CN115098778A

CN115098778A - Recommendation method and device for item model data and storage medium

Info

Publication number: CN115098778A
Application number: CN202210775654.6A
Authority: CN
Inventors: 郝科伟; 覃其荧
Original assignee: Meiping Meiwu Shanghai Technology Co ltd
Current assignee: Meiping Meiwu Shanghai Technology Co ltd
Priority date: 2022-07-01
Filing date: 2022-07-01
Publication date: 2022-09-23

Abstract

The application provides a recommendation method, equipment and storage medium of article model data, wherein the method comprises the following steps: acquiring a space category of a target space based on the target space clicked by a user; determining a first article model type missing in a target space according to the space type and current space collocation data of the target space; and pushing target object model data corresponding to the first object model category to the user so that the user can quickly arrange and design the missing object models, and therefore user design experience and efficiency are improved.

Description

Recommendation method and device for article model data and storage medium

Technical Field

The present application relates to the field of information processing technologies, and in particular, to a method, an apparatus, and a storage medium for recommending article model data.

Background

With the continuous deepening of network sales services, the field related to internet sales is not limited to the traditional fields of daily clothing and the like, but expands towards more fields, wherein home decoration home furnishing is one of the typical fields. The existing home decoration home furnishing type sales service system can provide a whole home decoration design scheme for a consumer user, and the consumer user can select a complete set of whole home design scheme or select partial articles from the whole home design scheme for online ordering.

Designers typically install a design platform at home to match and layout the whole house for the user to create a whole house design. Specifically, the designer may perform design work according to the house type and the decoration style of the user, and in the design process, the designer needs to search for a model meeting the style requirements from a model library provided by the platform, wherein the model includes furniture models such as a wardrobe and a bed, and ornament models such as green plants, ornaments and curtains.

The behavior of the designer for searching the model has the characteristics of long time sequence, high frequency, repeated matching and the like, and the design efficiency is low. Therefore, how to bring efficient design experience to designers is an urgent problem to be solved.

Disclosure of Invention

The embodiment of the application provides a recommendation method, equipment and storage medium for article model data, and improves user design experience and efficiency.

A first aspect of an embodiment of the present application provides a method for recommending item model data, including:

acquiring a space category of a target space;

determining a first article model type missing from the target space according to the space type and the current space collocation data of the target space;

and pushing target item model data corresponding to the first item model category.

In an optional embodiment of the first aspect of the present application, the spatial categories comprise a spatial primary category; the obtaining of the space category of the target space includes:

determining a space main category of the target space according to the attribute information of all article models in the target space; the attribute information of the item model comprises a space main category corresponding to the item model.

In an optional embodiment of the first aspect of the present application, the determining, according to the attribute information of all item models in the target space, a space main category of the target space includes:

counting the quantity of the article models corresponding to each space main category according to the attribute information of all the article models;

and taking the space main category with the largest quantity of the item models as the space main category of the target space.

In an optional embodiment of the first aspect of the present application, the spatial category comprises a spatial subcategory below a spatial main category; the obtaining of the space category of the target space includes:

acquiring the space main category of the target space and the attribute information of all article models in the target space;

and determining the space subcategory according to the space main category and the attribute information of all the item models in the target space.

In an optional embodiment of the first aspect of the present application, the method further comprises: acquiring article configuration information corresponding to the space category, wherein the article configuration information is used for indicating a plurality of article model categories preset by the space category;

the determining the first item model type missing in the target space according to the space type and the current space collocation data of the target space includes:

and determining the first article model type according to the article configuration information corresponding to the space type and the current space collocation data of the target space.

In an optional embodiment of the first aspect of the present application, the item configuration information includes a plurality of item model categories preset by the space category and a priority of each item model category;

if there are multiple first item model types, determining the first item model type missing in the target space according to the item configuration information corresponding to the space type and the current space collocation data of the target space, including:

and determining a plurality of first item model types and the priority of each first item model type according to the item configuration information corresponding to the space type and the current space collocation data of the target space.

In an optional embodiment of the first aspect of the present application, the pushing target item model data corresponding to the first item model category includes:

acquiring data of N item models corresponding to the first item model category from the item model library, wherein N is a positive integer;

and pushing data of the N item models corresponding to the first item model category.

In an optional embodiment of the first aspect of the present application, the obtaining data of N item models corresponding to the first item model category from the item model library includes:

and acquiring data of the N item models matched with the style types input by the user from the item model library.

acquiring data of the N item models matched with the use preference of the user from the item model library;

or

And acquiring data of the first N item models of which the use times of the group users are greater than a threshold value from the item model library.

and pushing and displaying target item model data corresponding to the first item model category in a first window.

In an optional embodiment of the first aspect of the present application, if there are a plurality of first item model types, the pushing and displaying, in a first window, target item model data corresponding to the first item model type includes:

determining a second item model category of highest priority from the plurality of first item model categories;

and pushing and displaying target item model data corresponding to the second item model category on the first window.

acquiring data of a plurality of article models corresponding to each first article model category;

and pushing and displaying data of the part of the item model corresponding to each first item model category in the first window.

A second aspect of an embodiment of the present application provides an apparatus for recommending item model data, including:

the acquisition module is used for acquiring the space category of the target space;

the processing module is used for determining the first article model type missing from the target space according to the space type and the current space collocation data of the target space;

and the pushing module is used for pushing the target item model data corresponding to the first item model type.

