Customized virtual exhibition hall system

Abstract

The invention provides a customized virtual exhibition hall system. The system comprises a user data recording module used for recording basic information and browsing behavior information of a target user and interaction information between the target user and a virtual exhibition hall; the user data analysis module is used for performing user preference analysis according to the information recorded by the user data to obtain a user preference analysis result; the personalized recommendation module is used for providing preference content recommendation services for the user according to the user preference analysis result, and the preference content recommendation services comprise preference content recommendation, preference content display priority adjustment, associated content recommendation of preference content and / or preference content recommendation of users with similar behaviors; and the path planning module is used for planning the shortest visiting path which enables the target user to browse the preference content. According to the customized virtual exhibition hall system provided by the invention, personalized customization of the virtual exhibition hall content and the visiting route can be realized, and visiting experience closer to the requirement of a user is provided for the user.

Description

Technical Field

[0001] The present application relates to the field of virtual reality technology, and in particular to a customized virtual exhibition hall system. Background Art

[0002] With the development of information technology and the improvement of user experience requirements, enterprises and institutions are paying more and more attention to attracting and maintaining customers through digital means. Although traditional virtual exhibition halls provide certain conveniences, they are still insufficient in terms of personalized services. How to use advanced data analysis technology and artificial intelligence algorithms to dynamically adjust exhibition hall content and plan tour routes based on user behavior preferences has become an urgent problem to be solved.

[0003] Therefore, the present application provides a customized virtual exhibition hall system to solve one of the above technical problems. Summary of the invention

[0004] The purpose of this application is to provide a customized virtual exhibition hall system that can solve at least one of the technical problems mentioned above. The specific solution is as follows: According to the specific implementation of the present application, in a first aspect, the present application provides a customized virtual exhibition hall system, including: A user data collection module is used to collect the basic information of the target user, browsing behavior information, and interaction information between the target user and the virtual exhibition hall; a user data analysis module is used to perform user preference analysis based on the information collected by the user data to obtain user preference analysis results; a personalized recommendation module is used to provide users with preference content recommendation services based on the user preference analysis results, including preference content recommendation, preference content display priority adjustment, related content recommendation of preference content, and / or preference content recommendation of users with similar behavior; a route planning module is used to plan the shortest tour route that allows the target user to browse the preference content.

[0005] In one embodiment, the user data collection module includes: a user management submodule, which is used to provide user login and user authentication for the target user, and to create a personal virtual image based on the basic information and preference data of the target user; wherein the preference data is data input by the target user for adjusting the personal virtual image; a data collection submodule, which is used to collect the browsing behavior information of the target user, including the number of clicks, browsing time, page dwell time, and / or number of returns of the target user on the virtual exhibition hall; and an interaction record submodule, which is used to store the interaction information, including the interaction record between the target user and the virtual exhibition hall, and feedback information submitted by the target user.

[0006] In one implementation, the user data analysis module includes: a data processing sub-module for cleaning, feature extraction, and feature construction of the basic information, browsing behavior information, and / or interaction information to obtain basic features; a feature engineering sub-module for constructing combined features and time series features based on the basic features; and a preference analysis sub-module for performing user preference analysis according to the basic features, the combined features, and the time series features to obtain the user preference analysis result.

[0007] In one implementation, the personalized recommendation module includes: a content recommendation sub-module for predicting the preference group to which the target user belongs through a classification model to recommend preference content to the target user; a sequential pattern mining sub-module for comparing the planned historical visit route with the actual historical trajectory data of the target user to obtain frequent subsequences in the historical visit route for adjusting the display priority of preference content for the target user; wherein, the adjustment of the display priority of preference content includes adjusting the placement order of preference content and / or adjusting the planned order of preference content in the visit route; a dynamic adjustment sub-module for monitoring the real-time behavior of the target user to trigger the execution of associated content recommendation for preference content when the real-time behavior meets specified conditions; and a collaborative filtering sub-module for calculating the similarity between the target user and other users according to the behavior of the target user to recommend the preference content of the similar behavior users to the target user when similar behavior users are determined.

[0008] In one implementation, the similarity calculation adopts the Pearson correlation coefficient calculation method.

