CN118113933A - Meta-universe data recommendation method and device - Google Patents
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Abstract
The application relates to the technical field of computers, and provides a meta-universe data recommendation method and device. The method comprises the following steps: acquiring target data in a meta-universe environment; the target data comprises scene target data, object target data and user target data; constructing a knowledge graph according to the target data; and recommending at least one kind of data in the scene, the object and the user to the user according to the knowledge graph. The meta-universe data recommendation method and the device provided by the application can fully mine the relation among scene data, object data, user data and data by creating the knowledge graph of the meta-universe data, so that the data and the relation among the data are fully utilized to recommend at least one kind of data in the scene, the object and the user to the user, behavior reference is provided for the user, and the utilization rate of data resources and the user experience are improved.
Description
Technical Field
The application relates to the technical field of computers, in particular to a meta space data recommendation method and device.
Background
With the rapid development of virtual reality and augmented reality technologies, the meta universe becomes a new field of immersive experience for people.
The amount of data in the meta universe is huge and complex, and thousands of ways exist between the data, but no effective method for organizing and utilizing the data exists at present, so that behavioral references cannot be provided for users according to the data, data resources are wasted, and user experience is reduced.
Disclosure of Invention
The embodiment of the application provides a meta-universe data recommendation method and device, which are used for solving the technical problems that at present, no effective method for utilizing meta-universe data is organized, so that behavior references cannot be provided for users according to the data, data resources are wasted and user experience is reduced.
In a first aspect, an embodiment of the present application provides a meta-universe data recommendation method, including:
Acquiring target data in a meta-universe environment; the target data comprises scene target data, object target data and user target data;
Constructing a knowledge graph according to the target data;
And recommending at least one kind of data in the scene, the object and the user to the user according to the knowledge graph.
In one embodiment, the constructing a knowledge-graph according to the target data includes:
Establishing first triplet data of any scene according to the relation between any scene target data and other scene target data, the relation between any scene target data and any object target data and the relation between any scene target data and any user target data;
Establishing second triplet data of any object according to the relation between any object target data and other object target data, the relation between any object target data and any scene target data and the relation between any object target data and any user target data;
Establishing third triplet data of any user according to the relation between any user target data and other user target data, the relation between any user target data and any scene target data and the relation between any user target data and any object target data;
And constructing a knowledge graph according to the first triplet data of all scenes, the second triplet data of all objects and the third triplet data of all users.
In one embodiment, the recommending at least one kind of data of the scene, the object and the user to the user according to the knowledge graph includes:
According to the knowledge graph, the relationship between the historical user target data of the user and the historical user target data of other users, the relationship between the historical user target data of the user and the historical scene target data and the relationship between the historical user target data of the user and the historical object target data are deduced to obtain at least one type of data in the preference scene, the preference object and the preference user of the user;
at least one type of data among the preference scene, the preference object, and the preference user is recommended to the user.
In one embodiment, the recommending at least one kind of data of the scene, the object and the user to the user according to the knowledge graph includes:
establishing an index for target data in the knowledge graph;
According to target keywords input by a user, scene target data, object target data and user target data related to the target keywords are searched in the knowledge graph through the index, and at least one type of data in a preference scene, preference objects and preference users of the user is obtained; the target keywords comprise scene keywords, object keywords and user keywords;
at least one type of data among the preference scene, the preference object, and the preference user is recommended to the user.
In one embodiment, the obtaining target data in a metauniverse environment includes:
acquiring scene data, object data and user data in a meta-universe environment;
And cleaning the scene data, the object data and the user data to obtain target data.
In one embodiment, after the knowledge-graph is constructed according to the target data, the method includes:
and updating the original target data in the knowledge graph according to the latest obtained target data so as to correct the knowledge graph in real time.
In one embodiment, the target data includes structured data, semi-structured data, and unstructured data.
In a second aspect, an embodiment of the present application provides a meta-cosmic data recommending apparatus, including:
The target data acquisition module is used for: acquiring target data in a meta-universe environment; the target data comprises scene target data, object target data and user target data;
the knowledge graph construction module is used for: constructing a knowledge graph according to the target data;
The data recommending module is used for: and recommending at least one kind of data in the scene, the object and the user to the user according to the knowledge graph.
In a third aspect, an embodiment of the present application provides an electronic device, including a processor and a memory storing a computer program, where the processor implements the steps of the meta-universe data recommendation method according to the first aspect when executing the program.
