User data sharing method for furniture platform and related equipment

Abstract

The invention provides a user data sharing method for a furniture platform and related equipment, and the method comprises the steps: determining a furniture feature vector of each piece of furniture and a user feature vector of each user in shared data through obtaining the shared data in a specified furniture platform; determining an approaching value set of each user feature vector according to all furniture feature vectors, determining a cooperation degree between two corresponding users according to every two approaching value sets, determining a hot start furniture decision domain of a target user according to all the cooperation degrees, determining a sharing prediction value of each kind of furniture in the hot start furniture decision domain, and determining the sharing prediction value of each kind of furniture in the hot start furniture decision domain. According to the method and the device, the furniture recommendation information of the target user is obtained, the adjustment coefficient of the target user for each kind of furniture is determined, the furniture recommendation information of the target user is adjusted according to the adjustment coefficient of the target user for each kind of furniture, the adjusted furniture recommendation information is shared to the shared database of the furniture platform, and personalized shared recommendation can be carried out on potentially ordered furniture of the target user.

Description

Technical Field

[0001] This application relates to the technical field of furniture platforms. More specifically, this application relates to a user data sharing method and related devices for a furniture platform. Background Art

[0002] Furniture platform technology is a database platform for users to share data and order furniture, integrated through technologies such as database management, recommendation systems, big data, security, and privacy protection. The furniture platform collects the basic data of registered users on the platform and the furniture on file, and combines the basic information of users through data analysis algorithms to recommend customized furniture for users. With the development of the furniture platform, furniture recommendations have become more efficient and accurate for each user.

[0003] The user data sharing of the furniture platform is an operation to share the basic information when users register on the furniture platform and data such as furniture ordering records and browsing records during the use process on the furniture platform. By sharing the user data of the furniture platform, the accuracy of furniture recommendations for each registered user on the furniture platform can be improved, and the data stacking on the furniture platform can be reduced. However, in the prior art, only the registered basic data, furniture ordering records, and browsing records of the target user can be used to recommend furniture for the target user, ignoring the recommendation of furniture that some target users do not have ordering records and browsing records but will order. Therefore, how to achieve personalized shared recommendations for potential ordered furniture of target users has become an urgent problem to be solved. Summary of the Invention

[0004] This application provides a user data sharing method and related devices for a furniture platform, which can achieve personalized shared recommendations for potential ordered furniture of target users.

[0005] In a first aspect, this application provides a user data sharing method for a furniture platform, including the following steps: Obtain the shared data in a specified furniture platform, and determine the furniture feature vector of each furniture and the user feature vector of each user in the shared data; Determine the approximation value set of each user feature vector according to all the furniture feature vectors, and determine the cooperation degree between two corresponding users according to every two approximation value sets; Determine the hot start furniture decision domain of the target user according to all the cooperation degrees; Select a piece of furniture in the hot start furniture decision domain, determine the sharing prediction value of this piece of furniture according to all the cooperation degrees between the target user and other users, determine the adjustment coefficient of the target user for this piece of furniture according to the sharing prediction value and the approximation value set of the target user, and continue to determine the adjustment coefficients of the target user for the remaining furniture in the hot start furniture decision domain; Adjust the furniture recommendation information of the target user according to the adjustment coefficient of the target user for each piece of furniture, and share the adjusted furniture recommendation information to the shared database of the furniture platform.

[0006] In some embodiments, determining the set of approaching values of each user feature vector according to all furniture feature vectors specifically includes: Obtain a user feature vector; Determine the approaching value between this user feature vector and all furniture feature vectors; Take the set composed of all approaching values as the set of approaching values of this user feature vector; Repeat the above steps to determine the set of approaching values of the remaining user feature vectors.

[0007] In some embodiments, determining the hot-start furniture decision domain of the target user according to all synergy degrees specifically includes: Determine the feature vector layer of the target user according to all synergy degrees, and further determine the hot-start furniture decision domain of the target user through the feature vector layer of the target user.

