Data processing method and device and computer program product

Abstract

The invention provides a data processing method and device, electronic equipment, a storage medium and a computer program product, relates to the technical field of artificial intelligence, in particular to the technical fields of large models, image processing, big data and the like, and can be applied to an object recommendation scene. According to the specific implementation scheme, similar users of a target user are determined according to portrait data of the target user; determining a target recommendation object interested by the target user from the published data of the similar users according to the portrait data; and fusing the target recommendation object with the user image of the target user to obtain first fused data matching the target recommendation object with the target user. The target recommendation object interested by the target user is quickly and accurately determined based on the published data of the similar users, and the experience substitution feeling of the user is increased.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, specifically to the fields of large models, image processing, big data, etc. In particular, it relates to a data processing method, apparatus, electronic device, storage medium, and computer program product, which can be applied to the object recommendation scenario. Background Art

[0002] Data recommendation is a technology that analyzes user data and provides personalized recommendation services for users. Under the current data recommendation technology, the user experience immersion is poor and the satisfaction is low. Summary of the Invention

[0003] The present disclosure provides a data processing method, apparatus, electronic device, storage medium, and computer program product.

[0004] According to a first aspect, a data processing method is provided, including: determining similar users of a target user according to the portrait data of the target user; determining target recommendation objects that the target user is interested in from the published data of the similar users according to the portrait data; fusing the target recommendation objects with the user image of the target user, and obtaining and displaying through a preset terminal first fused data that adapts the target recommendation objects to the target user.

[0005] According to a second aspect, a data processing apparatus is provided, including: a user determination unit configured to determine similar users of a target user according to the portrait data of the target user; an object determination unit configured to determine target recommendation objects that the target user is interested in from the published data of the similar users according to the portrait data; a fusion unit configured to fuse the target recommendation objects with the user image of the target user, and obtaining and displaying through a preset terminal first fused data that adapts the target recommendation objects to the target user.

[0006] According to a third aspect, an electronic device is provided, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in any implementation manner of the first aspect.

[0007] According to a fourth aspect, a non-transitory computer-readable storage medium storing computer instructions is provided, and the computer instructions are used to cause a computer to execute the method described in any implementation manner of the first aspect.

[0008] According to a fifth aspect, a computer program product is provided, including: a computer program that, when executed by a processor, implements the method described in any implementation manner of the first aspect.

[0009] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood from the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The drawings are used to better understand the solution and do not constitute a limitation to the present disclosure. Among them:

[0011] Figure 1 is an exemplary system architecture diagram to which an embodiment of the present disclosure can be applied;

[0012] Figure 2 is a flowchart of an embodiment of the data processing method according to the present disclosure;

[0013] Figure 3 Schematic diagram of the first fused data according to this embodiment;

[0014] Figure 4 is a schematic diagram of the application scenario of the data processing method according to this embodiment;

[0015] Figure 5 Schematic diagram of the second fused data according to this embodiment;

[0016] Figure 6 is a flowchart of another embodiment of the data processing method according to the present disclosure;

[0017] Figure 7 is a structural diagram of an embodiment of the data processing apparatus according to the present disclosure;

[0018] Figure 8 is a schematic structural diagram of a computer system suitable for implementing the embodiments of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] The following describes exemplary embodiments of the present disclosure with reference to the drawings. Various details of the embodiments of the present disclosure are included to facilitate understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0020] In the technical solution of the present disclosure, the processing of collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0021] Figure 1 Illustrates an exemplary architecture 100 to which the data processing method and apparatus of the present disclosure can be applied.

[0022] As Figure 1 shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The terminal devices 101, 102, 103 are communicatively connected to form a topology network, and the network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0023] The terminal devices 101, 102, 103 may be hardware devices or software that support network connections for data interaction and data processing. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices that support network connections, information acquisition, interaction, display, processing, etc., including but not limited to smartphones, tablets, e-book readers, laptop computers, and desktop computers, etc. When the terminal devices 101, 102, 103 are software, they may be installed in the above-listed electronic devices. It may be implemented as, for example, multiple software or software modules for providing distributed services, or may also be implemented as a single software or software module. No specific limitation is made here.

[0024] The server 105 may be a server that provides various services. For example, based on the portrait data of the target user obtained from the terminal devices 101, 102, 103, similar users are determined, and target recommended objects of interest to the target user are determined from the published data of the similar users, so as to fuse the target recommended objects with the user image in the background processing server. As an example, the server 105 may be a cloud server.

[0025] It should be noted that the server may be hardware or software. When the server is hardware, it may be implemented as a distributed server cluster composed of multiple servers, or may also be implemented as a single server. When the server is software, it may be implemented as multiple software or software modules (such as software or software modules for providing distributed services), or may also be implemented as a single software or software module. No specific limitation is made here.

[0026] It should also be noted that the data processing method provided by the embodiments of the present disclosure is generally executed by the server, but it does not exclude the possibility of being executed by the terminal device, or being executed by the server and the terminal device in cooperation with each other. Correspondingly, each part (such as each unit) included in the data determination device may be all set in the server, or may be all set in the terminal device, or may also be respectively set in the server and the terminal device.

[0027] It should be understood, Figure 1The numbers of the terminal devices, networks, and servers therein are merely illustrative. According to the implementation requirements, there can be any number of terminal devices, networks, and servers. When the electronic device on which the data determination method runs does not need to perform data transmission with other electronic devices, the system architecture may only include the electronic device (such as a terminal device or a server) on which the data determination method runs.

[0028] Please refer to Figure 2 , Figure 2 which is a flowchart of a data processing method provided by an embodiment of the present disclosure. Among them, in process 200, the following steps are included:

[0029] Step 201, determine similar users of the target user according to the portrait data of the target user.