In a fourth aspect, an embodiment of the present application provides a non-transitory computer readable storage medium, including a computer program, which when executed by a processor, implements the steps of the meta-universe data recommendation method according to the first aspect.
According to the meta-universe data recommendation method and device, target data in a meta-universe environment are obtained, the target data comprise scene target data, object target data and user target data, a knowledge graph is constructed according to the target data, and at least one type of data in the scene, the object and the user is recommended to the user according to the knowledge graph. By creating the knowledge graph of the meta-universe data, the relation among the scene data, the object data, the user data and the data can be fully mined, so that the data and the relation among the data can be fully utilized to recommend at least one kind of data in the scene, the object and the user to the user, behavior references are provided for the user, and the utilization rate of data resources and the user experience are improved.
Drawings
In order to more clearly illustrate the application or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a metadata recommendation method according to an embodiment of the present application;
FIG. 2 is a second flowchart of a metadata recommendation method according to an embodiment of the present application;
Fig. 3 is a schematic structural diagram of a metadata recommendation device according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that in the description of embodiments of the present application, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element. The orientation or positional relationship indicated by the terms "upper", "lower", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of description and to simplify the description, and are not indicative or implying that the apparatus or elements in question must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as limiting the present application. Unless specifically stated or limited otherwise, the terms "mounted," "connected," and "coupled" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art according to the specific circumstances.
The terms "first," "second," and the like in this specification are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged, as appropriate, such that embodiments of the present application may be implemented in sequences other than those illustrated or described herein, and that the objects identified by "first," "second," etc. are generally of a type, and are not limited to the number of objects, such as the first object may be one or more. In addition, "and/or" indicates at least one of the connected objects, and the character "/", generally indicates that the associated object is an "or" relationship.
Fig. 1 is a schematic flow chart of a metadata recommendation method according to an embodiment of the present application. Referring to fig. 1, an embodiment of the present application provides a meta-universe data recommendation method, which may include:
101. acquiring target data in a meta-universe environment;
The target data includes scene target data, object target data, and user target data;
102. Constructing a knowledge graph according to the target data;
103. and recommending at least one type of data in the scene, the object and the user to the user according to the knowledge graph.
In step 101, the target data may include structured data, semi-structured data, and unstructured data.
In step 102, the data is represented in the form of a knowledge graph, so that a more comprehensive, direct and accurate knowledge view can be provided, and the reliability requirement of the user using the data in the meta-universe scene is improved.
According to the meta-universe data recommendation method provided by the application, target data in a meta-universe environment is obtained, the target data comprise scene target data, object target data and user target data, a knowledge graph is constructed according to the target data, and at least one type of data in the scene, the object and the user is recommended to the user according to the knowledge graph. By creating the knowledge graph of the meta-universe data, the relation among the scene data, the object data, the user data and the data can be fully mined, so that the data and the relation among the data can be fully utilized to recommend at least one kind of data in the scene, the object and the user to the user, behavior references are provided for the user, and the utilization rate of data resources and the user experience are improved.
FIG. 2 is a second flowchart of a metadata recommendation method according to an embodiment of the present application. Referring to fig. 2, in one embodiment, constructing a knowledge-graph from target data may include:
201. establishing first triplet data of a scene according to the relation between any scene target data and other scene target data, the relation between the scene target data and any object target data and the relation between the scene target data and any user target data;
202. establishing second triplet data of the object according to the relation between any object target data and other object target data, the relation between the object target data and any scene target data and the relation between the object target data and any user target data;
203. Establishing third triplet data of the user according to the relation between any user target data and other user target data, the relation between the user target data and any scene target data and the relation between the user target data and any object target data;
204. And constructing a knowledge graph according to the first triplet data of all scenes, the second triplet data of all objects and the third triplet data of all users.
In step 201 to step 203, the triplet data refers to data in the form of "entity-relationship-entity", any scene, any object and any user can be used as an entity, and the relationship between every two entities is mined through the data corresponding to the entity, so as to form the triplet data. Thus, in step 201, for any scene A, its first triplet data may be "scene A-relationship-scene B", "scene A-relationship-object C" or "scene A-relationship-user D"; in step 202, for any object C, the second triplet data may be "object C-relationship-object E", "object C-relationship-scene A", or "object C-relationship-user D"; in step 203, for any user D, the third triplet data may be "user D-relationship-user F", "user D-relationship-scene a", or "user D-relationship-object C".