[0008] In some embodiments, adjusting the furniture recommendation information of the target user according to the adjustment coefficient of the target user for each piece of furniture specifically includes: Obtain the adjustment coefficient of a piece of furniture in the hot-start furniture decision domain; Determine the furniture recommendation level of this piece of furniture according to this adjustment coefficient; Repeat the above steps to determine the furniture recommendation levels of the remaining furniture in the hot-start furniture decision domain; Adjust the furniture recommendation information of the target user according to the furniture recommendation levels of all furniture in the hot-start furniture decision domain.

[0009] In some embodiments, determining the furniture recommendation level of this piece of furniture according to this adjustment coefficient specifically includes: Divide the furniture recommendation level into strong recommendation level, secondary recommendation level, and weak recommendation level according to the value of the adjustment coefficient, and further determine the furniture recommendation level of this piece of furniture.

[0010] In some embodiments, take the basic features of each furniture on file in the shared database of the furniture platform and the basic features of each registered user as the shared data in the furniture platform.

[0011] In some embodiments, take the feature vector composed of the basic features of a piece of furniture in a specified furniture platform as the furniture feature vector.

[0012] In a second aspect, the present application provides a device related to user data sharing for a furniture platform, including: An acquisition module, configured to acquire shared data in a specified furniture platform, and determine a furniture feature vector for each piece of furniture and a user feature vector for each user in the shared data; A processing module, configured to determine an approximation value set for each user feature vector according to all the furniture feature vectors, and determine the cooperation degree between two corresponding users according to every two approximation value sets; The processing module is further configured to determine a hot-start furniture decision domain for a target user according to all the cooperation degrees; The processing module is further configured to select a piece of furniture in the hot-start furniture decision domain, determine a sharing prediction value for the piece of furniture according to all the cooperation degrees between the target user and other users, determine an adjustment coefficient of the target user for the piece of furniture according to the sharing prediction value and the approximation value set of the target user, and continue to determine the adjustment coefficients of the target user for the remaining furniture in the hot-start furniture decision domain; A sharing module, configured to adjust the furniture recommendation information of the target user according to the adjustment coefficient of the target user for each piece of furniture, and share the adjusted furniture recommendation information to the shared database of the furniture platform.

[0013] In a third aspect, the present application provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to acquire the code and execute the above-mentioned user data sharing method for a furniture platform.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned user data sharing method for a furniture platform is implemented.

[0015] The technical solution provided by the disclosed embodiments of the present application has the following beneficial effects: In the embodiments of the present application, after obtaining the shared data in the specified furniture platform, the furniture feature vector of each piece of furniture and the user feature vector of each user in the shared data are determined. The approximation value set of each user feature vector is determined according to all the furniture feature vectors. The collaboration degree between two corresponding users is determined according to every two approximation value sets. The hot-start furniture decision domain of the target user is determined according to all the collaboration degrees. One piece of furniture in the hot-start furniture decision domain is selected. The shared prediction value of this piece of furniture is determined according to all the collaboration degrees between the target user and other users. The adjustment coefficient of the target user for this piece of furniture is determined according to the shared prediction value and the approximation value set of the target user. The adjustment coefficients of the target user for the remaining furniture in the hot-start furniture decision domain are continuously determined. The furniture recommendation information of the target user is adjusted according to the adjustment coefficient of the target user for each piece of furniture. The adjusted furniture recommendation information is shared to the shared database of the furniture platform. In the present application, the similarity between every two users among all users is characterized by the collaboration degree between users, so as to find the similar users of the target user, and determine the furniture that will be reserved by the target user among the furniture not used by the target user according to the ordering furniture records of the similar users. The set of all furniture that will be reserved by the target user is used as the hot-start furniture decision domain, and then the shared prediction values of all the furniture in the hot-start furniture decision domain are determined. The tendency degree of the target user for the selected furniture is predicted through the shared prediction value, and then the adjustment coefficients of all the furniture in the hot-start furniture decision domain are determined according to the tendency degree. Finally, the furniture recommendation information of the target user is adjusted according to the adjustment coefficient, so as to increase the recommendation proportion of potential ordering furniture when recommending furniture to the target user. In summary, the personalized shared recommendation of the target user's potential ordering furniture is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is an exemplary flowchart of a user data sharing method for a furniture platform shown according to some embodiments of the present application; Figure 2 is a schematic flowchart of determining the approximation value set of each user feature vector shown according to some embodiments of the present application; Figure 3 is a schematic flowchart of adjusting the furniture recommendation information of the target user shown according to some embodiments of the present application; Figure 4 is a schematic diagram of the module composition of a user data sharing system for a furniture platform shown according to some embodiments of the present application; Figure 5 is a schematic diagram of the structure of a computer device for implementing a user data sharing method for a furniture platform shown according to some embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The core of this application is to determine the furniture feature vectors of each piece of furniture and the user feature vectors of each user in the shared data by obtaining the shared data in a specified furniture platform. Based on all the furniture feature vectors, the set of approximation values of each user feature vector is determined. Based on every two sets of approximation values, the collaboration degree between the corresponding two users is determined. Based on all the collaboration degrees, the hot-start furniture decision domain of the target user is determined. The shared prediction value of each piece of furniture in the hot-start furniture decision domain is determined, and then the adjustment coefficient of the target user for each piece of furniture is determined. The furniture recommendation information of the target user is adjusted according to the adjustment coefficient of the target user for each piece of furniture, and the adjusted furniture recommendation information is shared to the shared database of the furniture platform, so as to realize personalized shared recommendations for the potential ordered furniture of the target user.

