Artificial intelligence-based cultural and creative work display management method and system

Abstract

The invention relates to the technical field of artificial intelligence, and provides an artificial intelligence-based cultural and creative work display management method and system. The method comprises the following steps: acquiring a cultural creative work database which comprises a plurality of works and recommended interested user portraits; establishing exhibiting user portraits; identifying common characteristics of the two, and calculating a recommendation probability; extracting high-probability works to form a work set, and performing clustering analysis on similarity; eliminating repeated works based on the similarity, and optimizing a work set; obtaining a display position map, and counting the number; if the position is insufficient, assigning and randomly extracting works according to a recommendation probability; and managing the extracted works through the display module by taking the display position as a limit. The technical problem that the display effect is poor due to the fact that display is influenced by subjective factors and accurate recommendation is not carried out in the prior art is solved, and the technical effects that automatic screening, recommendation and display management are carried out on the creative works through the artificial intelligence technology, and the display effect and the management efficiency are improved are achieved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and specifically to a method and system for displaying and managing cultural and creative works based on artificial intelligence. Background Art

[0002] With the vigorous development of the cultural and creative industry and the sharp increase in the number of cultural and creative works, traditional exhibition management methods have been unable to cope with such large-scale and efficient exhibition needs. Those methods that rely on manual screening and arrangement are not only inefficient, but also difficult to cover all works, and are even affected by subjective factors, resulting in many excellent cultural and creative works not being fully displayed and recognized. At the same time, as the audience's aesthetics and interests in cultural and creative works continue to change, their requirements for exhibition management are also getting higher and higher. The audience hopes to see more works that meet their preferences and needs, and requires exhibition management to more accurately grasp the audience's needs and preferences and provide a personalized exhibition experience. Summary of the invention

[0003] This application provides a method and system for the display and management of cultural and creative works based on artificial intelligence, aiming to solve the technical problems in the prior art that the display is affected by subjective factors and no accurate recommendation is made, resulting in poor display effect.

[0004] In view of the above problems, the present application provides a method and system for displaying and managing cultural and creative works based on artificial intelligence.

[0005] In a first aspect disclosed in the present application, a method for displaying and managing cultural and creative works based on artificial intelligence is provided, the method comprising: obtaining a cultural and creative work database, wherein the cultural and creative work database includes a plurality of cultural and creative works, and establishing a plurality of recommended interest user portraits of the plurality of cultural and creative works; establishing an exhibitor user portrait of an exhibitor user; identifying common features of the exhibitor user portrait and the plurality of recommended interest user portraits to obtain a plurality of recommendation probabilities of the plurality of cultural and creative works; extracting a first collection of works whose recommendation probability is greater than a first preset probability, and clustering the works similarity of the first collection of works to generate a similar works distribution index; performing a similar works elimination analysis based on the similar works distribution index, deleting the eliminated works from the first collection of works to obtain a first optimized collection of works; obtaining a display position distribution map of a display module, and counting the number of display positions; determining whether the number of display positions is less than or equal to the number of first works in the first optimized collection of works, and if so, assigning a recommendation probability to the first optimized collection of works based on the plurality of recommendation probabilities to obtain a first display probability set; taking the number of display positions as a constraint, randomly extracting the first optimized collection of works based on the first display probability set, and displaying and managing the extracted works through a display module.

[0006] Another aspect disclosed in the present application provides a cultural and creative works display management system based on artificial intelligence, the system comprising: a database acquisition component, the database acquisition component is used to acquire a cultural and creative works database, wherein the cultural and creative works database includes multiple cultural and creative works, and establishes multiple recommended interest user portraits of the multiple cultural and creative works; a portrait establishment component, the portrait establishment component is used to establish an exhibitor user portrait of an exhibitor user; a feature recognition component, the feature recognition component is used to perform common feature recognition on the exhibitor user portrait and the multiple recommended interest user portraits to obtain multiple recommendation probabilities for the multiple cultural and creative works; a similarity clustering component, the similarity clustering component is used to extract a first collection of works whose recommendation probability is greater than a first preset probability, and perform work similarity clustering on the first collection of works to generate a similar work distribution index an elimination analysis component, the elimination analysis component is used to perform elimination analysis of similar works based on the similar works distribution index, delete the eliminated works from the first collection of works, and obtain a first optimized collection of works; a quantity statistics component, the quantity statistics component is used to obtain a display position distribution map of the display module, and count the number of display positions; a probability assignment component, the probability assignment component is used to determine whether the number of display positions is less than or equal to the number of first works in the first optimized collection of works, and if so, assign a recommendation probability to the first optimized collection of works based on the multiple recommendation probabilities to obtain a first display probability set; a random extraction component, the random extraction component is used to randomly extract the first optimized collection of works based on the first display probability set with the number of display positions as a constraint, and display and manage the extracted works through the display module.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The above-mentioned method for displaying and managing cultural and creative works based on artificial intelligence can understand the audience characteristics of each work by acquiring a database of cultural and creative works and establishing a user portrait of recommended interests. Subsequently, based on the portrait of the participating users, the common features with the user portrait of recommended interests are identified, thereby calculating the recommendation probability of each work for the participating users. After that, the works with a high recommendation probability are screened out, and similarity clustering analysis is performed to avoid displaying works that are too similar. Then, based on the available positions of the display modules, the screened works are intelligently extracted to ensure that the most attractive works are displayed within a limited position. This method not only improves the display efficiency, but also better meets the personalized needs of the audience and enhances the visiting experience.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 A flowchart of a method for displaying and managing cultural and creative works based on artificial intelligence in one embodiment;

[0012] Figure 2 This is an architecture diagram of an artificial intelligence-based cultural and creative works display management system in one embodiment.