In step 204, the knowledge graph is constructed by mining the relationships between all scenes, objects, users, objects, scenes, users, objects and users.
In practical applications, there is no strict timing relationship between step 201, step 202 and step 203; that is, any steps may be performed simultaneously or first, depending on the actual requirement, and are not limited herein.
According to the embodiment, the knowledge graph is constructed by the scene target data, the object target data and the user target data in the meta-universe and the relation between the scene target data, the object target data and the user target data, so that the running state and the trend of the meta-universe can be fully and comprehensively mined, and a foundation is laid for providing user behavior reference subsequently.
In one embodiment, recommending at least one type of data of a scene, an object and a user to the user according to the knowledge graph may include:
And according to the relation between the historical user target data of the user and the historical user target data of other users, the relation between the historical user target data of the user and the historical scene target data and the relation between the historical user target data of the user and the historical object target data, at least one type of data in the preference scene, the preference object and the preference user of the user is obtained by reasoning, and at least one type of data in the preference scene, the preference object and the preference user is recommended to the user.
The user target data comprises attribute data, behavior data and the like of the user, the scene target data comprises attribute data, state data and the like of the scene, the object target data comprises attribute data, state data and the like of the object, and the preference of the user to the scene, the object and the user can be deduced according to the historical interaction data among the user target data, the historical interaction data of the user target data and the scene target data and the historical interaction data of the user target data and the object target data, so that the preferred scene, object and user can be recommended to the user according to the preference.
It should be noted that, according to the inference result, one type of data, two types of data or three types of data of the scene, the object and the user may be recommended to the user, which is not limited herein.
According to the embodiment, through analysis and statistics of target data in the knowledge graph, association and rules in the entity and the relation can automatically recommend preference options for the user, and the user can be helped to make decisions and plans in the meta universe.
In one embodiment, recommending at least one type of data of a scene, an object and a user to the user according to the knowledge graph may include:
establishing an index for target data in the knowledge graph, and searching scene target data, object target data and user target data related to the target keywords in the knowledge graph through the index according to the target keywords input by the user to obtain at least one type of data in the user's preference scene, preference object and preference user, and recommending the at least one type of data in the preference scene, preference object and preference user to the user, wherein the target keywords comprise scene keywords, object keywords and user keywords.
And storing and managing the entities and the relations in the knowledge graph by utilizing technologies such as a graph database and the like, and realizing rapid parallel access and processing of mass data stored in a distributed mode by combining indexes. For example, when a user inputs a scene keyword, the scene target data, the object target data and the user target data related to the scene keyword may be searched in the knowledge graph according to the scene keyword, and since the scene keyword is a search keyword of the user, that is, represents a preference of the user, the scene target data, the object target data and the user target data related to the scene keyword may represent a preference scene, a preference object and a preference user of the user, and are similar when the user inputs the object keyword or the user keyword, which is not repeated herein.
According to the embodiment, target data in the knowledge graph can be searched through target keywords input by a user, association and rules in statistical entities and relations are analyzed, preference options are recommended to the user, namely, the preference options are recommended to the user according to preference requirements actively submitted by the user, and the user is helped to make decisions and plan in the meta universe.
Furthermore, the automatic reasoning result of the knowledge graph can be combined with the user retrieval, when the user inputs the target keyword, the result which is strongly related to the target keyword can be rapidly screened from the automatic reasoning result to be used as a recommendation option, or the result obtained by mutually correcting the automatic reasoning result and the reasoning result after the user retrieval is used as the recommendation option, so that the accuracy and the reliability of the recommendation option are further improved.
In one embodiment, obtaining target data in a metauniverse environment may include:
scene data, object data and user data in the meta-universe environment are collected, and data cleaning is carried out on the scene data, the object data and the user to obtain target data.
According to the embodiment, the data can be de-duplicated and de-noised by cleaning the data, so that the consistency, accuracy and completeness of the data are ensured, a rich and accurate data set is obtained, and a data base is provided for constructing a knowledge graph.
In one embodiment, after constructing the knowledge-graph according to the target data, it may include:
And updating the original target data in the knowledge graph according to the latest target data so as to correct the knowledge graph in real time.
Because the user data, the scene data and the object data in the meta-universe environment are changed in real time, the target data also need to be updated in real time and synchronized into the knowledge graph, for example, the target data in the knowledge graph is added, deleted, corrected and the like, so that the accuracy and the integrity of the knowledge graph are ensured.