[0030] In this embodiment, the execution subject of the data processing method (for example, Figure 1 the server therein) can determine similar users of the target user according to the portrait data of the target user.

[0031] The portrait data of a user refers to the comprehensive description of the user's characteristics formed by collecting, sorting, and analyzing the user's multi-dimensional information, so as to better understand the user's needs, interests, and behavior habits, and thus provide an object recommendation service that better meets the user's expectations. As an example, the user portrait data mainly includes the following main aspects:

[0032] 1. Basic information

[0033] including but not limited to demographic characteristics such as age, gender, geographical location (such as city, province), occupation, education level, etc., and family status data such as marital status, children's situation, etc.

[0034] 2. Behavioral data

[0035] including but not limited to browsing behaviors such as the user's browsing records, accessed pages, and stay time on platforms such as websites and applications; purchase behaviors such as purchase history, shopping frequency, and purchase amount; search behaviors such as search keywords and frequencies of the user on search engines or platforms; interaction behaviors such as posts, comments, and likes on social media.

[0036] 3. Consumption data

[0037] including but not limited to consumption habits such as preferences for online purchases, offline purchases, or mixed purchases, and loyalty to different brands and products; the user's purchasing power level, including overall consumption ability and psychological consumption level in specific categories (such as clothing, electronic products, etc.); consumption psychology such as the user's purchase motivation and price sensitivity.

[0038] 4. Social data

[0039] including but not limited to social relationships such as the user's friends, followers, and followed objects on the social network; the user's interactions on the social media, such as the discussions participated in and the content shared.

[0040] 5. Device data

[0041] including but not limited to the device types such as the models and operating systems of the devices used by the user, such as mobile phones and computers; the application programs installed and used by the user on the devices.

[0042] 6. Psychological data

[0043] including but not limited to emotional tendencies such as the emotional attitudes expressed by the user on the social media and in comments; the user's satisfaction with the product or service; value data such as the user's beliefs and social positions.

[0044] 7. Requirement data

[0045] The expectations and requirements of the user for the product or service, including explicit requirements and potential requirements.

[0046] It should be noted that the portrait data of each dimension of the user is collected under the authorization of the user. For example, when there is a need to collect portrait data of the target dimension of the user, a display interface for instructing the user to authorize is displayed to the user through the preset terminal of the user, and the data to be collected is clearly displayed in the display interface, so as to collect data when the user agrees, that is, under the authorization of the user.

[0047] As an example, the above-mentioned execution entity can extract features from the portrait data of the target user to obtain the portrait features of the target user; calculate the similarity between the portrait features of the target user and the portrait features of other users in the portrait feature set; and determine similar users of the target user from other users according to the similarity. The similar users can be either users with actual association relationships such as friendship relationships with the target user, or users without actual association relationships with the target user. Generally, multiple similar users are determined.

[0048] As another example, the above-mentioned execution entity can determine partial portrait data of the target dimension from the portrait data of the target user according to the recommendation scenario; extract features from the partial portrait data of the target user to obtain partial portrait features of the target user; calculate the similarity between the portrait features of the target user and the portrait features of other users in the portrait feature set; and determine similar users of the target user from other users according to the similarity. In the above-mentioned execution entity or the electronic device communicatively connected to the above-mentioned execution entity, a relationship table representing the corresponding relationship between the recommendation scenario and the target dimension can be set, or a data analysis model capable of determining partial portrait data of the target dimension required by the recommendation scenario based on its own data analysis ability can be set.

[0049] Step 202: Determine target recommended objects that the target user is interested in from the published data of similar users according to the portrait data.

[0050] In this embodiment, the above-mentioned execution entity can determine target recommended objects that the target user is interested in from the published data of similar users according to the portrait data.

[0051] The published data of similar users can be spatial dynamic data, note data, and Moments data published by similar users on various application programs. The published data can be data in multiple modalities such as text, audio, and video, and may involve various objects such as food, items, scenic spots, animals, etc. For example, photos and evaluations of the user's own food tasting, travel photos, travelogues, and travel guides shared by the user.

[0052] The published data of similar users is collected under the authorization of the users. For example, when there is a need to collect the published data of similar users, a display interface for indicating user authorization is presented to the similar users through the preset terminals of the similar users. The data to be collected is clearly displayed in the display interface for data collection when the similar users consent, that is, under the authorization of the similar users.

[0053] As an example, the above-mentioned execution entity can analyze various objects in the published data of similar users; according to the portrait data, such as the user interest data in the portrait data, determine target recommended objects that the target user is interested in from the published data of similar users. The target recommended objects can be various types of objects. For example, for each object type among multiple object types that the user is interested in, determine target recommended objects of this object type that the target user is interested in from the published data of similar users.

[0054] As another example, before determining the target recommended objects corresponding to the target user, it is possible to determine the target object type expected by the target user based on the interaction with the target user; according to the portrait data, such as the user interest data in the portrait data, determine target recommended objects of the target object type that the target user is interested in from the published data of similar users.

[0055] Taking the object type as clothes as an example, after the above-mentioned execution entity determines similar users who are similar in body type and clothing style to the target user, it can determine multiple clothing objects displayed by the similar users from the published data of the similar users; according to the portrait data of the target user, determine target clothing objects that the target user is interested in from the multiple clothing objects corresponding to the similar users.

[0056] Taking the object type of scenic spots as an example, after the above-mentioned execution entity determines similar users who have the same travel hobbies as the target user, it can determine multiple scenic spot objects visited by the similar users from the published data of the similar users; according to the portrait data of the target user, it can determine the target scenic spot objects that the target user is interested in from the multiple scenic spot objects corresponding to the similar users.