In an optional embodiment of the second aspect of the present application, the spatial categories comprise a spatial primary category; an acquisition module to:

In an optional embodiment of the second aspect of the application, the processing module is configured to:

In an optional embodiment of the second aspect of the application, the spatial category comprises a spatial subcategory below a spatial main category; the acquisition module is used for acquiring the space main category of the target space and the attribute information of all article models in the target space;

and the processing module is used for determining the space subcategory according to the space main category and the attribute information of all the article models in the target space.

In an optional embodiment of the second aspect of the present application, the obtaining module is configured to obtain article configuration information corresponding to the space category, where the article configuration information is used to indicate multiple article model categories preset by the space category;

and the processing module is used for determining the first article model type according to the article configuration information corresponding to the space type and the current space collocation data of the target space.

In an optional embodiment of the second aspect of the present application, the item configuration information includes a plurality of item model categories preset by the space category and a priority of each item model category;

if there are multiple first item model categories, a processing module configured to:

In an optional embodiment of the second aspect of the present application, the obtaining module is configured to obtain, from the item model library, data of N item models corresponding to the first item model category, where N is a positive integer;

and the pushing module is used for pushing the data of the N article models corresponding to the first article model category.

In an optional embodiment of the second aspect of the present application, the obtaining module is configured to obtain data of the N item models matching the style type input by the user from the item model library.

In an optional embodiment of the second aspect of the present application, the obtaining module is configured to obtain, from the item model library, data of the N item models that match the user usage preference;

in an optional embodiment of the second aspect of the present application, the obtaining module is configured to obtain, from the item model library, data of top N item models, where the number of times of usage of group users is greater than a threshold.

In an optional embodiment of the second aspect of the present application, the pushing module is configured to push and display target item model data corresponding to the first item model category in a first window.

In an optional embodiment of the second aspect of the present application, if there are a plurality of first item model categories, the processing module is configured to determine a second item model category with a highest priority from the plurality of first item model categories;

and the pushing module is used for pushing and displaying the target item model data corresponding to the second item model type in the first window.

In an optional embodiment of the second aspect of the present application, if there are multiple first item model categories, the obtaining module is configured to obtain data of multiple item models corresponding to each of the first item model categories;

and the pushing module is used for pushing and displaying data of the part of the article model corresponding to each first article model category in the first window.

A third aspect of embodiments of the present application provides an electronic device, including: a memory, a processor, and a computer program; the computer program is stored in the memory and configured to be executed by the processor to implement the method of any one of the first aspects of the present application.

A fourth aspect of embodiments of the present application provides a computer-readable storage medium having a computer program stored thereon, the computer program being executable by a processor to implement the method according to any one of the first aspect of the present application.

A fifth aspect of embodiments of the present application provides a computer program product comprising a computer program which, when executed by a processor, performs the method of any one of the first aspects of the present application.

The embodiment of the application provides a recommendation method, equipment and a storage medium of article model data, wherein the method comprises the following steps: acquiring a space category of a target space based on clicking of the target space by a user; determining a first article model type of the target space missing according to the space type and the current space collocation data of the target space; and pushing target object model data corresponding to the first object model category to the user so that the user can quickly arrange and design the missing object models, and therefore user design experience and efficiency are improved.

Drawings

Fig. 1 is a schematic view of an application scenario of a recommendation method for item model data according to an embodiment of the present application;

fig. 2 is a schematic flowchart of a recommendation method for item model data according to an embodiment of the present application;

FIG. 3 is a schematic diagram of an interface provided by an embodiment of the present application;

FIG. 4 is a schematic view of an interface provided by an embodiment of the present application;

FIG. 5 is a schematic view of an interface provided by an embodiment of the present application;

FIG. 6 is a schematic diagram of an interface change provided by an embodiment of the present application;

FIG. 7 is a schematic diagram of an interface change provided by an embodiment of the present application;

fig. 8 is a schematic flowchart of a recommendation method for item model data according to an embodiment of the present application;

fig. 9 is a schematic structural diagram of an apparatus for recommending item model data according to an embodiment of the present application;

fig. 10 is a hardware configuration diagram of an electronic device according to an embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to the protection scope of the present application.

The terms "first," "second," and the like in the description and in the claims, and in the drawings, of the embodiments of the application are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in other sequences than described or illustrated herein.

It will be understood that the terms "comprises" and "comprising," and any variations thereof, as used herein, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

In the description of the embodiments of the present application, the term "correspond" may indicate that there is a direct correspondence or an indirect correspondence between the two, may also indicate that there is an association between the two, and may also indicate and be indicated, configure and configured, and so on.

First, a brief description will be given of relevant terms related to embodiments of the present application.

First, the object model refers to a model used in a home decoration design tool, for example, a furniture model such as a wardrobe, a bed, a sofa, and a table, a home appliance model such as a refrigerator, a washing machine, and a fan, a decoration model such as a carpet, a curtain, a mirror, and a green plant, and the like, and the present application is not limited thereto. The item model may be referred to herein simply as a model.

Secondly, the article model library comprises the data of the various article models, and the article models prestored in the article model library comprise both general models only used for home decoration design and models associated with physical products. Wherein, the model related to the physical product is a model which can be purchased by a user on line, and the designer can obtain a certain commission by using the model related to the physical product. The item model library may be referred to herein simply as the model library.

With the application of technologies such as internet, big data and cloud computing, the home decoration design industry tends to be intelligent and customized, and designers can perform home decoration design on line in real time through the internet to provide customized design services for users. At present, a common home decoration design tool is an online webpage version home decoration design tool, that is, a designer can access a home decoration design platform through a browser, and complete house matching and layout are performed on the platform. In the design process, the behavior of searching for the model by the designer has the characteristics of long time sequence, high-frequency words, rearrangement and the like, for example, the designer needs to search for the model meeting the requirements of the house type and the decoration style of the user in a model library of over millions for collocation and layout, the behavior of searching for the model only accounts for about 13% of the total design time, and the feedback of the designer on the using satisfaction degree of the model and the pain point difficult in model searching is increasingly strong.