According to the embodiment, the flexibility and the expandability of the data in the meta-universe scene are enhanced through the real-time updating of the knowledge graph, the accuracy and the integrity of the knowledge graph are ensured, and the accuracy and the reliability of providing recommended options for users by using the knowledge graph are further improved.
The description of the metadata recommendation device provided by the embodiment of the present application is provided below, and the metadata recommendation device described below and the metadata recommendation method described above may be referred to correspondingly.
Fig. 3 is a schematic structural diagram of a metadata recommendation device according to an embodiment of the present application. Referring to fig. 3, an embodiment of the present application provides a meta-cosmic data recommending apparatus, which may include:
a target data acquisition module 301, configured to: acquiring target data in a meta-universe environment; the target data comprises scene target data, object target data and user target data;
the knowledge graph construction module 302 is configured to: constructing a knowledge graph according to the target data;
a data recommendation module 303, configured to: and recommending at least one kind of data in the scene, the object and the user to the user according to the knowledge graph.
The application provides a meta-universe data recommendation device, which is used for acquiring target data in a meta-universe environment, wherein the target data comprises scene target data, object target data and user target data, constructing a knowledge graph according to the target data, and recommending at least one type of data in a scene, an object and a user to the user according to the knowledge graph. By creating the knowledge graph of the meta-universe data, the relation among the scene data, the object data, the user data and the data can be fully mined, so that the data and the relation among the data can be fully utilized to recommend at least one kind of data in the scene, the object and the user to the user, behavior references are provided for the user, and the utilization rate of data resources and the user experience are improved.
In one embodiment, the knowledge graph construction module 302 is specifically configured to:
Establishing first triplet data of any scene according to the relation between any scene target data and other scene target data, the relation between any scene target data and any object target data and the relation between any scene target data and any user target data;
Establishing second triplet data of any object according to the relation between any object target data and other object target data, the relation between any object target data and any scene target data and the relation between any object target data and any user target data;
Establishing third triplet data of any user according to the relation between any user target data and other user target data, the relation between any user target data and any scene target data and the relation between any user target data and any object target data;
And constructing a knowledge graph according to the first triplet data of all scenes, the second triplet data of all objects and the third triplet data of all users.
In one embodiment, the data recommendation module 303 is specifically configured to:
According to the knowledge graph, the relationship between the historical user target data of the user and the historical user target data of other users, the relationship between the historical user target data of the user and the historical scene target data and the relationship between the historical user target data of the user and the historical object target data are deduced to obtain at least one type of data in the preference scene, the preference object and the preference user of the user;
at least one type of data among the preference scene, the preference object, and the preference user is recommended to the user.
In one embodiment, the data recommendation module 303 is specifically configured to:
establishing an index for target data in the knowledge graph;
According to target keywords input by a user, scene target data, object target data and user target data related to the target keywords are searched in the knowledge graph through the index, and at least one type of data in a preference scene, preference objects and preference users of the user is obtained; the target keywords comprise scene keywords, object keywords and user keywords;
at least one type of data among the preference scene, the preference object, and the preference user is recommended to the user.
In one embodiment, the target data acquisition module 301 is specifically configured to:
acquiring scene data, object data and user data in a meta-universe environment;
And cleaning the scene data, the object data and the user data to obtain target data.
In one embodiment, the system further comprises a data updating module (not shown in the figure) for:
and updating the original target data in the knowledge graph according to the latest obtained target data so as to correct the knowledge graph in real time.
In one embodiment, the target data includes structured data, semi-structured data, and unstructured data.
Fig. 4 illustrates a schematic structural diagram of an electronic device, as shown in fig. 4, which may include: processor 410, communication interface (Communication Interface) 420, memory 430, and communication bus 440, wherein processor 410, communication interface 420, and memory 430 communicate with each other via communication bus 440. Processor 410 may call a computer program in memory 430 to perform the steps of the metauniverse data recommendation method, including, for example:
Acquiring target data in a meta-universe environment; the target data comprises scene target data, object target data and user target data;
Constructing a knowledge graph according to the target data;
And recommending at least one kind of data in the scene, the object and the user to the user according to the knowledge graph.
Further, the logic instructions in the memory 430 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, embodiments of the present application further provide a computer program product, where the computer program product includes a computer program, where the computer program may be stored on a non-transitory computer readable storage medium, where the computer program when executed by a processor is capable of executing the steps of the meta space data recommendation method provided in the foregoing embodiments, for example, including:
Acquiring target data in a meta-universe environment; the target data comprises scene target data, object target data and user target data;
Constructing a knowledge graph according to the target data;
And recommending at least one kind of data in the scene, the object and the user to the user according to the knowledge graph.