[0009] In one implementation, the system further includes: a virtual scene creation module for creating the virtual exhibition hall corresponding to the real exhibition hall through three-dimensional modeling technology and outputting images or animation sequences through rendering technology to display the virtual exhibition hall.

[0010] In one implementation, the virtual scene creation module is further used to create the activity boundary of the target user in the virtual exhibition hall.

[0011] In one implementation, the system further includes: the display module for embedding the exhibits and explanation information of the real exhibition hall into the virtual exhibition hall.

[0012] In one implementation, the path planning module uses the shortest path algorithm to plan the shortest visit route that enables the target user to browse the preference content.

[0013] In one implementation, the content recommendation sub-module is further used to dynamically adjust the display content according to the real-time behavior of the target user.

[0014] Compared with the prior art, the above solution of the embodiment of the present application has at least the following beneficial effects: The present application provides a customized virtual exhibition hall system, including a user data collection module, a user data analysis module, a personalized recommendation module, and a path planning module. This architecture ensures that the system can comprehensively understand the needs of users from multiple dimensions and provide highly personalized services accordingly. By combining the basic information, browsing behavior information, and interaction information of users, the system can more accurately capture the interest points of users, and then provide accurate content recommendations and services for users, significantly improving user satisfaction and engagement. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Shows a block diagram of a customized virtual exhibition hall system; Figure 2 Shows a block diagram of the composition of a user data collection module; Figure 3 Shows a block diagram of the composition of a user data analysis module; Figure 4 Shows a block diagram of the composition of a personalized recommendation module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] In order to make the objectives, technical solutions, and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0017] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "the", and "said" used in the embodiments of the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. "Multiple" generally includes at least two.

[0018] It should be understood that the term " / and / " used herein is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the preceding and following associated objects.

[0019] It should be understood that although terms such as first, second, and third may be used in the embodiments of the present application for description, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of the present application, the first may also be referred to as the second, and similarly, the second may also be referred to as the first.

[0020] Depending on the context, as used herein, the words "if", "when" can be interpreted as "when...", "when...", "in response to determining", or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detected (stated condition or event)" can be interpreted as "when determined", "in response to determining", "when detecting (stated condition or event)", or "in response to detecting (stated condition or event)".

[0021] It should also be noted that the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, so that a commodity or system including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such commodity or system. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the commodity or system including the said element.

[0022] It should be particularly noted that symbols and / or numbers in the specification, if not marked in the accompanying drawings, are not reference numerals.

[0023] The present application provides a customized virtual exhibition hall system, which is based on the digital twin technology of the metaverse and the customized guided tour system based on artificial intelligence.

[0024] The optional embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0025] Figure 1 A block diagram of a customized virtual exhibition hall system 1 is shown, as Figure 1 shown, the customized virtual exhibition hall system 1 at least includes a user data collection module 11, a user data analysis module 12, a personalized recommendation module 13, a path planning module 14, and may include a virtual scene creation module 15 and / or a display module 16 based on requirements.

[0026] The user data collection module 11 is used to collect the basic information, browsing behavior information of the target user, and the interaction information between the target user and the virtual exhibition hall.

[0027] The user data analysis module 12 is used to analyze the user preferences according to the information collected by the user data, and obtain the user preference analysis result.

[0028] The personalized recommendation module 13 is used to provide users with preference content recommendation services according to the user preference analysis results, including preference content recommendation, adjustment of the display priority of preference content, recommendation of associated content of preference content, and / or recommendation of preference content of users with similar behaviors.

[0029] The path planning module 14 is used to plan the shortest visit route that enables the target user to view preference content.

[0030] This application provides a customized virtual exhibition hall system 1, including a user data collection module 11, a user data analysis module 12, a personalized recommendation module 13, and a path planning module 14. This architecture ensures that the system can comprehensively understand the needs of users from multiple dimensions and provide highly personalized services accordingly. By combining the basic information, browsing behavior information, and interaction information of users, the system can more accurately capture the interest points of users, and then provide accurate content recommendations and services for users, significantly improving user satisfaction and engagement.