In view of the above problems, an embodiment of the present application provides a method for recommending item model data, which has the following main inventive concepts: when a designer performs space configuration on a certain space, the designer needs to search an article model meeting the design requirements of a user in an article model library, including a main article model, a secondary article model and the like in the space. The primary item model can be understood as an indispensable item model in the space, and the secondary item model can be understood as an available item model in the space. To improve the usage experience of the designer, the inventor considers that the space category of the current space can be determined first based on the operation selected by the designer on any article model in the current space, for example, the current space is determined to be a living room or a bedroom. And secondly, determining which article models are missing in the current space based on article configuration information preset in the current space and existing article models in the current space, and intelligently recommending missing article model data, such as giving an article collocation recommendation list for a designer to refer to. Through the analysis of the existing space information and the existing article models in the design scheme, the article collocation information suitable for the layout of the current control is given, and a designer can quickly find one or a certain group of article models from the article collocation information to carry out space collocation and layout, so that the design efficiency is improved.

Optionally, in an embodiment, the designer may also input information such as style, color, label (e.g., new product, shipping, commission, animation, etc.) and the like of the item model in advance, and the platform may perform item collocation recommendation in combination with the information input by the designer, thereby improving the accuracy of intelligent recommendation.

Optionally, in an embodiment, the platform may also perform item collocation recommendation in combination with preferences of a designer using the model, or preferences of a group of users using the model, and the like, and may also improve accuracy of intelligent recommendation.

Optionally, in an embodiment, the spatial category may be further refined, that is, the spatial subcategories are divided under the spatial main category. For example, the main category of space is bedroom, and the subcategories are bedroom dressing table, bedroom desk, bedroom children's room, etc. For example, the main category of the space is living room, and the sub-categories thereof include areas such as living room television wall, living room sofa, and living room bar. The spatial subcategory is a small micro-scene of a certain space. And the space scene is refined, so that the model can be accurately pushed.

First, an application scenario of the technical solution provided in the embodiment of the present application is briefly introduced below.

Fig. 1 is a schematic view of an application scenario of a recommendation method for item model data according to an embodiment of the present application. As shown in fig. 1, the application scenario of the present embodiment includes a client 101 and a home decoration design platform 102, where the client 101 is in communication connection with the home decoration design platform 102, and the home decoration design platform 102 provides a home decoration design service. The home decoration design platform 102 is also referred to as a home decoration design server or a server. A user (i.e., designer) can access the home appliance design platform 102 through the client 101, perform online home appliance design on the platform, and generate a home appliance design solution. In practice, a user may access the home appliance design platform 102 through a browser, application, or applet on the client 101.

As an example, the home design platform 102 includes a recommender 103 of item model data. The recommending device 103 responds to the user operation, acquires the space type and space collocation data of the current design space, and pushes the article model data missing from the current design control to the client 101 based on the article model configuration, the article model library and the space collocation data corresponding to the space type, so that the user can quickly find a proper article model for space collocation and layout.

The article model configuration corresponding to the space category includes a plurality of groups of corresponding relations such as space category 1 and article model configuration information 1, space category 2 and article model configuration information 2, …, space category n and article model configuration information n, for example, the article model configuration information 1 corresponding to the space category 1 at least includes a plurality of article model categories whose spaces are category 1, for example, the article model categories of bedrooms include beds, bedside cabinets, wardrobes, and the like.

It should be noted that the recommendation apparatus 103 for item model data may also be disposed at a client, that is, the technical solution provided in the embodiment of the present application may be applied to the client or a server, and the embodiment is not limited in any way.

Optionally, the client 101 may be any electronic device with a display function, including but not limited to a smart phone, a notebook computer, a tablet computer, an intelligent vehicle-mounted device, an intelligent wearable device, an intelligent screen, and the like.

Optionally, the home decoration design platform 102 may be a common server or a cloud server, which is also called a cloud computing server or a cloud host and is a host product in a cloud computing service system. The home design platform 102 may also be a server of a distributed system or a server incorporating a blockchain.

Based on the application scenario, a recommendation scheme of the item model data provided by the embodiment of the present application is described in detail below by a specific embodiment. It should be noted that the technical solutions provided in the embodiments of the present application may include part or all of the following contents, and these specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.

Fig. 2 is a schematic flowchart of a method for recommending item model data according to an embodiment of the present application. As shown in fig. 2, the method for recommending item model data of the present embodiment includes the following steps:

step 201, obtaining the space type of the target space.

In one possible implementation, in response to a first operation of selecting an item model in a target space by a user, a space category of the target space is obtained. The first operation may be a click operation, and the item model selected by the user may be any item model in the target space.

In one possible implementation, the space category of the target space is obtained in response to a second operation of the user on any position of the target space. Wherein, the second operation may be a click operation.

In this embodiment, the target space may be any space selected by the user in the home decoration design drawing, and the user may click any position in the space, or may click a certain object model in the space. The spatial class of the target space includes a spatial primary class. Taking the field of home decoration design as an example, the main category of space may be a main category of space such as a kitchen, a bedroom, a living room, a dining room, a balcony, and the like.

