effect of the current component combination; The recommendation module is used to generate recommendation information for the user according to the sales index, price index and matching index of each component combination; wherein the recommendation information includes information of each target clothing component in at least one of the component combinations.

13. The device according to claim 12, characterized in that The determination module is specifically used for: Determining a sales index of the component combination according to the sales volume of each target clothing component in the component combination; Determining a price index of the component combination according to the customized prices of each target clothing component in the component combination; wherein the customized price is the customized price of the customized clothing to which the target clothing component belongs; The matching index of the component combination is determined according to the sales ranking of each target clothing component in the component combination; wherein the sales ranking represents the ranking of the target clothing component in the sales list including all clothing components; and each clothing component in the sales list is sorted from large to small according to sales.

14. The device according to claim 13, characterized in that The determination module is specifically used for: The sales index of the component combination is determined based on the cumulative sales, minimum sales and maximum sales of the component combination; wherein the cumulative sales is the sum of the sales of all target clothing components in the component combination; the minimum sales is the sales of the target clothing component with the smallest sales in the component combination; and the maximum sales is the sales of the target clothing component with the largest sales in the component combination.

15. The device according to claim 13, characterized in that The determining module is further specifically used for: The price index of the component combination is determined according to the cumulative price, minimum price and maximum price of the component combination; wherein the cumulative price is the sum of the customized prices of all target clothing components in the component combination; the minimum price is the customized price of the target clothing component with the smallest customized price in the component combination; and the maximum price is the customized price of the target clothing component with the largest customized price in the component combination.

16. The device according to claim 13, characterized in that The determining module is further specifically used for: Determine the number of hot components of the component combination according to the sales ranking of each target clothing component in the component combination; wherein the number of hot components is the total number of hot clothing components in the component combination; the hot clothing component is a target clothing component whose sales ranking is less than or equal to a preset ranking; According to the number of popular components of the component combination, a matching index of the component combination is determined.

17. The device according to claim 16, characterized in that The determining module is further specifically used for: If it is determined that the number of popular components of the component combination is greater than or equal to a preset number, then determining that the matching index of the component combination represents that the clothing component matching effect of the component combination is good; If it is determined that the number of popular components of the component combination is less than the preset number, it is determined that the matching index of the component combination represents that the clothing component matching effect of the component combination is not good.

18. The device according to claim 12, characterized in that The recommendation module is specifically used for: The recommended value of the component combination is determined according to the sales index, price index and matching index of the component combination; wherein the recommended value represents the degree of matching between the component combination and the customization requirements of the user; and the recommendation information of the user is generated according to the recommended value of each component combination.

19. The device according to claim 18, characterized in that The recommendation module is specifically used for: Determine the praise index and search index of the component combination; wherein the praise index represents the user praise of each target clothing component in the component combination; the search index represents the user search status of each target clothing component in the component combination; The recommendation value of the component combination is determined according to the sales index, price index, matching index, positive review index and search index of the component combination.

20. The device according to claim 18, characterized in that The recommendation module is also specifically used for: A preset number of component combinations with high recommendation values ​​are determined from the component combinations to be the component combinations in the recommendation information.

21. The device according to any one of claims 12 to 20, characterized in that The acquisition module is specifically used for: Performing language recognition processing on the demand information to obtain key information corresponding to the demand information; wherein the key information represents clothing attributes of the customized clothing required by the user; According to the key information, the N target clothing parts are obtained from the clothing information library; wherein the clothing parts have label information; the label information represents the clothing attributes of the custom clothing to which the clothing parts belong; the target clothing parts are clothing parts whose clothing attributes represented by the label information are consistent with the clothing attributes represented by the key information.

22. The device according to claim 21, characterized in that The key information includes season information, style information, gender information and budget information; The tag information includes a season tag, a style tag, a gender tag, and a price tag; the price tag represents the customized price of the customized clothing to which the clothing component belongs.

23. An electronic device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 11.

24. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 11 when executed by a processor.

25. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method according to any one of claims 1 to 11 is implemented.

Images