[0057] Taking the object type of food as an example, after the above-mentioned execution entity determines similar users whose food taste styles are similar to those of the target user, it can determine multiple food objects tasted by the similar users from the published data of the similar users; according to the portrait data of the target user, it can determine the target food objects that the target user is interested in from the multiple food objects corresponding to the similar users.

[0058] In some optional implementation manners of this embodiment, the above-mentioned execution entity may execute the above step 202 in the following manner:

[0059] The first step is to determine candidate recommended objects that the target user is interested in from the published data of the similar users according to the portrait data.

[0060] As an example, the above-mentioned execution entity may determine all types of recommended objects that the target user is interested in from the published data of the similar users according to the portrait data of the target user, and use all the recommended objects as candidate recommended objects.

[0061] The second step is to determine the target recommended objects associated with the target location corresponding to the target user from the candidate recommended objects.

[0062] The target location corresponding to the target user is a location that has a certain association with the target user. The target location can be either the location where the target user is currently located or the search location in a map application.

[0063] The target recommended objects associated with the target location refer to objects that have a certain association with the target location. For example, the published location of the published data to which the target recommended object belongs is the target location or within the preset range corresponding to the target location; or, the object carrier of the target recommended object is located at the target location or within the preset range corresponding to the target location.

[0064] Taking the target recommended object as clothes as an example, the clothing store that sells this type of clothes is located near the target location corresponding to the target user.

[0065] Taking the target recommended object as a scenic spot as an example, the scenic area to which the scenic spot belongs is located near the target location corresponding to the target user.

[0066] In this implementation manner, based on determining candidate recommended objects of interest to the target user from the published data of similar users, the target recommended objects are further screened from them based on the target location corresponding to the user, so as to accurately match the user's needs, enhance the shopping convenience of the user, improve the satisfaction of the user with the target recommended objects, and help improve the interactivity between the target user and the target recommended objects associated with the target location.

[0067] Step 203: Integrate the user images of the target recommended object and the target user to obtain and display, through a preset terminal, the first integrated data in which the target recommended object is adapted to the target user.

[0068] In this embodiment, the above-mentioned execution entity may integrate the user images of the target recommended object and the target user to obtain the first integrated data in which the target recommended object is adapted to the target user.

[0069] The user image of the target user may be any user image set in advance or the user image taken by the target user in real time.

[0070] As an example, first, the above-mentioned execution entity uses image segmentation technology to separately extract the target recommended object and the target user object in the user image. It can be implemented using semantic segmentation or instance segmentation models in deep learning frameworks (such as PyTorch, TensorFlow). For example, use the Mask R-CNN (Mask Region-based Convolutional Neural Network) model for instance segmentation to separately extract the regions of the target recommended object and the target user object. Then, ensure that the target recommended object and the target user object are aligned in size and perspective. If not aligned, image registration techniques such as feature matching and perspective transformation are required to adjust the target recommended object to the same perspective and size as the target user object. Then, integrate the target recommended object and the target user object. Specifically, adjust the color and light of the target recommended object to match the hue and light effect of the target user object. This can be achieved through techniques such as histogram matching and color correction. Furthermore, select a suitable image fusion algorithm to fuse the target recommended object and the target user object. For the fusion of such specific objects, methods such as weighted average method, multi-resolution pyramid fusion, and wavelet transform fusion can be used. Finally, optimize the details of the integrated data, such as edge smoothing and defect removal, to improve the quality and naturalness of the integrated data. Image restoration techniques or deep learning models can be used for further processing.

[0071] As another example, a large model with image, audio, and video processing capabilities can fuse the user images of the target recommendation object and the target user to obtain the first fused data that adapts the target recommendation object to the target user.

[0072] The first fused data is generally in the form of image modality or video modality. The data type of the first fused data is generally consistent with the data type of the published data to which the target recommendation object belongs. For example, when the published data is an image, the first fused data is also an image. To further improve the user's immersive experience, of course, the first fused data in video modality can also be generated based on the first fused data in image modality.

[0073] In some cases, the first fused data can use the background of the published data to which the target recommendation object belongs as the background, and fuse the user images of the target recommendation object and the target user to obtain the first fused data.

[0074] The posture of the target user object in the user image can be the same as the posture of the similar user in the published data. At this time, the presentation effect of the obtained first fused data is to replace the similar user in the published data with the target user. Taking clothes as an example, the presentation effect of the first fused data is that the target user is wearing the clothes of the similar user; taking a scenic spot as an example, the presentation effect of the first fused data is that the target user is in the scenic spot that the similar user has visited.

[0075] Of course, the posture of the target user object in the user image can be different from the posture of the similar user in the published data. At this time, the presentation effect of the obtained first fused data is to replace the similar user in the published data with the target user, and further adjust the layout and interaction relationship between the objects in the first fused data based on the posture of the target user, so that the first fused data presents a real, harmonious, and coordinated effect. Continuing to refer to Figure 3 , a schematic diagram of the first fused data is shown. The first fused data is a fused image that combines the target user 301 and the target recommendation object 302.

[0076] Continuing to refer to Figure 4 , Figure 4 is a schematic diagram of an application scenario 400 of the data processing method according to this embodiment. First, the server 401 determines the similar user 403 of the target user according to the portrait data of the target user 402; according to the portrait data, determines the target recommendation object 404 that the target user is interested in from the published data of the similar user; fuses the user images of the target recommendation object and the target user to obtain the first fused data 405 that adapts the target recommendation object to the target user.

[0077] In this embodiment, a data processing method is provided. According to the portrait data of the target user, similar users of the target user are determined; according to the portrait data, target recommended objects that the target user is interested in are determined from the published data of the similar users, and the target recommended objects that the target user is interested in can be quickly and accurately determined based on the published data of the similar users; the target recommended objects and the user image of the target user are fused to obtain first fused data in which the target recommended objects are adapted to the target user, enabling the user to view the target recommended objects immersively, increasing the sense of experience substitution, and helping to improve the exposure rate and order volume of the target recommended objects.