[0031] Figure 2 A block diagram showing the composition of a user data collection module 11 is as Figure 2 shown. The user data collection module 11 includes a user management sub-module 111, a data collection sub-module 112, and an interaction record sub-module 113.

[0032] The user management sub-module 111 is used to provide user login, user authentication for the target user, and create a personal virtual image according to the basic information and preference data of the target user. Among them, the preference data is the data input by the target user for adjusting the personal virtual image.

[0033] The data collection sub-module 112 is used to collect the browsing behavior information of the target user, including the number of clicks, browsing time, page stay time, and / or return times of the target user for the virtual exhibition hall.

[0034] The interaction record sub-module 113 is used to store interaction information, including the interaction records between the target user and the virtual exhibition hall, and the feedback information submitted by the target user.

[0035] In the embodiments of this application, the specific composition of the user data collection module 11 is further refined, including a user management sub-module 111, a data collection sub-module 112, and an interaction record sub-module 113. The introduction of these sub-modules not only enhances the system's ability to obtain user information, but also makes the entire data collection process more structured and systematic. In particular, the function of creating a personal virtual image increases the user's sense of immersion and belonging. And the detailed behavior tracking helps with more in-depth data analysis later, laying a solid foundation for personalized services.

[0036] Figure 3 shows a block diagram of the composition of a user data analysis module 12, as Figure 3 shown, the user data analysis module 12 includes a data processing sub-module 121, a feature engineering sub-module 122, and a preference analysis sub-module 123.

[0037] The data processing sub-module 121 is used to clean, extract features, and construct features from basic information, browsing behavior information, and / or interaction information to obtain basic features.

[0038] The feature engineering sub-module 122 is used to construct combined features and time series features based on the basic features.

[0039] The preference analysis sub-module 123 is used to perform user preference analysis according to the basic features, combined features, and time series features to obtain the user preference analysis result.

[0040] In the embodiments of the present application, by cleaning, extracting features, and constructing the original data, the system can effectively remove noise data and refine truly valuable information. The construction of combined features and time series features enables the system to better understand the user's behavior patterns and development trends, improving the accuracy of preference prediction. The results of preference analysis directly guide the personalized recommendation strategy, ensuring a high degree of matching between the recommended content and the user's needs.

[0041] In some embodiments, the system uses the Pandas library of Python to clean data, handle missing values and outliers, extract features such as activity density, interest intensity, and activity index, and construct time series features such as behavior trends and periodic behaviors. Analyze the user's browsing depth, behavior coherence, and return visit rate to understand the visit pattern and points of interest. Use a classification model to predict the user preference group, and use the PrefixSpan algorithm to mine frequent subsequences to adjust the exhibit order. Dynamically adjust the display content according to real-time behavior, such as recommending relevant exhibits if the stay time is long. Based on collaborative filtering technology, find similar users and recommend the exhibits they like, and combine the shortest path algorithm to plan the shortest route including the exhibits of interest to ensure efficient access to content.

[0042] Figure 4 shows a block diagram of the composition of a personalized recommendation module 13, as Figure 4 shown, the personalized recommendation module 13 includes a content recommendation sub-module 131, a sequential pattern mining sub-module 132, a dynamic adjustment sub-module 133, and a collaborative filtering sub-module 134.

[0043] The content recommendation sub-module 131 is used to predict the preference group to which the target user belongs through a classification model to recommend preference content to the target user.

[0044] The sequence pattern mining sub-module 132 is used to compare the planned historical visit route with the actual historical trajectory data of the target user to obtain the frequent subsequences in the historical visit route, so as to adjust the display priority of the preferred content for the target user. Among them, the adjustment of the display priority of the preferred content includes adjusting the arrangement order of the preferred content and / or adjusting the planned order of the preferred content in the visit route.

[0045] The dynamic adjustment sub-module 133 is used to monitor the real-time behavior of the target user, so as to trigger the recommendation of associated content of the preferred content when the real-time behavior meets the specified conditions.

[0046] The collaborative filtering sub-module 134 is used to calculate the similarity between the target user and other users according to the behavior of the target user, so as to recommend the preferred content of the similar behavior users to the target user when the similar behavior users are determined.