[0018] To better understand the above technical solution, the above technical solution will be described in detail below in combination with the accompanying drawings of the specification and specific implementation manners. Refer to Figure 1 , which is an exemplary flowchart of a user data sharing method for a furniture platform according to some embodiments of the present application. The user data sharing method 100 for the furniture platform mainly includes the following steps: In step 101, obtain the shared data in a specified furniture platform, and determine the furniture feature vectors of each piece of furniture and the user feature vectors of each user in the shared data.

[0019] Specifically, the shared data in the specified furniture platform can be obtained through the shared database of the furniture platform. It should be noted that the shared database of the furniture platform is a database including user basic information and furniture basic information and used for information sharing. The shared data in the specified furniture platform is the basic features of each furniture on file and the basic features of each registered user in the shared database of the furniture platform. The basic features of the furniture include material, design style, price, size, and color. The basic features of the user include name, age, address, hobbies, and consumption preferences. The furniture feature vector is a feature vector composed of the basic features of a piece of furniture in the specified furniture platform. The user feature vector is a feature vector composed of the basic features of a user in the specified furniture platform. In addition, in this application, each furniture feature vector is sorted according to the filing time sequence of each piece of furniture in the shared database of the furniture platform, and each user feature vector is sorted according to the registration time sequence of each user.

[0020] In addition, it should be noted that in the process of forming the feature vector composed of basic features, it is realized according to the one-hot encoding technology in the prior art. In other embodiments, other methods can also be used to form the corresponding feature vectors of each user and furniture respectively, which is not limited here.

[0021] In step 102, based on all furniture feature vectors, a set of approximation values for each user feature vector is determined, and the collaboration degree between two corresponding users is determined according to every two sets of approximation values.

[0022] In some embodiments, with reference to Figure 2 As shown, the figure is a schematic flowchart for determining a set of approximation values for each user feature vector in some embodiments of the present application. In this embodiment, determining a set of approximation values for each user feature vector based on all furniture feature vectors can be implemented by the following steps: First, in step 1021, a user feature vector is obtained. Then, in step 1022, the approximation values between this user feature vector and all furniture feature vectors are determined. Secondly, in step 1023, the set composed of all the approximation values is used as the set of approximation values for this user feature vector. Finally, in step 1024, the above steps are repeated to determine the sets of approximation values for the remaining user feature vectors.