[0078] In some alternative implementation manners of this embodiment, the above-mentioned execution entity may further perform the following operations: Determine a first target carrier to which the target recommended object belongs from the object carriers associated with the target location.

[0079] The object carriers associated with the target location refer to the carriers of the target recommended objects that have a certain association relationship with the target location. For example, the object carrier of a clothing object is a clothing store, and the object carrier of a scenic spot object is a scenic area.

[0080] As an example, each object carrier associated with the target location is correspondingly provided with identification information of the recommendable objects included therein; according to the identification information of the target recommended object, the first target carrier including the target recommended object can be determined.

[0081] Taking the target recommended object as clothing as an example, the above-mentioned execution entity can determine a clothing store near the target location corresponding to the target user that sells the above-mentioned clothing; taking the target recommended object as cosmetics (such as lipsticks) as an example, the above-mentioned execution entity can determine a cosmetics store near the target location corresponding to the target user that sells the above-mentioned cosmetics.

[0082] After the target carrier is determined, relevant information of the target carrier, such as location information, name information, and distance information from the target user, can be displayed to the target user through a preset terminal.

[0083] In this implementation manner, by determining the first target carrier to which the target recommended object belongs from the object carriers associated with the target location, the user can learn the first target carrier including the target recommended object, and thus the user can obtain the target recommended object more conveniently, further improving the purchase convenience of the user.

[0084] In some alternative implementation manners of this embodiment, the above-mentioned execution entity may further perform the following operations:

[0085] First step, display the recommendable objects in the first target carrier through a preset terminal.

[0086] Objects carried in the first target carrier can all be recommended objects in the first target carrier. The above-mentioned execution entity can display the recommended objects in the first target carrier based on display forms such as images and videos.

[0087] The second step is to determine the selected object from the recommended objects according to the first selection operation of the target user.

[0088] The target user can browse the recommended objects one by one based on a preset terminal in a way such as swiping; and during the browsing process, based on first selection operations such as clicking and touching, determine the selected object from the recommended objects.

[0089] Third, fuse the selected object and the user image to obtain the second fused data that adapts the selected object to the target user.

[0090] In this implementation manner, the above-mentioned execution entity can fuse the selected object and the user image with reference to the fusion method in step 203 above to obtain the second fused data that adapts the selected object to the target user, and display the second fused data to the target user through a preset terminal.

[0091] In this implementation manner, a recommendation method combining online browsing and data fusion is provided, enabling the target user to conveniently browse the recommended objects in the first target carrier and immersively view the adaptability of the selected object to themselves based on data fusion, further improving the user's information acquisition efficiency and experience.

[0092] In some alternative implementation manners of this embodiment, the selected objects determined by the user based on the first selection operation can be multiple. In this implementation manner, the above-mentioned execution entity can execute the above third step (the process of obtaining the second fused data) in the following manner:

[0093] First, for each selected object, fuse the selected object and the user image to obtain the second fused data that adapts the selected object to the target user; then, display the multiple second fused data corresponding to the multiple selected objects through a preset terminal.

[0094] First, for each selected object, the above-mentioned execution entity can fuse the selected object and the user image with reference to the fusion method shown in step 203 above to obtain the second fused data that adapts the selected object to the target user, thereby obtaining multiple second fused data corresponding to the multiple selected objects. Then, display the multiple second fused data corresponding to the multiple selected objects through a preset terminal using a preset display method.

[0095] The preset display method is, for example, a multi-grid display method, a picture gallery display method, or a picture switching display method.

[0096] In this implementation manner, by obtaining and displaying multiple pieces of second fused data corresponding to multiple selected objects one by one, it is possible to enable the user to intuitively compare the compatibility between different selected objects in the multiple pieces of second fused data and the user himself / herself, helping the target user to conveniently determine the selected object suitable for himself / herself and improving the information processing efficiency of the target user.

[0097] In some optional implementation manners of this embodiment, the above-mentioned execution entity may execute the above-mentioned first step (the display operation of recommended objects) in the following manner:

[0098] First, display the first panoramic image of the first target carrier through a preset terminal. Among them, the first panoramic image includes the recommended objects in the first target carrier.

[0099] The first panoramic image can specifically be a true display in the form of the first target carrier image. To improve the browsing efficiency and experience of the user, guiding information may be displayed in the display interface corresponding to the first panoramic image, and the guiding information is used to indicate the position information of different types of recommended objects in the first panoramic image.

[0100] Taking the first panoramic image of a clothing store as an example, based on the perspective of the target user in the first panoramic image, guiding arrows corresponding to clothes of different styles or models are displayed in the display interface to instruct the target user to slide according to the arrows to be able to browse the clothes of the corresponding style or model.

[0101] Then, according to the second selection operation on the target recommended object among the recommended objects, display the three-dimensional model data of the target recommended object through a preset terminal.

[0102] During the browsing process based on the first panoramic image, the target user may have a need to view a certain recommended object in detail. At this time, the target user can determine the target recommended object through a preset terminal based on second selection operations such as clicking and touching, and then the above-mentioned execution entity can display the three-dimensional model data of the target recommended object through the preset terminal.

[0103] The three-dimensional model data of each recommended object can be stored in the above-mentioned execution entity or an electronic device communicatively connected to the above-mentioned execution entity.

[0104] During the process of the preset terminal displaying the three-dimensional model data of the target recommended object, the user can interact with the three-dimensional model data based on interaction actions such as clicking, touching, and sliding to zoom in or out the three-dimensional model or display the three-dimensional model from different angles.