[0047] In the embodiments of the present application, the application of the classification model can help the system quickly identify the preferred groups of users, so as to achieve efficient personalized recommendation. The sequence pattern mining optimizes the arrangement order of the exhibits and makes the visit route more reasonable. The dynamic adjustment mechanism under real-time behavior monitoring ensures the immediacy and pertinence of the content recommendation. The collaborative filtering based on similarity calculation broadens the recommendation source, increases the possibility of discovering new interest points, and greatly enriches the user's visit experience.

[0048] As a feasible embodiment, the similarity calculation adopts the Pearson correlation coefficient calculation method. Among them, the Pearson correlation coefficient is a widely used and mature statistical index, which can accurately measure the strength of the linear relationship between two variables. Applying this calculation method in this system can effectively quantify the similarity of behavior patterns between different users, provide more appropriate content recommendations for users, simplify the calculation process at the same time, and improve the response speed of the system.

[0049] As a feasible embodiment, the system further includes a virtual scene creation module 15, which is used to create a virtual exhibition hall corresponding to the real exhibition hall through 3D modeling technology and output an image or animation sequence through rendering technology to display the virtual exhibition hall. This method not only enhances the realism of the visual effect, but also makes the user feel as if they are in a real exhibition hall, enhancing the interest and educational significance of the visit. The output of high-quality images or animation sequences also brings a more vivid and intuitive display method for users, further promoting the transmission and understanding of information.

[0050] As a feasible embodiment, the virtual scene creation module 15 is also used to create the activity boundary of the target user in the virtual exhibition hall. This design takes into account the needs of security and regularity in the virtual environment, preventing accidents caused by users' incorrect operations. At the same time, the reasonable setting of the activity range also ensures the free exploration of users in the virtual exhibition hall, without affecting the experience quality due to excessive restrictions, achieving a balance between user experience and system management.

[0051] As a feasible embodiment, the system further includes a display module 16 for embedding the exhibits and explanation information of the real exhibition hall into the virtual exhibition hall.

[0052] In some embodiments, the system outputs a preset visit route for users to automatically tour, continuously optimizing the visit route to ensure a personalized and efficient experience. By using 3D modeling and rendering technologies to create a realistic virtual scene, users can move freely in the metaverse, and real exhibits and explanation information are embedded in the form of images, web pages, videos, etc., enhancing the sense of reality and interactivity.

[0053] As a feasible embodiment, the path planning module 14 uses the shortest path algorithm to plan the shortest visit route that meets the browsing preferences of the target user.

[0054] In the embodiments of the present application, the path planning module 14 uses the shortest path algorithm to plan the shortest visit route that meets the browsing preferences of the user. Among them, the shortest path algorithm can be, for example, the Dijkstra algorithm. By calculating the visit route through the shortest path algorithm, not only can the time cost of users be saved, but also it can ensure that they can access the exhibits they are interested in with the optimal path, improving the overall fluency and satisfaction of the visit.

[0055] As a feasible embodiment, the content recommendation sub-module 131 is also used to dynamically adjust the display content according to the real-time behavior of the target user. This feature allows the system to continuously respond to the latest feedback of users during the visit, continuously optimize the recommendation results, and maintain the freshness and attractiveness of the content.

[0056] In this application, a digital exhibition hall service system is built. The system includes multiple different general modules such as a user data collection module 11, a virtual scene creation module 15, a display module 16, etc., which are used to support the convenient interaction between the virtual exhibition hall and users. The user data collection module 11 collects users' basic information and behavior preferences in various ways. Specifically, it records users' registration information (such as age, gender, occupation, hobbies, etc.), behavior tracking data in the virtual exhibition hall (such as click times, browsing time, page stay time, and return times, etc., an event stream with timestamps), the interaction with elements in the virtual exhibition hall (such as exhibits, buttons, Q&A, etc.), and direct feedback (such as ratings, comments, questionnaire results). The virtual scene creation module 15 is responsible for constructing a realistic virtual exhibition hall space, enabling users to move freely in the metaverse. This involves using 3D modeling software to model in a one-to-one ratio according to the actual size and layout, adding real material textures and simulating natural lighting conditions, and finally outputting high-quality images or animation sequences through a rendering engine. At the same time, an intelligent navigation function is integrated to optimize the user's tour experience. The display module 16 embeds the exhibits and explanatory information in the real scene into the virtual exhibition hall in various forms, provides detailed exhibit viewing, interactive 3D model display, and dynamically adjusts the content according to user preferences.