[0023] Among them, in some embodiments, determining the approximation values between this user feature vector and all furniture feature vectors can be implemented by the following steps: Obtain the th furniture feature vector ; Obtain this user feature vector ; According to the th furniture feature vector , this user feature vector , determine the approximation value between the th furniture feature vector and this user feature vector. Among them, the approximation value between the th furniture feature vector and this user feature vector can be implemented by the following formula: Among them, is the approximation value between this furniture feature vector and this user feature vector, represents the vector inner product between the th furniture feature vector and this user feature vector, is the vector length of the th furniture feature vector, is the vector length of this user feature vector.

[0024] It should be noted that the approaching value is a quantitative value used to fit the degree of inclination of the user towards the furniture. The larger the approaching value, the higher the degree of inclination of the user corresponding to the selected user feature vector towards the furniture corresponding to the selected furniture feature vector.

[0025] In addition, it should be noted that there is a one-to-one correspondence between the approaching value set and each user, and there is a one-to-one correspondence between the sorting of each approaching value in the approaching value set and the sorting of each furniture feature vector.

[0026] In some embodiments, in this step, the cooperation degree between two corresponding users determined according to every two approaching value sets can be implemented by the following steps: Obtain the th approaching value in the th approaching value set ; Obtain the th approaching value in the th approaching value set ; Determine the cooperation amplification parameter of the recommended furniture quantity ; According to the th approaching value in the th approaching value set , the th approaching value in the th approaching value set and the cooperation amplification parameter of the recommended furniture quantity , determine the cooperation degree between the user corresponding to the th approaching value set and the user corresponding to the th approaching value set, where the cooperation degree between the user corresponding to the th approaching value set and the user corresponding to the th approaching value set can be implemented by the following formula: Where is the cooperation degree between the user corresponding to the th approaching value set and the user corresponding to the th approaching value set, is the average approaching value of the th approaching value set, is the average approaching value of the th approaching value set, is the quantity of furniture in the shared database of the furniture platform.

[0027] It should be noted that in the present application, the average approximation value is the average of all approximation values in the set of approximation values. The synergy degree is a quantitative value representing the similarity degree between two users. The greater the synergy degree, the greater the similarity degree between the two users corresponding to the synergy degree. The value of the synergy degree ranges from -1 to 1. The similarity degree between the users corresponding to each two sets of approximation values is characterized by all synergy degrees, so that when recommending furniture to a target user, by considering the registration basic data, furniture ordering records, and browsing records of the target user's similar users, the furniture that the target user will order can be found, and personalized sharing recommendations of the target user's potential ordered furniture can be realized.

[0028] In addition, it should be noted that the synergy amplification parameter is used to control the magnitude of the synergy degree, thereby affecting the number of furniture recommended to users. The synergy amplification parameter can be preset according to the number requirement of furniture recommended to users by the furniture platform, and generally takes a constant value in the range of [1, 1.8].

[0029] In step 103, determine the hot start furniture decision domain of the target user according to all synergy degrees.

[0030] In some embodiments, in this step, determining the hot start furniture decision domain of the target user according to all synergy degrees can be implemented by the following steps: Determine the feature vector layer of the target user according to all synergy degrees; Determine the hot start furniture decision domain of the target user through the feature vector layer.

[0031] Specifically, when implementing, determine the feature vector layer of the target user according to all synergy degrees, that is: cluster all user feature vectors according to all synergy degrees, determine the feature vector layers of multiple users, and use the user feature vector layer where the user feature vector corresponding to the target user is located as the feature vector layer of the target user.

[0032] Specifically, when implementing, determine the hot start furniture decision domain of the target user through the feature vector layer, that is: use the users corresponding to all user feature vectors other than the user feature vector corresponding to the target user in the feature vector layer as similar users, and use the furniture set composed of furniture that the target user has not ordered or browsed and the similar users have ordered or browsed as the hot start furniture decision domain of the target user.

[0033] It should be noted that in the process of clustering all user feature vectors according to all synergy degrees to determine multiple user feature vector layers, after using the above synergy degree to characterize the similarity degree between each two users, the K-means clustering algorithm of the existing technology is used to implement the clustering of all user feature vectors. In other embodiments, other methods can also be used to implement the clustering of all user feature vectors, which is not limited here.