[0105] In this implementation manner, during the display process of the first panoramic image, the three-dimensional model data of the target recommended object selected by the target user can be further displayed to meet the detailed viewing needs of the target user for the target recommended object.

[0106] In some alternative implementation manners of this embodiment, the above-mentioned execution entity may further perform the following operations:

[0107] In the first step, through a large model, according to the received editing operation, edit the target recommended object in the first fused data to obtain an edited object.

[0108] The target user can implement the editing operation on the target recommended object based on various methods. For example, the target user can input a requirement text expressing the editing requirement to a preset terminal, and the large language model can perform natural language understanding on the requirement text, determine the editing requirement of the target user, and edit the target recommended object according to the editing requirement. The requirement text is, for example, "Add a cartoon pattern to the front chest part of the clothes".

[0109] For another example, the above-mentioned execution entity can provide a painting editing function for the user for the target editing object based on the large model, and the target user can perform painting on the basis of the target recommended object displayed on the preset terminal to edit the target recommended object.

[0110] As another example, the above-mentioned execution entity can provide the user with a variety of preset editing operations based on the large model, including but not limited to scaling, attribute adjustment, replacement, etc. The user can select the target editing operation to implement the editing of the target recommended object.

[0111] In the second step, determine a second target carrier including the edited object or a similar object of the edited object.

[0112] The edited object is generally an object that the user is more satisfied with on the basis of the target recommended object. The above-mentioned execution entity can extract the object feature data of the edited object, and then calculate the similarity between the object feature data of the edited object and the object feature data of the recommendable objects in each object carrier; thereby determining a second target carrier including the edited object or a similar object of the edited object.

[0113] In the third step, display a second panoramic image of the second target carrier through a preset terminal. The second panoramic image includes the recommendable objects in the second target carrier.

[0114] The second panoramic image can specifically be a true display in the form of a second target carrier image. In order to improve the browsing efficiency and experience of the user, guiding information can be displayed in the display interface corresponding to the second panoramic image, and the guiding information is used to indicate the position information of different types of recommendable objects in the second panoramic image. The second target carrier can be either an online virtual object carrier (such as an online store) or an offline physical object carrier.

[0115] In this implementation manner, the object editing requirements of the user can be implemented based on a large model, and on this basis, the second target carrier including the edited object can be searched for and displayed, which can quickly help the user find the desired edited object and further improve the user's information acquisition efficiency.

[0116] In some optional implementation manners of this embodiment, the above-mentioned execution subject may execute the above-mentioned first step in the following manner:

[0117] First, identify and display, through a preset terminal, multiple components in the target recommended object.

[0118] As an example, the above-mentioned execution subject may identify multiple components in the target recommended object based on image recognition and segmentation technologies and display the multiple components through a preset terminal.

[0119] Then, through the large model, according to the received editing operation, edit the target component among the multiple components to obtain an edited object.

[0120] The target user can specifically issue an editing operation on the target component among the multiple components, and the implementation manner of its editing operation can be implemented with reference to the implementation manner in the above-mentioned embodiment and will not be elaborated here.

[0121] In this implementation manner, the user can specifically perform an editing operation on the target component in the target recommended object, which improves the convenience and pertinence of the user's editing process and helps improve the user's editing efficiency.

[0122] In some optional implementation manners of this embodiment, the above-mentioned execution subject may execute the above-mentioned second step in the following manner: Determine a second target carrier including the edited object or a similar object of the edited object from the object carriers at the target position associated with the target user.

[0123] The object carrier associated with the target position refers to the carrier of the edited object or a similar object of the edited object that has a certain association relationship with the target position.

[0124] As an example, each object carrier associated with the target position is correspondingly provided with characteristic data of the recommendable object included; according to the characteristic data of the edited object, a second target carrier including the edited object or a similar object of the edited object can be determined.

[0125] After determining the target carrier, relevant information of the second target carrier, such as location information, name information, and distance information from the target user, can be displayed to the target user through a preset terminal.

[0126] In this implementation manner, by determining, from the object carriers associated with the target position, the second target carrier to which the edited object or the similar object of the edited object belongs, the user can be informed of the second target carrier including the edited object or the similar object of the edited object, so that the user can more conveniently obtain the edited object or the similar object of the edited object, further improving the shopping convenience and experience of the user.

[0127] In some optional implementation manners of this embodiment, the above execution entity may further perform the following operations:

[0128] The first step is to determine a target matching object that matches the target user in the fused data according to the fused data and the portrait data.

[0129] Among them, the fused data is the first fused data or the second fused data.

[0130] The second step is to fuse the target matching object and the fused data to obtain the third fused data that adapts the target matching object to the target user in the fused data.

[0131] For the first fused data, the above execution entity may determine a target matching object that matches the target user and the target recommended object according to the first fused data and the portrait data; fuse the target recommended object, the user image, and the target matching object to obtain the third fused data that adapts the target recommended object, the target matching object to the target user. The first fused data may be either the original target recommended object or the edited object after editing.

[0132] For the second fused data, the above execution entity may determine a target matching object that matches the target user and the selected object according to the second fused data and the portrait data; fuse the selected object, the user image, and the target matching object to obtain the third fused data that adapts the selected object, the target matching object to the target user.

[0133] Since the object types of the target recommended object, the edited object, or the selected object are different, the object types of their corresponding matching objects are different. A relationship table representing the corresponding relationship between the object types of the target recommended object, the edited object, or the selected object and the object types of the matching objects may be set in the above execution entity or an electronic device communicatively connected to the above execution entity to determine the matching objects corresponding to the target recommended object, the edited object, or the selected object.