The operation of the user in the home decoration design interface is exemplified below with reference to the drawings. For example, fig. 3 is a schematic interface diagram provided in an embodiment of the present application, and as shown in a in fig. 3, the home decoration design interface 300 includes a model selection area 301, a model editing area 302, a design navigation area 303, and a spatial design area 304. The model selection area 301 displays a public library which can be selected by a user, the user can search various article models in the public library, such as a furniture model, a decoration model, a kitchen and toilet model, a household appliance model, and the like, as shown in a in fig. 3, the user inputs a search word "wardrobe", and various types of wardrobes in the article model library are displayed in the model selection area 301. After a user selects an article model in the model selection area 301, the position and the orientation of the article model can be edited in the model editing area 302, as shown in a in fig. 3, the user selects a wardrobe model from the model selection area 301, the wardrobe model is dragged to a designated position of a bedroom in the design space, the position of the wardrobe model is adjusted according to the arrow indication on the wardrobe model, and as shown in a in fig. 3, the wardrobe model is in a selected state. In addition, the user can select any room in the design space in the design navigation area 303, thereby quickly entering the selected room for home design. The user may edit the parameters of the wall and the floor in the design space in the space design area 304, such as setting the height and thickness of the wall, setting the thickness of the floor, and so on.

In one embodiment, obtaining the spatial class of the target space comprises: and determining the space main category of the target space according to the attribute information of all article models in the target space. The attribute information of the item model comprises a space main category corresponding to the item model. It should be understood that the same item model may correspond to a plurality of space main categories, that is, the same item model may be disposed in a plurality of spaces, for example, a leisure chair may be disposed in a living room, a study room, and a bedroom.

Based on the above, the space main category of the target space is determined according to the space main categories in the attribute information of all the article models in the target space. Specifically, the number of the article models corresponding to each space main category may be counted according to the attribute information of all the article models, and the space main category with the largest number of the article models is used as the space main category of the target space. For example, the target space includes three types of articles, namely a bed, a bedside table and a leisure chair, the main type of the space corresponding to the bed and the bedside table is a bedroom, the main type of the space corresponding to the leisure chair is a bedroom or a study room or a living room, and the main type of the space corresponding to the target space can be determined to be the bedroom by counting that the number of article models corresponding to the bedroom is the largest.

Optionally, the attribute information of the item model further includes information such as brand, size, identification, category, price, commission, purchase link, etc. of the item model. Where the commission refers to a commission that the designer can obtain using the item model. The purchase link is usually associated with an e-commerce platform, and a designer or a consumer can click on the purchase link of the item model to view detailed information of the physical product associated with the item model.

In one embodiment, obtaining the spatial class of the target space comprises: and acquiring the space collocation data of the target space, and acquiring the space main category of the target space by analyzing the space collocation data of the target space. The space collocation data of the target space comprises an identification of a space main category of the target space. It should be understood that, when a user designs a target space, the main category of the space of the target space may be customized, for example, the room is set to be a bedroom, a study, a gymnasium, etc., and the identification of the main category of the space may be stored in the space collocation data of the target space based on the user customization.

Step 202, determining a first item model type with missing target space according to the space type and the current space collocation data of the target space.

In this embodiment, different space categories correspond to different article configuration information, and the article configuration information is used to indicate a plurality of article model categories preset by the space categories. The article configuration information of different space categories can be stored in a database of the home decoration design platform in advance.

In one embodiment, the method includes the steps of obtaining article configuration information corresponding to a space type of a target space from a database of a home decoration design platform, and determining a first article model type according to the article configuration information corresponding to the space type and current space collocation data of the target space. The current space collocation data of the target space comprises current item model data of the target space, such as identification, space position and orientation of an item model, main space category of the target space and the like.

Optionally, the article configuration information corresponding to the space category of the target space includes article configuration information corresponding to the main space category of the target space, for example, the main space category of the target space is a bedroom, and the article configuration information corresponding to the bedroom includes article models such as a bed, a wardrobe, a bedside table, a bedside lamp, a hat rack, and a curtain. Correspondingly, determining the first article model type according to the article configuration information corresponding to the space type and the current space collocation data of the target space, including: and determining the type of the first article model according to the article configuration information corresponding to the main type of the space and the current space collocation data of the target space.

It should be understood that the current item models in the target space may not be complete, and based on the item configuration information corresponding to the primary category of space in the target space, it may be determined which item models are missing in the target space, the missing item models may be one or more, and accordingly, the number of the first item model categories may be one or more.

Optionally, the article configuration information corresponding to the space main category includes a plurality of article model categories preset by the space main category and a priority of each article model category. For example, taking the main category of the space as a bedroom as an example, the bedroom is preset with the following article models: the necessary article models such as beds, wardrobes, bedside cabinets and the like and unnecessary article models such as bedside lamps, bed stools, hat racks and the like can be seen to have different priorities of the article models of different types.

In this embodiment, the priority of the item model may be determined according to the necessary degree (or importance degree) of the item model in the target space. Alternatively, the priority levels of the item models in the target space may be set to two or more. For example, taking the target space as a bedroom, the first-level item model includes: bed, bedside cupboard, wardrobe, the article model of second grade has: bedside lamp, bed bench, hat rack, air conditioner, the article model of third grade has: wall lamps, carpets, curtains, greens, mirrors, etc. Where the priority levels from high to low are: the article models of the first grade, the second grade and the third grade are generally ornament-like article models.

In an embodiment, if there are a plurality of first item model types, that is, there are a plurality of item models with missing target space, the plurality of first item model types and the priority of each first item model type may be determined according to the item configuration information corresponding to the space type and the current space collocation data of the target space. Wherein the purpose of determining the priority of each first item model category is to: item model recommendations can be based on priority, for example, when a plurality of item models are missing, item models with high priority can be recommended first, and then item models with the second priority can be recommended.