[0034] In addition, the similar users described in this application are determined according to the similarity between all users in the shared database of the furniture platform and the target user. The hot-start furniture decision domain represents the set of furniture that will be reserved by the target user among all the furniture in the furniture platform that has not been used by the target user, which will not be elaborated here.

[0035] In step 104, select a piece of furniture from the hot-start furniture decision domain, determine the sharing prediction value of this piece of furniture according to all the collaboration degrees between the target user and other users, determine the adjustment coefficient of the target user for this piece of furniture according to the sharing prediction value and the approaching value set of the target user, and continue to determine the adjustment coefficients of the target user for the remaining furniture in the hot-start furniture decision domain.

[0036] In some embodiments, determining the sharing prediction value of this piece of furniture according to all the collaboration degrees between the target user and other users can be implemented by the following steps: Obtain the approaching value in the th approaching value set corresponding to this piece of furniture ; Obtain the collaboration degree between the target user and the th approaching value set ; Determine the number of sensitive time periods of the furniture platform ; Determine the time-series weighting coefficient of furniture tendency degree ; Determine the furniture sensitivity parameter corresponding to this piece of furniture in the th sensitive time period ; According to the approaching value in the th approaching value set corresponding to this piece of furniture , the collaboration degree between the target user and the th user , the number of sensitive time periods of the furniture platform , the time-series weighting coefficient of furniture tendency degree , the furniture sensitivity parameter corresponding to this piece of furniture in the th sensitive time period , determine the sharing prediction value of the target user for this piece of furniture. Among them, the sharing prediction value of the target user for this piece of furniture can be determined according to the following formula: Where is the sharing prediction value of the target user for this piece of furniture, is the absolute value of the collaboration degree between the target user and the th user, The number of users in the shared database of the furniture platform.

[0037] It should be noted that the shared prediction value described in this application is a quantitative value used to predict the degree of preference of users for furniture. The larger the shared prediction value, the higher the degree of preference of the target user for this type of furniture. By using the shared prediction value to predict the degree of preference of the target user for this type of furniture, and then considering the registration basic data, furniture ordering records, and browsing records of similar users of the target user to find the furniture that the target user will order, realizing the personalized shared recommendation of potential ordered furniture for the target user; the number of sensitive time periods is used to characterize the number of time periods in a year that will affect the degree of preference of the target user for furniture. The more the number of sensitive time periods, the more time periods in a year that will affect the degree of preference of the target user for furniture; the time series weighting coefficient is a value used to balance the degree of preference of the target user for furniture. The time weighting coefficient is a constant with a value range between 0 and 1; the furniture sensitivity parameter is used to characterize the sensitivity of furniture in different time periods. The value of the furniture sensitivity parameter is 0 and 1. When the value of the furniture sensitivity parameter is 1, it means that the sensitivity of this furniture is high during the th sensitive time period. When the value of the furniture sensitivity parameter is 0, it means that the sensitivity of this type of furniture is low during the th sensitive time period. In some embodiments, the number of sensitive time periods can be preset to 4. Then, this application divides a year into 4 sensitive time periods, obtains the sensitive time period corresponding to the current date, and further determines the furniture sensitivity parameter of this type of furniture during the sensitive time period corresponding to the current date.

[0038] In addition, the sensitive time period in this application is the time period in a year that will affect the number of reservations for this furniture; the value of the furniture sensitivity parameter is determined according to the number of reservations of the selected furniture in the sensitive time period corresponding to the current date in the shared database of the furniture platform. The higher the sensitivity of the selected furniture, the higher the degree of preference of the predicted target user for the selected furniture. The lower the sensitivity of the selected furniture, the lower the degree of preference of the predicted target user for the selected furniture.