[0134] Taking the target recommended object or selected object as clothes as an example, the corresponding types of matching objects are, for example, shoes, accessories, the user's hairstyle, cosmetics, etc. For each type of matching object corresponding to the clothes type, the above-mentioned execution entity can determine the target matching object that matches the merged effect under this type based on the merged effect of the target recommended object or selected object and the target user.

[0135] Taking the target recommended object or selected object as a scenic spot as an example, the corresponding type of matching object is, for example, clothing suitable for the scenic spot. Continuing to refer to Figure 5 , a schematic diagram of the third merged data is shown. The target recommended object of the target user 501 is a scenic spot in the ancient building style, and the determined matching object is the ancient costume 502 suitable for the ancient building, specifically the costume of an ancient princess.

[0136] In this implementation manner, based on the first merged data or the second merged data, it is possible to further recommend suitable matching objects to the target user, improving the comprehensiveness of the recommended data, and enabling the user to immerse in viewing the matching objects through the third merged data, improving the user experience and information processing efficiency.

[0137] In some alternative implementation manners of this embodiment, the above-mentioned execution entity can execute the above-mentioned first step (the process of determining the target matching object) in the following manner:

[0138] First, according to the merged data and the portrait data, determine multiple candidate matching objects that match the target user in the merged data; then, according to the received third selection operation, determine the target matching object from the multiple candidate matching objects.

[0139] As an example, for each type of matching object corresponding to the object type of the target recommended object, the edited object or the selected object, through a large model, according to the merged data and the portrait data, determine at least one candidate matching object under this type of matching object that matches the target user in the merged data, and display the candidate matching objects to the target user through a preset terminal; the user can send a third selection operation to the preset terminal based on action instructions such as clicking and touching, or voice instructions, to instruct the above-mentioned execution entity to determine the target matching object from the multiple candidate matching objects.

[0140] In this implementation manner, multiple candidate matching objects are determined based on the merged data and the portrait data for the user to select, meeting the diverse needs of the target user and enhancing the sense of participation and control of the target user.

[0141] In some alternative implementation manners of this embodiment, the above-mentioned execution entity can also perform the following operations:

[0142] First, determine a third target carrier that includes the target matching object; then, display a third panoramic image of the third target carrier through a preset terminal, where the third panoramic image includes recommendable objects in the third target carrier.

[0143] The third target carrier can be either an online virtual object carrier (such as an online store) or an offline physical object carrier.

[0144] Each object carrier is correspondingly provided with identification information of the recommendable objects it includes; based on the identification information of the target matching object, the third target carrier that includes the target matching object can be determined.

[0145] In this implementation manner, it can enable the user to know the third target carrier that includes the target matching object, thereby enabling the user to obtain the target matching object more conveniently, and further improving the shopping convenience and experience of the user.

[0146] In some optional implementation manners of this embodiment, the above execution subject may also perform the following operations: generate rich media comment data by combining the corresponding fused data and the feature data of the target carrier.

[0147] Among them, the fused data is the first fused data or the second fused data, and the target carrier is the first target carrier corresponding to the first fused data or the second target carrier corresponding to the second fused data. The rich media comment data includes, for example, images or videos corresponding to the fused data, and text comment data.

[0148] As an example, in response to the target user's consumption behavior regarding the target recommended object in the first fused data, generate rich media comment data by combining the first fused data and the feature data of the first target carrier. In response to the target user's consumption behavior regarding the selected object in the second fused data, generate rich media comment data by combining the second fused data and the feature data of the second target carrier.

[0149] Specifically, first, determine each component in the fused data; then, combine the feature data of the target carrier (such as the types of goods in a store, the dishes in a food store, the scenic spots in a scenic area, etc.) to extract feature information related to the content of the fused data. This may include the category, location, size, etc. of the object. By matching the image recognition result with the feature data of the target object carrier, the object in the image can be more accurately located and described. Finally, based on the results of image recognition and feature data extraction, use natural language processing and content generation technologies to generate rich media comment data. This may include:

[0150] Text comment: Generate descriptive text comments based on the recognized objects and scenes, highlighting the characteristics of the objects and the user's experience.

[0151] Image annotation: Add annotations to the image to indicate the location and relevant information of specific objects, enhancing the intuitiveness of the comment.

[0152] Video generation: If there are multiple images or video clips, they can be combined into a video and accompanied by explanations or subtitles to form richer comment content.

[0153] In this implementation, the above-mentioned execution entity can combine the corresponding fused data and the feature data of the target carrier to generate rich media comment data, improving the efficiency of comment data generation and further enhancing the user experience.

[0154] Continue to refer to Figure 6 , which shows a schematic flowchart 600 of another embodiment of the data processing method according to the present disclosure. In flowchart 600, the following steps are included:

[0155] Step 601, determine similar users of the target user according to the portrait data of the target user.

[0156] Step 602, determine target recommended objects that the target user is interested in from the published data of the similar users according to the portrait data.

[0157] Step 603, fuse the target recommended object with the user image of the target user, and obtain and display through a preset terminal the first fused data that adapts the target recommended object to the target user.

[0158] Step 604, edit the target recommended object in the first fused data according to the received editing operation through a large model to obtain an edited object.

[0159] Step 605, determine a second target carrier including the edited object or a similar object of the edited object.

[0160] Step 606, display the second panoramic image of the second target carrier through a preset terminal.

[0161] Among them, the second panoramic image includes recommendable objects in the second target carrier.

[0162] The flowchart 600 of the data processing method in this embodiment, compared with flowchart 200, specifically illustrates the process of editing the target recommended object to obtain an edited object and the process of determining a second target carrier including the edited object or a similar object of the edited object, enriching the data processing method, increasing the sense of experience substitution, and improving the controllability and participation of the user in the data recommendation process.

[0163] Continue to refer to Figure 7, as an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a data processing device. This system embodiment corresponds to the Figure 2 method embodiment shown, and this system can be specifically applied to various electronic devices.