[0057] In addition, the system also has a user data analysis module 12. This module can use the Pandas library in Python to clean the raw data, remove unnecessary rows or columns, handle missing values and outliers to ensure the data quality, and extract useful features from these data and construct combined features and time series features to better understand users' behavior patterns and development trends. In addition, this module is also used to predict users' preference groups, analyze visiting habits, adjust the display content in real time, and plan the shortest visiting route in combination with users' interests, so as to provide users with a highly personalized and efficient visiting experience.

[0058] The implementation process of a customized virtual exhibition hall system 1 begins with a user registering an account through a website or application and filling in basic information (such as age, gender, occupation) and hobbies. The system collects this information for subsequent personalized recommendations. Once the user starts visiting the virtual exhibition hall, the system immediately records their behavioral data, including the number of clicks, browsing time, page stay time, and return times, and stores it in a database. At the same time, the interactions between the user and the elements in the virtual exhibition hall (such as exhibits, buttons, Q&A, etc.) and any feedback information are recorded. To ensure the quality of the data, the Pandas library in Python is used for data cleaning, handling missing values and outliers, and then useful features are extracted from the original data, such as activity density, interest intensity, activity index, etc., and time series features are constructed, such as behavioral trends and periodic behaviors, to analyze the user's browsing depth, behavioral coherence, and return rate, and understand their visiting patterns and points of interest.

[0059] In the embodiments of this application, the random forest algorithm can be used to predict which preference group the user belongs to, such as science enthusiasts or art enthusiasts, etc. Secondly, the PrefixSpan algorithm can be used to mine the frequent subsequences in the user's visiting route, analyze the visiting habits and adjust the exhibit arrangement order accordingly. Next, the display content is dynamically adjusted according to the user's real-time behavior. For example, when the user stays in front of an exhibit for more than a certain time, more relevant exhibits are recommended. In addition, collaborative filtering technology can be used to find other users with similar behaviors and recommend the exhibits they like. On this basis, the Dijkstra algorithm can be further used to plan the shortest path and combine the user's interests to plan the shortest visiting route including the exhibits the user is interested in. Finally, the system organizes the output results into a preset visiting route and provides it to the user. The user can choose to automatically tour along the preset route, and the system will optimize the provided preset visiting route in real time according to the user's information and habits to ensure that the user can access the most interesting content in the most efficient way, thus obtaining a personalized and efficient visiting experience.

[0060] Although the operations are described in a specific order in the drawings, it should not be understood as requiring the operations to be performed in the specific order shown or in a serial order, or requiring all the operations shown to obtain the desired result. In certain environments, multitasking and parallel processing may be advantageous.

[0061] Any of the steps, operations, or procedures described herein can be performed or implemented using one or more hardware or software modules alone or in combination with other devices. In one embodiment, the software module is implemented using a computer program product including a computer-readable medium containing computer program code, which can be executed by a computer processor to perform any or all of the described steps, operations, or procedures.

[0062] For purposes of illustration and description, the foregoing description of the implementation of the present application has been given. The foregoing description is not exhaustive and is not intended to limit the present application to the exact form disclosed. Various variations and modifications may be possible in light of the above teachings, or various variations and modifications may be obtained from the practice of the present application. These embodiments are chosen and described in order to illustrate the principles of the present application and its practical application, so that those skilled in the art can utilize the present application in various embodiments and various modifications suitable for the particular purposes contemplated.

[0063] It can be further understood that, unless otherwise specified, "connection" includes direct connection between two elements without other components therebetween, and also includes indirect connection between two elements with other elements therebetween.