[0039] In some embodiments, in this step, determining the adjustment coefficient of the target user for this type of furniture according to the shared prediction value and the proximity value set of the target user can be implemented by the following steps: Obtain the proximity value between the target user and this type of furniture in the proximity value set corresponding to the target user ; Obtain the shared prediction value of the target user for this type of furniture ; Determine the positive adjustment factor for the number of recommended furniture ; Obtain the furniture feature vector of this type of furniture ; Obtain the user feature vector of the target user ; According to the proximity value between the target user and this type of furniture , the shared prediction value of this type of furniture by the target user , the positive adjustment factor of the recommended furniture quantity , the furniture feature vector of this type of furniture , the user feature vector of the target user Determine the adjustment coefficient of this type of furniture, where the adjustment coefficient of this type of furniture can be implemented by the following formula: Wherein, is the adjustment coefficient of this type of furniture, is the vector length of the furniture feature vector of this type of furniture, and is the vector length of the user feature vector of the target user.

[0040] It should be noted that the positive adjustment factor described in this application represents a parameter value of the target user's expected degree of the recommended furniture quantity. The larger the positive adjustment factor, the larger the adjustment coefficient of this type of furniture. By controlling the adjustment coefficient corresponding to this type of furniture, the recommended furniture quantity of the target user is further controlled. The positive adjustment factor generally takes a value of a constant between.

[0041] In addition, the adjustment coefficient described in this application is a mapping parameter of the credibility of the shared prediction value, and generally takes a value of a constant between. The larger the adjustment coefficient, the higher the credibility of the shared prediction value corresponding to this adjustment coefficient.

[0042] In step 105, adjust the furniture recommendation information of the target user according to the adjustment coefficient of each type of furniture for the target user, and share the adjusted furniture recommendation information to the shared database of the furniture platform.

[0043] In some embodiments, as shown in Figure 3 , this figure is a schematic flow chart for adjusting the furniture recommendation information of the target user in some embodiments of this application. In this embodiment, adjusting the furniture recommendation information of the target user according to the adjustment coefficient of each type of furniture for the target user can be implemented by the following steps: First, in step 1031, obtain the adjustment coefficient of a type of furniture in the warm start furniture decision domain; Then, in step 1032, determine the furniture recommendation level of this type of furniture according to this adjustment coefficient; Secondly, in step 1033, repeat the above steps to determine the furniture recommendation levels of the remaining furniture in the hot-start furniture decision domain; Finally, in step 1034, adjust the furniture recommendation information of the target user according to the furniture recommendation levels of all the furniture in the hot-start furniture decision domain.

[0044] Specifically, when implementing, determine the furniture recommendation level of this type of furniture according to this adjustment coefficient, that is: divide the furniture recommendation level into strong recommendation level, secondary recommendation level, and weak recommendation level according to the value of the adjustment coefficient, and then determine the furniture recommendation level of this type of furniture. Specifically: when the value of the adjustment coefficient is within classify the furniture corresponding to this adjustment coefficient as the strong recommendation level; when the value of the adjustment coefficient is within classify the furniture corresponding to this adjustment coefficient as the secondary recommendation level; when the value of the adjustment coefficient is within classify the furniture corresponding to this adjustment coefficient as the weak recommendation level.

[0045] Specifically, when implementing, adjust the furniture recommendation information of the target user according to the furniture recommendation levels of all the furniture in the hot-start furniture decision domain, that is: adjust the recommended page ratio and recommended time of each furniture on the recommended page of the furniture platform interface of the target user according to the furniture recommendation levels of all the furniture in the hot-start furniture decision domain. Specifically: adjust the recommended page ratio and recommended time of the furniture at the strong recommendation level to the optimal on the furniture platform interface of the target user; adjust the recommended page ratio and recommended time of the furniture at the secondary recommendation level to the sub-optimal; adjust the recommended page ratio and recommended time of the furniture at the weak recommendation level to the worst. In some embodiments, the specific sizes of the recommended page ratio and recommended time of each furniture recommendation level can be set according to the usage habits of platform users, which will not be elaborated here.

[0046] It should be noted that in the process of sharing the adjusted furniture recommendation information to the shared database of the furniture platform, first provide a set of user information interfaces, and realize the request and data acquisition between users through the user information interfaces. The user information interfaces are information transmission channels that define transmission functions, transmission methods, transmission protocols, and transmission tools. In some embodiments, the user information interfaces can be obtained through the application programming interface technology in the prior art, which is not limited here.