[0164] As shown in Figure 7 , the data processing device 700 includes: a user determination unit 701 configured to determine similar users of a target user according to the portrait data of the target user; an object determination unit 702 configured to determine a target recommended object that the target user is interested in from the published data of the similar users according to the portrait data; a fusion unit 703 configured to fuse the target recommended object with the user image of the target user to obtain first fused data in which the target recommended object is adapted to the target user.

[0165] In some optional implementation manners of this embodiment, the object determination unit 702 is further configured to: determine candidate recommended objects that the target user is interested in from the published data of the similar users according to the portrait data; and determine a target recommended object associated with the target position corresponding to the target user from the candidate recommended objects.

[0166] In some optional implementation manners of this embodiment, the above device further includes: a carrier determination unit (not shown in the figure) configured to determine a first target carrier to which the target recommended object belongs from the object carriers associated with the target position.

[0167] In some optional implementation manners of this embodiment, the above device further includes: a display unit (not shown in the figure) configured to display the recommendable objects in the first target carrier through a preset terminal; and the object determination unit 702 is further configured to determine a selected object from the recommendable objects according to a first selection operation of the target user; the fusion unit 703 is further configured to fuse the selected object with the user image to obtain second fused data in which the selected object is adapted to the target user.

[0168] In some optional implementation manners of this embodiment, there are multiple selected objects, and the fusion unit 703 is further configured to: for each selected object, fuse the selected object with the user image to obtain second fused data in which the selected object is adapted to the target user; and display multiple second fused data corresponding to the multiple selected objects one by one through a preset terminal.

[0169] In some optional implementation manners of this embodiment, the display unit is further configured to: display a first panoramic image of the first target carrier through a preset terminal, where the first panoramic image includes the recommendable objects in the first target carrier; and according to a second selection operation on a target recommendable object among the recommendable objects, display three-dimensional model data of the target recommendable object through a preset terminal.

[0170] In some alternative implementation manners of this embodiment, the above device further includes: an editing unit (not shown in the figure), configured to edit a target recommended object in the first fused data according to a received editing operation through a large model to obtain an edited object; and the object determination unit 702 is further configured to determine a second target carrier including the edited object or a similar object of the edited object; the display unit is further configured to display a second panoramic image of the second target carrier through a preset terminal, where the second panoramic image includes recommendable objects in the second target carrier.

[0171] In some alternative implementation manners of this embodiment, the editing unit is further configured to: identify and display, through a preset terminal, multiple components in the target recommended object; edit a target component in the multiple components according to a received editing operation through a large model to obtain an edited object.

[0172] In some alternative implementation manners of this embodiment, the object determination unit 702 is further configured to: determine, from object carriers at a target position corresponding to an associated target user, a second target carrier including the edited object or a similar object of the edited object.

[0173] In some alternative implementation manners of this embodiment, the object determination unit 702 is further configured to determine a target matching object that matches the target user in the fused data according to the fused data and portrait data, where the fused data is the first fused data or the second fused data; the fusion unit 703 is further configured to fuse the target matching object and the fused data to obtain a third fused data that adapts the target matching object to the target user in the fused data.

[0174] In some alternative implementation manners of this embodiment, the object determination unit 702 is further configured to: determine multiple candidate matching objects that match the target user in the fused data according to the fused data and portrait data; determine a target matching object from the multiple candidate matching objects according to a received third selection operation.

[0175] In some alternative implementation manners of this embodiment, the carrier determination unit is further configured to determine a third target carrier including the target matching object; the display unit is further configured to display a third panoramic image of the third target carrier through a preset terminal, where the third panoramic image includes recommendable objects in the third target carrier.

[0176] In some alternative implementation manners of this embodiment, the above device further includes: a comment generation unit, configured to generate rich media comment data by combining the corresponding fused data and the feature data of the target carrier, where the fused data is the first fused data or the second fused data, and the target carrier is the first target carrier corresponding to the first fused data or the second target carrier corresponding to the second fused data.

[0177] In this embodiment, a data processing device is provided. According to the portrait data of the target user, similar users of the target user are determined; according to the portrait data, target recommended objects that the target user is interested in are determined from the published data of the similar users, and the target recommended objects that the target user is interested in can be determined quickly and accurately based on the published data of the similar users; the target recommended objects and the user image of the target user are fused to obtain the first fused data that adapts the target recommended objects to the target user, enabling the user to view the target recommended objects immersively, increasing the sense of experience substitution, and helping to improve the exposure rate and order success rate of the target recommended objects.

[0178] According to an embodiment of the present disclosure, the present disclosure further provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is enabled to implement the data processing method described in any of the above embodiments.

[0179] According to an embodiment of the present disclosure, the present disclosure further provides a readable storage medium, which stores computer instructions for enabling a computer to implement the data processing method described in any of the above embodiments when executed.

[0180] The embodiment of the present disclosure provides a computer program product, which can implement the data processing method described in any of the above embodiments when executed by a processor.

[0181] Figure 8 FIG. shows a schematic block diagram of an exemplary electronic device 800 that can be used to implement the embodiments of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, a personal digital processing, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely exemplary and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0182] AsFigure 8 As shown, device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0183] Multiple components in the device 800 are connected to the I / O interface 805, including: an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a magnetic disk, an optical disc, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the device 800 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0184] The computing unit 801 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 801 executes the various methods and processes described above, such as a data processing method. For example, in some embodiments, the data processing method can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the data processing method described above can be executed. Alternatively, in other embodiments, the computing unit 801 can be configured to execute the data processing method in any other appropriate manner (e.g., by means of firmware).