And step 203, pushing target item model data corresponding to the first item model type.

In one possible embodiment, target item model data corresponding to a first item model category is obtained from an item model library. In this embodiment, the number of the first item model categories may be one or more.

For ease of understanding, the following scenario describes the number of first item model categories as one.

In an embodiment, the target item model data corresponding to the first item model category includes data of N item models of the first item model category, that is, data of N item models corresponding to the first item model category is obtained from an item model library, where N is a positive integer. For example, if the first item model category currently missing in the target space is determined to be bedside table, model data for N bedside tables may be recommended from the item model library.

In an embodiment, when data of N item models corresponding to the first item model category is pushed, if the data of the N item models corresponding to the first item model category is more, a part of the N item models may be pushed first, and a user views the remaining part of the item models by page turning.

In one embodiment, after determining the first item model category missing from the target space, the data of the N item models may be randomly selected from the item model library according to the first item model category.

In order to improve the recommendation accuracy of item model data, N item models can be selected according to the following embodiments:

in one embodiment, the data of the N item models matching the style type input by the user may be obtained from the item model library in combination with the style type input by the user, that is, the data of the N item models may be obtained from the item model library according to the first item model category and the style type input by the user. For example, if it is determined that the first item model category currently missing in the target space is bedside table and the style type input by the user is nordic wind, model data of N nordic wind bedside tables can be recommended from the item model library for the user to select. The genre type may be selected in selection control 701 in model selection area 301, referring to the interface shown in a in FIG. 7.

In one embodiment, the data of the N item models matching the user usage preference may be obtained from an item model library in combination with the user's historical design data, i.e., the data of the N item models may be obtained from the item model library according to the first item model category and the user's historical design data. It should be appreciated that the user's preferences for using the item model, such as style, color, hue, presence or absence of commissions, etc., of the item model that the user often uses, can be determined based on the user's historical design data. For example, determining that the first item model category currently missing in the target space is a bedside table, and determining that the color tone of the item model frequently used by the user is a warm color tone, model data of the bedside tables of N warm color tones may be recommended from the item model library for selection by the user.

In one embodiment, the historical design data of the group users may be combined to obtain data of the top N article models of which the number of times of use by the group users is greater than the threshold from the article model library, that is, the data of the top N article models of which the number of times of use by the group users is greater than the threshold (that is, the popular article models of the first article model category) may be obtained from the article model library according to the first article model category and the historical use data of the group users on the first article model category.

In one embodiment, the style type input by the user and the historical design data of the group users can be combined to obtain the data of the top N article models which are matched with the style type input by the user and used by the group users more than the threshold value from the article model library.

The above embodiments show how to obtain recommendation data of a certain type of article model from an article model library, and the recommendation data of the article model can be recommended by combining historical design data of a user, model requirements input by the user or historical design data of group users. Based on this, if the number of the types of the first item models is multiple, the recommendation data of each type of item model is acquired for each type of item model, and finally all the acquired recommendation data are summarized.

In one embodiment, the target item model data is displayed in a model selection area of the home appliance design interface. For example, assuming that the displayed target item model is determined to be a bedside cabinet model, after the user selects the wardrobe model in the model editing area 302, the display content of the model selection area 301 is changed from the originally displayed plurality of wardrobe models to the plurality of bedside cabinet models shown in b in fig. 3, as shown in b in fig. 3.

In one embodiment, the target item model data is pushed and displayed in a first window of the home appliance design interface. The first window may be located at any position of the home decoration design interface, for example, the first window is located at the center of the home decoration design interface, or the bottom of the model selection area of the home decoration design interface.

Fig. 4 is a schematic interface diagram provided in an embodiment of the present application. Assuming that the displayed target item model is determined to be a bedside table model, as shown in a in fig. 4, after the user selects the wardrobe model in the model edit area 302, a first window 401 is displayed at the bottom of the model selection area 301, and recommended bedside table models of, for example, 6 are displayed in the first window 401.

In one embodiment, if there are a plurality of first item model categories, the method for push-displaying the target item model data in the first window includes: and determining a second item model type with the highest priority from the plurality of first item model types, and pushing and displaying target item model data corresponding to the second item model type in the first window.

In this embodiment, the necessary item models with missing target spaces are preferentially recommended, that is, the item models with high priority are selected from the missing item models for recommendation and display, so that recommendation more conforms to the design idea and intention of the user, and accurate pushing is realized.

It should be noted that the number of the second item model types with the highest priority may be one or more, and if the number of the second item model types with the highest priority is more than one, the item model data of the plurality of second item model types may be displayed at the same time. Furthermore, if the model selection area or the first window of the home decoration design interface can display N item models at most, the second item models are divided into N display positions according to the types.

As shown in b of fig. 4, assuming that two types of target item models are determined to be preferentially displayed, namely, a bed model and a bedside table model, and the first window can display 6 item models at most, 3 bed models and 3 bedside table models can be displayed in the first window 401. Optionally, the user may view more item model recommendations by clicking the page turn button of the first window 401.

In one embodiment, if there are a plurality of first item model categories, the method for push-displaying the target item model data in the first window includes: and acquiring data of a plurality of article models corresponding to each first article model type, and pushing and displaying data of part of article models corresponding to each first article model type in a first window.

In this embodiment, a group of item models with missing target spaces is preferentially recommended, that is, for example, one recommendation model is selected from each missing item model to combine into a group of recommendation models, where the group of recommendation models includes the recommendation models of a plurality of missing items, so that a user can simultaneously view recommendation data of the missing group of item models, and the user can select a certain missing item model from the recommendation models to perform quick matching.