[0047] In addition, on the other hand of the present application, in some embodiments, the present application provides a user data sharing system for a furniture platform. Refer to Figure 4, This figure is a schematic diagram of the module composition of a user data sharing system according to some embodiments of the present application. The user data sharing system 200 includes: an acquisition module 201, a processing module 202, and an adjustment module 203, which are described as follows: The acquisition module 201. In the present application, the acquisition module 201 is mainly used to acquire shared data in a specified furniture platform, and determine the furniture feature vector of each piece of furniture and the user feature vector of each user in the shared data; The processing module 202. In the present application, the processing module 202 is mainly used to determine the approximation value set of each user feature vector according to all the furniture feature vectors, and determine the cooperation degree between two corresponding users according to every two approximation value sets; It should be noted that in the present application, the processing module 202 is also used to determine the hot start furniture decision domain of the target user according to all the cooperation degrees; In addition, in the present application, the processing module 202 is also used to select a piece of furniture in the hot start furniture decision domain, determine the sharing prediction value of this piece of furniture according to all the cooperation degrees between the target user and other users, determine the adjustment coefficient of the target user for this piece of furniture according to the sharing prediction value and the approximation value set of the target user, and continue to determine the adjustment coefficients of the target user for the remaining furniture in the hot start furniture decision domain; The sharing module 203. In the present application, the sharing module 203 is mainly used to adjust the furniture recommendation information of the target user according to the adjustment coefficient of the target user for each piece of furniture, and share the adjusted furniture recommendation information to the shared database of the furniture platform.

[0048] In addition, the present application also provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to obtain the code and execute the above-mentioned user data sharing method for a furniture platform.

[0049] In some embodiments, refer to Figure 5 , This figure is a schematic diagram of the structure of a computer device applying the user data sharing method for a furniture platform according to some embodiments of the present application. The user data sharing method for a furniture platform in the above embodiments can be implemented by Figure 5 the computer device shown. The computer device 300 includes at least one processor 301, a communication bus 302, a memory 303, and at least one communication interface 304.

[0050] The processor 301 may be a general-purpose central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more for controlling the execution of the user data sharing method for the furniture platform in this application.

[0051] The communication bus 302 may include a path for transmitting information between the above components.

[0052] The memory 303 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or may also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 303 may exist independently and be connected to the processor 301 through the communication bus 302. The memory 303 may also be integrated with the processor 301.

[0053] Among them, the memory 303 is used to store the program code for executing the solution of this application and is controlled by the processor 301 for execution. The processor 301 is used to execute the program code stored in the memory 303. The program code may include one or more software modules. The user data sharing method for the furniture platform in the above embodiments may be implemented by one or more software modules in the program code of the processor 301 and the memory 303.

[0054] The communication interface 304, using any device such as a transceiver, is used to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0055] In a specific implementation, as an example, a computer device may include multiple processors, and each of these processors may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. The processors here may refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0056] The above computer device may be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device may be a desktop computer, a laptop computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. The embodiments of the present application do not limit the type of the computer device.

[0057] In addition, the present application also provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the above-mentioned user data sharing method for a furniture platform is implemented.

[0058] In summary, in the user data sharing method for a furniture platform disclosed in the embodiments of the present application, first, by obtaining the shared data in a specified furniture platform, the furniture feature vector of each piece of furniture and the user feature vector of each user in the shared data are determined. According to all the furniture feature vectors, the approaching value set of each user feature vector is determined. According to every two approaching value sets, the cooperation degree between the corresponding two users is determined. According to all the cooperation degrees, the hot-start furniture decision domain of the target user is determined. The shared prediction value of each piece of furniture in the hot-start furniture decision domain is determined, and then the adjustment coefficient of the target user for each piece of furniture is determined. The furniture recommendation information of the target user is adjusted according to the adjustment coefficient of the target user for each piece of furniture, and the adjusted furniture recommendation information is shared to the shared database of the furniture platform, so as to realize personalized shared recommendation for the potential ordered furniture of the target user.