[0185] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), systems on a chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from a storage system, at least one input device, and at least one output device, and transmits the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0186] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing system, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowchart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, executed partially on the machine as an independent software package and partially on a remote machine, or executed entirely on a remote machine or server.

[0187] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0188] To provide for interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide for interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, speech input, or tactile input).

[0189] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0190] A computer system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The client - server relationship is created by computer programs running on the respective computers and having a client - server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system to address the defects of high management difficulty and weak business scalability existing in traditional physical hosts and virtual private server (VPS, Virtual Private Server) services; or it can be a server of a distributed system, or a server combined with blockchain.

[0191] According to the technical solution of the embodiment of the present disclosure, a data processing method and device are provided. Similar users of the target user are determined according to the portrait data of the target user; according to the portrait data, target recommended objects that the target user is interested in are determined from the published data of the similar users, and the target recommended objects that the target user is interested in can be determined quickly and accurately based on the published data of the similar users; the target recommended objects and the user image of the target user are fused to obtain first fused data in which the target recommended objects are adapted to the target user, so that the user can view the target recommended objects immersively, increasing the sense of experience substitution and helping to improve the exposure rate and order volume of the target recommended objects.

[0192] It should be understood that various forms of the processes shown above can be used, steps can be reordered, added or deleted. For example, the steps described in the present disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution provided by the present disclosure can be achieved, and no limitation is made herein.

[0193] The above specific embodiments do not constitute a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub - combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present disclosure shall be included within the protection scope of the present disclosure.

Claims

1. A data processing method, comprising: Determine similar users of the target user based on the target user's portrait data; According to the portrait data, determining the target recommended objects that the target user is interested in from the published data of the similar users; The target recommended object is fused with the user image of the target user to obtain and display, through a preset terminal, first fused data that adapts the target recommended object to the target user.

2. The method according to claim 1, wherein: The step of determining, based on the portrait data, target recommendation objects that the target user is interested in from the published data of the similar users includes: Determine, based on the portrait data, candidate recommendation objects that the target user is interested in from the published data of the similar users; A target recommended object associated with the target position corresponding to the target user is determined from the candidate recommended objects.

3. The method according to claim 2, wherein: Also includes: A first target carrier to which the target recommended object belongs is determined from the object carriers associated with the target position.

4. The method according to claim 3, wherein: Also includes: Displaying the recommendable objects in the first target carrier through the preset terminal; Determining a selection object from the recommended objects according to a first selection operation of the target user; The selected object is fused with the user image to obtain second fused data that matches the selected object with the target user.

5. The method according to claim 4, wherein: The selection objects are multiple, and The fusing the selected object with the user image to obtain second fused data that matches the selected object with the target user includes: For each of the selected objects, fuse the selected object with the user image to obtain second fused data that matches the selected object with the target user; The preset terminal displays a plurality of the second fused data corresponding to a plurality of the selected objects one by one.

6. The method according to claim 4, wherein: The displaying of the recommendable objects in the first target carrier through the preset terminal includes: Displaying a first panoramic image of the first target carrier through the preset terminal, wherein the first panoramic image includes a recommendable object in the first target carrier; According to a second selection operation on a target recommendable object among the recommendable objects, the three-dimensional model data of the target recommendable object is displayed through the preset terminal.

7. The method according to claim 1, wherein: Also includes: Using the large model, according to the received editing operation, editing the target recommended object in the first fused data to obtain an edited object; determining a second target carrier including the edited object or a similar object to the edited object; A second panoramic image of the second target carrier is displayed through the preset terminal, wherein the second panoramic image includes the recommendable objects in the second target carrier.

8. The method according to claim 7, wherein: The step of editing the target recommended object in the first fused data according to the received editing operation through the large model to obtain the edited object includes: Identify and display multiple components of the target recommendation object through the preset terminal; Through the large model, according to the received editing operation, the target component among the multiple components is edited to obtain the edited object.

9. The method according to claim 7, wherein: The determining a second target carrier including the edited object or a similar object to the edited object comprises: A second target carrier including the edited object or an object similar to the edited object is determined from the object carriers associated with the target position corresponding to the target user.

10. The method according to any one of claims 1 to 9, wherein: Also includes: Determining a target matching object to be matched with a target user in the fused data according to the fused data and the portrait data, wherein the fused data is the first fused data or the second fused data; The target matching object and the fused data are fused to obtain third fused data that matches the target matching object with the target user in the fused data.

11. The method according to claim 10, wherein: The step of determining a target matching object to be matched with a target user in the fused data according to the fused data and the portrait data includes: Determining, based on the fused data and the portrait data, a plurality of candidate matching objects that match the target user in the fused data; According to the received third selection operation, the target matching object is determined from the multiple candidate matching objects.

12. The method according to claim 11, wherein: Also includes: determining a third target carrier including the target matching object; The third panoramic image of the third target carrier is displayed through the preset terminal, wherein the third panoramic image includes the recommendable objects in the third target carrier.

13. The method according to any one of claims 1 to 9, wherein: Also includes: Rich media commentary data is generated by combining the corresponding fused data and characteristic data of the target carrier, wherein the fused data is the first fused data or the second fused data, and the target carrier is the first target carrier corresponding to the first fused data or the second target carrier corresponding to the second fused data.

14. A data processing device, comprising: A user determination unit, configured to determine similar users of the target user based on the portrait data of the target user; an object determination unit, configured to determine, based on the portrait data, a target recommended object that the target user is interested in from the published data of the similar users; The fusion unit is configured to fuse the target recommended object with the user image of the target user, and obtain and display through a preset terminal the first fused data that adapts the target recommended object to the target user.

15. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 13.

16. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 13.

17. A computer program product comprising: A computer program which, when executed by a processor, implements the method according to any one of claims 1 to 13.

Images