[0064] It can be further understood that although the operations are described in a specific order in the drawings in the embodiments of the present application, it should not be construed as requiring the operations to be performed in the specific order shown or in a serial order, or requiring all the operations shown to obtain the desired result. In certain environments, multitasking and parallel processing may be advantageous.

[0065] Other embodiments of the present application will be readily apparent to those skilled in the art after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include known common general knowledge or conventional technical means in the art not disclosed in the present application. The specification and embodiments are to be considered as exemplary only, and the true scope and spirit of the present application are pointed out by the following claims.

[0066] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.

[0067] The above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A customized virtual exhibition hall system, characterized in that: include: A user data collection module is used to collect basic information of target users, browsing behavior information, and interaction information between the target users and the virtual exhibition hall; A user data analysis module, used to perform user preference analysis based on the information collected by the user data to obtain a user preference analysis result; A personalized recommendation module, used to provide users with preferred content recommendation services based on the user preference analysis results, including preferred content recommendation, preferred content display priority adjustment, related content recommendation of preferred content, and / or preferred content recommendation of users with similar behaviors; The path planning module is used to plan the shortest tour route that allows the target user to browse the preferred content.

2. The customized virtual exhibition hall system according to claim 1, characterized in that: The user data collection module includes: A user management submodule, used to provide user login and user authentication for the target user, and to create a personal virtual image according to the basic information and preference data of the target user; wherein the preference data is data input by the target user for adjusting the personal virtual image; A data collection submodule, used to collect the browsing behavior information of the target user, including the number of clicks, browsing time, page dwell time, and / or number of returns of the target user on the virtual exhibition hall; The interaction record submodule is used to store the interaction information, including the interaction record between the target user and the virtual exhibition hall, and the feedback information submitted by the target user.

3. The customized virtual exhibition hall system according to claim 1, characterized in that: The user data analysis module includes: A data processing submodule, for cleaning, extracting features and constructing features of the basic information, the browsing behavior information and / or the interactive information to obtain basic features; A feature engineering submodule, used for constructing combined features and time series features according to the basic features; The preference analysis submodule is used to perform user preference analysis according to the basic features, the combined features, and the time series features to obtain the user preference analysis results.

4. The customized virtual exhibition hall system according to claim 1, characterized in that: The personalized recommendation module includes: A content recommendation submodule, used to predict the preference group to which the target user belongs through a classification model, so as to recommend preferred content to the target user; The sequence pattern mining submodule is used to compare the planned historical tour route with the actual historical trajectory data of the target user to obtain the frequent subsequences in the historical tour route, so as to adjust the priority of displaying the preferred content for the target user; wherein the priority of displaying the preferred content includes adjusting the order of placing the preferred content and / or adjusting the order of planning the preferred content in the tour route; A dynamic adjustment submodule, used to monitor the real-time behavior of the target user, so as to trigger the execution of the recommendation of related content for the preferred content when the real-time behavior meets the specified conditions; The collaborative filtering submodule is used to calculate the similarity between the target user and other users according to the behavior of the target user, so as to recommend the preferred content of the similar behavior users to the target user when similar behavior users are determined.

5. The customized virtual exhibition hall system according to claim 4, characterized in that: The similarity calculation adopts the Pearson correlation coefficient calculation method.

6. The customized virtual exhibition hall system according to claim 1, characterized in that: The system further comprises: The virtual scene creation module is used to create the virtual exhibition hall corresponding to the real exhibition hall through three-dimensional modeling technology, and output images or animation sequences through rendering technology to display the virtual exhibition hall.

7. The customized virtual exhibition hall system according to claim 6, characterized in that: The virtual scene creation module is also used to create an activity boundary of the target user in the virtual exhibition hall.

8. The customized virtual exhibition hall system according to claim 1, characterized in that: The system further comprises: The display module is used to embed the exhibits and explanation information of the real exhibition hall into the virtual exhibition hall.

9. The customized virtual exhibition hall system according to claim 1, characterized in that: The path planning module adopts the shortest path algorithm to plan the shortest tour route that allows the target user to browse the preferred content.

10. The customized virtual exhibition hall system according to claim 1, characterized in that: The content recommendation submodule is also used to dynamically adjust the displayed content according to the real-time behavior of the target user.

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