[0059] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present application.

[0060] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these changes and modifications.

Claims

1. A user data sharing method for a furniture platform, characterized in that: The steps include: Acquire shared data in a designated furniture platform, and determine a furniture feature vector of each piece of furniture and a user feature vector of each user in the shared data; Determine the approximate value set of each user's feature vector based on all furniture feature vectors, and then determine the degree of coordination between two corresponding users based on every two approximate value sets; Determine the hot start furniture decision domain of the target user based on all the synergies; A kind of furniture in the hot start furniture decision domain is selected, and a shared prediction value of the furniture is determined according to all the synergies between the target user and other users, and an adjustment coefficient of the target user for the furniture is determined according to the shared prediction value and the approach value set of the target user, and the adjustment coefficient of the target user for the remaining furniture in the hot start furniture decision domain is further determined; The furniture recommendation information for the target user is adjusted according to the adjustment coefficient of the target user for each piece of furniture, and the adjusted furniture recommendation information is shared to the shared database of the furniture platform.

2. The method according to claim 1, characterized in that The approximate value set of each user feature vector is determined based on all furniture feature vectors, specifically including: Get a user feature vector; Determine the approximation value between the user feature vector and all furniture feature vectors; The set of all approximate values ​​is taken as the approximate value set of the user feature vector; Repeat the above steps to determine the approximate value set of the remaining user feature vectors.

3. The method according to claim 1, characterized in that The hot start furniture decision domain for the target user is determined based on all the synergies, including: Determine the feature vector layer of the target user based on all the synergies; The hot start furniture decision domain of the target user is determined through the feature vector layer.

4. The method according to claim 1, characterized in that Adjusting the furniture recommendation information for the target user according to the adjustment coefficient of each type of furniture for the target user specifically includes: Obtaining an adjustment coefficient of a type of furniture in the hot start furniture decision domain; Determine the recommended furniture grade of the furniture according to the adjustment coefficient; Repeat the above steps to determine the furniture recommendation levels of the remaining furniture in the hot start furniture decision domain; The furniture recommendation information for the target user is adjusted according to the furniture recommendation levels of all furniture in the hot start furniture decision domain.

5. The method according to claim 4, characterized in that The recommended furniture grade for this type of furniture is determined based on the adjustment coefficient, including: The furniture recommendation level is divided into a strong recommendation level, a secondary recommendation level, and a weak recommendation level according to the value of the adjustment coefficient, thereby determining the furniture recommendation level of the furniture.

6. The method according to claim 1, characterized in that The basic characteristics of each archived furniture and the basic characteristics of each registered user in the shared database of the furniture platform are used as shared data in the furniture platform.

7. The method according to claim 1, characterized in that A feature vector composed of basic features of a piece of furniture in a designated furniture platform is used as the furniture feature vector.

8. A user data sharing system for a furniture platform, characterized in that: include: An acquisition module, used for acquiring shared data in a designated furniture platform, and determining a furniture feature vector of each piece of furniture and a user feature vector of each user in the shared data; A processing module, used to determine a convergence value set of each user feature vector according to all furniture feature vectors, and determine the degree of coordination between two corresponding users according to every two convergence value sets; The processing module is further used to determine the hot start furniture decision domain of the target user according to all the coordination degrees; The processing module is further used to select a type of furniture in the hot start furniture decision domain, determine the shared prediction value of the type of furniture according to all the synergies between the target user and other users, determine the adjustment coefficient of the target user for the type of furniture according to the shared prediction value and the approach value set of the target user, and continue to determine the adjustment coefficient of the target user for the remaining furniture in the hot start furniture decision domain; The sharing module is used to adjust the furniture recommendation information of the target user according to the adjustment coefficient of the target user for each furniture, and share the adjusted furniture recommendation information to the shared database of the furniture platform.

9. A computer device, comprising a memory and a processor, wherein the memory stores a code, characterized in that: The processor is configured to obtain the code and execute the user data sharing method for a furniture platform as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the user data sharing method for a furniture platform as described in any one of claims 1 to 7 is implemented.

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