Fig. 5 is a schematic interface diagram provided in an embodiment of the present application. As shown in fig. 5, it is assumed that a plurality of object models are determined to be displayed, such as a bed, a bedside table, a bed stool, a bedside lamp, a hat rack, and a green plant in fig. 5, wherein the bed and the bedside table are object models of a first priority of an object space, the bed stool, the bed lamp, and the hat rack are object models of a second priority of the object space, and the green plant is an object model of a third priority of the object space. After the user selects the wardrobe model in the model editing area 302, a first window 501 is displayed at the bottom of the model selection area 301, and a recommended model of each of the above-mentioned item models is displayed in the first window 501. The user can also click the "one-button decoration" control on the first window 501, so as to realize one-button placement of the recommended item model in the target space. The user may also click on the page flip button of the first window 501 and may view more sets of item model recommendations.

Fig. 6 is a schematic view illustrating an interface change according to an embodiment of the present application. Assuming that the target item models displayed preferentially are determined to be a bed model and a bedside table model, after the user selects the wardrobe model in the model editing area 302, a first window 601 is displayed at the bottom of the model selection area 301, and the first window 601 displays the categories of the missing high-priority target item models which can be selected by the user, such as the selection controls of the bedside table and the bed shown in a in fig. 6. If the user clicks the controls corresponding to the bed in the first window 601, the recommended bed models may be displayed in the model selection area 301, as shown in b in fig. 6, the first window 601 may be kept in a suspended state, so that after the user clicks a certain recommended bed model, the user continues to click the controls corresponding to the bedside table in the first window 601, thereby further displaying the recommended bedside table models in the model selection area 301. The operation process can improve the design efficiency of users. In addition, the user can click the intelligent collocation control of the first window 601 to realize that the recommended bed model and the bedside table model are placed in the target space by one key.

Fig. 7 is a schematic view illustrating an interface change according to an embodiment of the present application. Assuming that a plurality of target object models are determined to be displayed, such as a bed, a bedside table, a bed stool, a bedside lamp, a hat rack, and a green plant shown in a in fig. 7, the pre-configured priorities of the various object models are as described in the embodiment of fig. 5. Unlike the interface shown in fig. 5, a selection control 701 is further provided in the model selection area 301 shown in a in fig. 7, and the selection control 701 can be used for the user to select the style and category (i.e. category) of the item model. The user clicks on the category item in the selection control 701 and may display the categories of the item models recommended in the first window 501 in a drop-down window, and after the user clicks on, for example, a hat rack in the drop-down window, the first window 501 may be collapsed while displaying the recommended plurality of hat rack models in the model selection area 301, as shown in b in fig. 7.

In the recommendation method of item model data shown in this embodiment, a target space is clicked by a user, and a space type of the target space is obtained; determining a first article model type missing in a target space according to the space type and current space collocation data of the target space; and pushing target article model data corresponding to the first article model category to the user so that the user can quickly layout and design the missing article model, and therefore the user design experience and efficiency are improved.

Furthermore, the target item model data corresponding to the first item model category can be obtained by combining the historical design data or design requirements of the user or the historical design data of group users, so that the item model data recommended to the user more meets the requirements of the user, and the recommendation accuracy of the item model data is improved.

Based on the above embodiments, the space category of the target space may be further refined, that is, a space sub-category is divided under the main category of the space, for example, the main category of the space is bedroom, and the sub-category is bedroom dressing table, bedroom desk, bedroom children room, and the like. Accordingly, item configuration information corresponding to the spatial subcategory may be configured to indicate which item models should be included in the spatial subcategory. The recommendation method for the item model data is further optimized in the following embodiments.

Fig. 8 is a flowchart illustrating a method for recommending item model data according to an embodiment of the present application. As shown in fig. 8, the method for recommending item model data of the present embodiment includes the following steps:

step 801, acquiring the space main category of the target space and the attribute information of all article models in the target space.

In one possible implementation, in response to a first operation of selecting an item model in a target space by a user, a space main category of the target space and attribute information of all item models in the target space are acquired. In one possible implementation mode, in response to a second operation of acting on any position of the target space by the user, the space main category of the target space and the attribute information of all article models in the target space are obtained.

In this embodiment, the main category of the target space may be obtained in the following two ways: in an optional implementation manner, the main category of the target space is obtained by analyzing the spatial collocation data of the target space. In another optional implementation, the space main category of the target space is determined according to the attribute information of the existing item model in the target space. The processing procedure may specifically refer to step 201 of the above embodiment, and is not described herein again.

The attribute information of the item model includes a spatial main category corresponding to the item model. Optionally, the attribute information of the item model further includes information of brand, size, identification, category, price, commission, purchase link, etc. of the item model.

Optionally, the attribute information of the item model may further include a spatial sub-category corresponding to the item model. It should be understood that the same item model may correspond to multiple space sub-categories, for example, a lounge chair may be provided in the area of a bedroom dresser, a living room bar, a bedroom desk, etc.

And step 802, determining a space subcategory according to the space main category of the target space and the attribute information of all the item models in the target space.

In one embodiment, before determining the spatial subcategory based on the spatial main category and the attribute information of all item models in the target space, the following steps may be performed:

step a, checking whether each article model in the target space is matched with the space main category of the target space.

And b, if the object model in the target space is not matched with the space main type of the target space, correcting the space main type of the object model in the target space.