In another aspect, embodiments of the present application further provide a processor-readable storage medium storing a computer program for causing a processor to execute the steps of the method provided in the above embodiments, for example, including:
Acquiring target data in a meta-universe environment; the target data comprises scene target data, object target data and user target data;
Constructing a knowledge graph according to the target data;
And recommending at least one kind of data in the scene, the object and the user to the user according to the knowledge graph.
The processor-readable storage medium may be any available medium or data storage device that can be accessed by a processor, including, but not limited to, magnetic storage (e.g., floppy disks, hard disks, magnetic tape, magneto-optical disks (MOs), etc.), optical storage (e.g., CD, DVD, BD, HVD, etc.), and semiconductor storage (e.g., ROM, EPROM, EEPROM, non-volatile storage (NAND FLASH), solid State Disk (SSD)), etc.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. A meta-universe data recommendation method, comprising:
Acquiring target data in a meta-universe environment; the target data comprises scene target data, object target data and user target data;
Constructing a knowledge graph according to the target data;
And recommending at least one kind of data in the scene, the object and the user to the user according to the knowledge graph.
2. The meta-universe data recommendation method of claim 1 wherein the constructing a knowledge graph from the target data includes:
Establishing first triplet data of any scene according to the relation between any scene target data and other scene target data, the relation between any scene target data and any object target data and the relation between any scene target data and any user target data;
Establishing second triplet data of any object according to the relation between any object target data and other object target data, the relation between any object target data and any scene target data and the relation between any object target data and any user target data;
Establishing third triplet data of any user according to the relation between any user target data and other user target data, the relation between any user target data and any scene target data and the relation between any user target data and any object target data;
And constructing a knowledge graph according to the first triplet data of all scenes, the second triplet data of all objects and the third triplet data of all users.
3. The meta-universe data recommendation method of claim 1 wherein the recommending at least one type of data among a scene, an object, and a user to a user according to the knowledge-graph comprises:
According to the knowledge graph, the relationship between the historical user target data of the user and the historical user target data of other users, the relationship between the historical user target data of the user and the historical scene target data and the relationship between the historical user target data of the user and the historical object target data are deduced to obtain at least one type of data in the preference scene, the preference object and the preference user of the user;
at least one type of data among the preference scene, the preference object, and the preference user is recommended to the user.
4. The meta-universe data recommendation method of claim 1 wherein the recommending at least one type of data among a scene, an object, and a user to a user according to the knowledge-graph comprises:
establishing an index for target data in the knowledge graph;
According to target keywords input by a user, scene target data, object target data and user target data related to the target keywords are searched in the knowledge graph through the index, and at least one type of data in a preference scene, preference objects and preference users of the user is obtained; the target keywords comprise scene keywords, object keywords and user keywords;
at least one type of data among the preference scene, the preference object, and the preference user is recommended to the user.
5. The meta-cosmic data recommendation method according to claim 1, wherein the acquiring the target data in the meta-cosmic environment includes:
acquiring scene data, object data and user data in a meta-universe environment;
And cleaning the scene data, the object data and the user data to obtain target data.
6. The meta-universe data recommendation method of claim 5 wherein after constructing a knowledge graph from the target data, comprising:
and updating the original target data in the knowledge graph according to the latest obtained target data so as to correct the knowledge graph in real time.
7. The meta-universe data recommendation method of claim 1 wherein,
The target data includes structured data, semi-structured data, and unstructured data.
8. A meta-universe data recommendation device, comprising:
The target data acquisition module is used for: acquiring target data in a meta-universe environment; the target data comprises scene target data, object target data and user target data;
the knowledge graph construction module is used for: constructing a knowledge graph according to the target data;
The data recommending module is used for: and recommending at least one kind of data in the scene, the object and the user to the user according to the knowledge graph.
9. An electronic device comprising a processor and a memory storing a computer program, characterized in that the processor implements the steps of the meta-cosmic data recommendation method according to any of claims 1 to 7 when the computer program is executed.
10. A non-transitory computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the steps of the meta space data recommendation method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410055504.7A CN118113933A (en) | 2024-01-15 | 2024-01-15 | Meta-universe data recommendation method and device |
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