The purpose of executing the steps is as follows: the user may place an item model in the target space that by default does not belong to the target space. For example, the main category of the space corresponding to the three-person sofa is defaulted to the living room, and if it is determined that the target space currently designed by the user is the bedroom, the main category of the space corresponding to the three-person sofa needs to be modified, that is, the main category of the space corresponding to the default three-person sofa is modified to the bedroom in the living room. Through the data verification process, the space collocation data of the target space is accurate.

In this embodiment, the spatial subcategories of the target space are determined according to the main spatial category of the target space and the category in the attribute information of each item model in the target space, that is, based on the existing item model and the main spatial category of the target space, which subcategory of the main spatial category the target space is, that is, the small micro-scene in the space is determined.

Step 803, determining the first item model type with missing target space according to the space sub-type and the current space collocation data of the target space.

In one embodiment, the method includes the steps of obtaining article configuration information corresponding to a space type of a target space from a database of a home decoration design platform, and determining a first article model type according to the article configuration information corresponding to the space type and current space collocation data of the target space. In this embodiment, the article configuration information corresponding to the space category of the target space includes article configuration information corresponding to the space subcategory of the target space, for example, the space subcategory of the target space is a bedroom dresser, and the article configuration information corresponding to the bedroom dresser includes article models such as a dresser, a dressing chair, a dressing mirror, a decoration, and a green plant.

Correspondingly, determining the first item model category missing from the target space according to the space sub-category and the current space collocation data of the target space, includes: and determining a first item model category according to the item configuration information corresponding to the space sub-category and the current space collocation data of the target space.

It should be understood that the current item models of the target space may not be complete, and based on the item configuration information corresponding to the spatial sub-category of the target space, it may be determined which item models are missing from the target space, and the missing item models may be one or more. Accordingly, the number of first item model categories may be one or more.

Optionally, the item configuration information corresponding to the space sub-category includes a plurality of item model categories preset by the space sub-category and a priority of each item model category. For example, taking the space category as a bedroom dresser as an example, the bedroom dresser is preset with the following object models: essential object models such as dressing tables, dressing chairs, dressing mirrors and the like, and unnecessary object models such as ornaments, green plants and the like.

In one embodiment, if there are multiple first item model categories, the multiple first item model categories and the priority of each first item model category may be determined according to the item configuration information corresponding to the space sub-category and the current space collocation data of the target space.

Step 804, target item model data corresponding to the first item model category is obtained from the item model library.

And step 805, pushing and displaying the target article model data.

Step 804 and step 805 of this embodiment correspond to step 203 and step 204 of the above embodiment, respectively, and the implementation principle thereof is similar, and specific reference may be made to the above embodiment, which is not described herein again.

In the recommendation method for item model data shown in this embodiment, a target space is clicked by a user, a main space category and space collocation data of the target space are obtained, and a sub-space category of the target space is determined based on the main space category and the space collocation data; determining a first item model category missing in the target space according to the space sub-category and the current space collocation data of the target space; and pushing target object model data corresponding to the first object model category to the user so that the user can quickly arrange and design the missing object models, and therefore user design experience and efficiency are improved. The method is particularly suitable for recommending the item models in the small micro scenes.

It should be noted that, in the technical solution provided in the embodiment of the present application, the article model may be expanded into a material, and the material includes, in addition to the article model, a hard-mounted material such as a floor, a tile, a wallpaper, a ceiling, and the like. Correspondingly, the article model library can be expanded into a material library, and the material library comprises data of the various article models, hardwares and the like.

The method for recommending item model data provided in the embodiment of the present application is described above, and the apparatus for recommending item model data provided in the embodiment of the present application will be described below.

In the embodiment of the present application, the recommendation apparatus for item model data may be divided into functional modules according to the method embodiments, for example, each functional module may be divided corresponding to each function, or two or more functions may be integrated into one processing module. The integrated module can be realized in a form of hardware or a form of a software functional module. It should be noted that, in the embodiment of the present application, the division of the module is schematic, and is only one logic function division, and there may be another division manner in actual implementation. The following description will be given by taking an example in which each functional module is divided by using a corresponding function.

Fig. 9 is a schematic structural diagram of an apparatus for recommending item model data according to an embodiment of the present application. As shown in fig. 9, the recommendation apparatus 900 for item model data of the present embodiment includes: an obtaining module 901, a processing module 902 and a pushing module 903.

An obtaining module 901, configured to obtain a space category of a target space;

a processing module 902, configured to determine, according to the space type and the current space collocation data of the target space, a first item model type missing from the target space;

a pushing module 903, configured to push target item model data corresponding to the first item model category.

In an optional embodiment of this embodiment, the spatial class comprises a spatial major class; an obtaining module 901, configured to:

In an optional embodiment of this embodiment, the processing module 902 is configured to:

In an optional embodiment of this embodiment, the spatial category includes a spatial subcategory below a spatial main category; an obtaining module 901, configured to obtain a space main category of the target space and attribute information of all article models in the target space;

a processing module 902, configured to determine the spatial subcategory according to the spatial main category and the attribute information of all item models in the target space.

In an optional embodiment of this embodiment, the obtaining module 901 is configured to obtain article configuration information corresponding to the space category, where the article configuration information is used to indicate a plurality of article model categories preset by the space category;

a processing module 902, configured to determine the first item model type according to the item configuration information corresponding to the space type and the current space collocation data of the target space.

In an optional embodiment of this embodiment, the item configuration information includes a plurality of item model categories preset by the space category and a priority of each item model category;

if there are more than one first item model categories, a processing module 902 is configured to:

In an optional embodiment of this embodiment, the obtaining module 901 is configured to obtain, from the item model library, data of N item models corresponding to the first item model category, where N is a positive integer;

the pushing module 903 is configured to push data of the N item models corresponding to the first item model category.

In an optional embodiment of this embodiment, the obtaining module 901 is configured to obtain, from the item model library, data of the N item models that match the style type input by the user.

In an optional embodiment of this embodiment, the obtaining module 901 is configured to obtain, from the item model library, data of the N item models that match the user usage preference;

in an optional embodiment of this embodiment, the obtaining module 901 is configured to obtain, from the item model library, data of top N item models whose usage times of group users are greater than a threshold.

In an optional embodiment of this embodiment, the pushing module 903 is configured to push and display target item model data corresponding to the first item model category in a first window.

In an optional embodiment of this embodiment, if there are a plurality of first item model categories, the processing module 902 is configured to determine a second item model category with a highest priority from the plurality of first item model categories;

the pushing module 903 is configured to push and display target item model data corresponding to the second item model category in the first window.

In an optional embodiment of this embodiment, if there are a plurality of first item model types, the obtaining module 901 is configured to obtain data of a plurality of item models corresponding to each first item model type;

the pushing module 903 is configured to push and display data of a part of the item model corresponding to each first item model category in the first window.

The recommendation apparatus for item model data provided in this embodiment may implement the technical solution of any one of the foregoing method embodiments, and the implementation principle and the technical effect are similar, which are not described herein again.

Fig. 10 is a hardware configuration diagram of an electronic device according to an embodiment of the present application. As shown in fig. 10, the electronic device 1000 provided in this embodiment includes:

a memory 1001, a processor 1002, and a computer program; the computer program is stored in the memory 1001 and configured to be executed by the processor 1002 to implement the technical solution of any one of the foregoing method embodiments, and the implementation principle and technical effect are similar, which are not described herein again.

Alternatively, the memory 1001 may be separate or integrated with the processor 1002. When the memory 1001 is a separate device from the processor 1002, the electronic device 1000 further includes: a bus 1003 is used to connect the memory 1001 and the processor 1002.

The embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor 1002 to implement the technical solution of any one of the foregoing method embodiments.

An embodiment of the present application provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the computer program implements the technical solutions of any of the foregoing method embodiments.

The embodiment of the application provides a chip, including: a processing module and a communication interface, the processing module being capable of performing the solution of any of the method embodiments described above.

Optionally, the chip further includes a storage module (e.g., a memory), where the storage module is configured to store instructions, and the processing module is configured to execute the instructions stored by the storage module, and execute the instructions stored in the storage module to enable the processing module to execute the technical solution of any one of the foregoing method embodiments.

It should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose processors, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.

The memory may comprise a high speed RAM memory, and may further comprise a non-volatile storage NVM, such as at least one magnetic disk memory, and may also be a usb disk, a removable hard disk, a read-only memory, a magnetic or optical disk, or the like.

The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, the buses in the figures of the present application are not limited to only one bus or one type of bus.

The storage medium may be implemented by any type or combination of volatile and non-volatile memory devices, 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 or optical disks. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.

An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an Application Specific Integrated Circuits (ASIC). Of course, the processor and the storage medium may reside as discrete components in an electronic device.

Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications or substitutions do not depart from the spirit and scope of the present disclosure as defined by the appended claims.

Claims

1. A recommendation method of item model data is characterized by comprising the following steps:

acquiring a space category of a target space;

determining a first article model type missing in the target space according to the space type and the current space collocation data of the target space;

and pushing target item model data corresponding to the first item model type.

2. The method of claim 1, wherein the spatial categories comprise a spatial major category; the obtaining of the space category of the target space includes:

3. The method according to claim 2, wherein the determining the main category of the target space according to the attribute information of all the item models in the target space comprises:

4. The method of claim 1, wherein the spatial categories include a spatial subcategory below a spatial main category; the obtaining of the space category of the target space includes:

5. The method according to any one of claims 1 to 4, further comprising:

acquiring article configuration information corresponding to the space category, wherein the article configuration information is used for indicating a plurality of article model categories preset by the space category;

determining the first item model type missing from the target space according to the space type and the current space collocation data of the target space, including:

6. The method according to claim 5, wherein the item configuration information comprises a plurality of item model categories preset by the space category and a priority of each item model category;

and determining a plurality of first article model categories and the priority of each first article model category according to the article configuration information corresponding to the space categories and the current space collocation data of the target space.

7. The method according to any one of claims 1 to 4, wherein the pushing of the target item model data corresponding to the first item model category comprises:

acquiring data of N item models corresponding to the first item model type from the item model library, wherein N is a positive integer;

8. The method according to claim 7, wherein the obtaining data of N item models corresponding to the first item model category from the item model library comprises:

9. The method according to claim 7, wherein the obtaining data of N item models corresponding to the first item model category from the item model library comprises:

or alternatively

10. The method according to any one of claims 1 to 4, wherein the pushing of the target item model data corresponding to the first item model category comprises:

11. The method according to claim 10, wherein if there are a plurality of first item model types, said pushing and displaying the target item model data corresponding to the first item model type in the first window comprises:

12. The method according to claim 10, wherein if there are a plurality of first item model types, said pushing and displaying the target item model data corresponding to the first item model type in the first window comprises:

13. An electronic device, comprising: a memory, a processor, and a computer program; the computer program is stored in the memory and configured to be executed by the processor to implement the method of any one of claims 1 to 12.

14. A computer-readable storage medium, on which a computer program is stored, which computer program is executed by a processor to implement the method according to any one of claims 1 to 12.