[0013] Explanation of the accompanying drawings: database acquisition component 1, portrait building component 2, feature recognition component 3, similarity clustering component 4, elimination analysis component 5, quantity statistics component 6, probability assignment component 7, random extraction component 8. DETAILED DESCRIPTION

[0014] The embodiments of the present application provide a method and system for managing the display of cultural and creative works based on artificial intelligence, thereby solving the technical problems in the prior art that the display is affected by subjective factors and no accurate recommendation is made, resulting in poor display effects.

[0015] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only 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 ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0016] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0017] Embodiment 1

[0018] like Figure 1 As shown, the present application provides a method for displaying and managing cultural and creative works based on artificial intelligence, the method comprising:

[0019] Acquire a cultural and creative work database, wherein the cultural and creative work database includes a plurality of cultural and creative works, and establish a plurality of recommended interest user portraits of the plurality of cultural and creative works;

[0020] With the rapid development of the cultural and creative industry, the number of cultural and creative works has exploded. Traditional display management methods, such as manual screening and arrangement, can no longer cope with such large-scale display needs. Therefore, the introduction of artificial intelligence technology can achieve automated and intelligent management of cultural and creative works, greatly improve display efficiency, and make accurate recommendations.

[0021] In an embodiment of the present application, during the process of displaying and managing cultural and creative works, the system terminal first obtains a cultural and creative work database, which contains information on numerous cultural and creative works. Subsequently, in order to recommend these works more accurately, the system terminal collects historical display records of multiple cultural and creative works and identifies interested users. After that, user features are extracted based on the identification results, and similar features are merged based on these features to construct multiple recommended interest user portraits. These portraits can reflect which types of users may be interested in the work. Through this step, works that are more likely to attract the attention of the audience can be screened out based on the recommended interest user portraits. In this way, the most attractive works can be selected for display from limited display resources, thereby optimizing the audience's viewing experience and laying a solid foundation for subsequent display management.

[0022] Furthermore, the present application provides for establishing multiple recommended interest user portraits of the multiple cultural and creative works, including:

[0023] Collecting historical display records of the plurality of cultural and creative works to identify interested users, and obtaining a plurality of interested user sets;

[0024] Extracting user features from the multiple sets of interested users to obtain multiple user feature sets;

[0025] The multiple user feature sets are centrally merged with similar features to obtain the multiple recommended interest user portraits.

[0026] Preferably, when preparing to display cultural and creative works, the system terminal first collects and analyzes the historical display records of multiple cultural and creative works. By analyzing these records, users who are interested in which works can be identified, and then multiple interest user sets are obtained. Subsequently, the characteristics of these interest users, such as age, gender, occupation, education, etc., are further extracted to form multiple user feature sets. These feature sets provide the system terminal with detailed information about which user groups may like which works. After that, the system terminal performs a pairwise similarity comparison to generate multiple similarities. Then, cluster analysis is performed on the similarities that meet the conditions to obtain multiple recommended interest user portraits. These portraits help the system terminal to more accurately understand which users may be interested in which cultural and creative works, providing strong support for subsequent display management.

[0027] Furthermore, the present application provides for merging similar features of the plurality of user feature sets, and the method further includes:

[0028] Extracting a first user feature set from the multiple user feature sets, and performing a similarity comparison on any two user features in the first user feature set to generate multiple first feature similarities;

[0029] Clustering user features whose first feature similarity is greater than a preset similarity to obtain multiple cluster feature sets;

[0030] Optionally, after obtaining multiple user feature sets, the system terminal randomly selects a user feature set as the first user feature set. Subsequently, any two user features in the feature set are compared for similarity. The system terminal selects a suitable similarity comparison method based on the type of feature. For example, for numerical features, such as age, the Euclidean distance is used to calculate the similarity; for categorical features, such as gender or occupation, the chi-square test is used to compare the similarity. Afterwards, all feature pairs in the first user feature set are traversed, and a similarity comparison operation is performed on each pair of user features. This involves inputting the values ​​of the two features into the selected similarity comparison method to obtain the similarity value between them. For each pair of features, a similarity value is generated based on the comparison result. This value reflects the degree of similarity between the two features, ranging from 0 to 1. Then, all calculated similarity values ​​are recorded to generate multiple first feature similarities. These first feature similarities will serve as the data basis for subsequent analysis.

[0031] After obtaining multiple first feature similarities, the system terminal sets a preset similarity as a threshold, which is used to screen out user feature pairs with sufficiently high similarities for subsequent clustering operations. Subsequently, all calculated first feature similarities are traversed to screen out user feature pairs with a similarity greater than the preset similarity. These user feature pairs will be considered highly similar and suitable for clustering. After that, the screened user feature pairs are converted into numerical vectors suitable for K-means algorithm processing, each vector representing a user feature. Then, a suitable K value is determined based on business requirements and data distribution. Then, K user feature vectors are randomly selected as initial cluster centers. These cluster centers will serve as the starting point for iteration. Further, the iterative process of the K-means algorithm is started. In each iteration, each user feature vector is assigned to the nearest cluster center, and then the center position of each cluster is recalculated. This process is repeated until the cluster center no longer changes significantly. After clustering is completed, each user feature vector is grouped into different cluster feature sets according to the cluster to which it belongs. Each cluster feature set represents a user group with similar user features. Through such processing, the system terminal can more clearly understand the characteristics and differences between different user groups, and provide more accurate data support for the establishment of subsequent recommended interest user portraits.

[0032] Calculate clustering indices of multiple clustering feature sets, merge features of clustering feature sets whose clustering indices are greater than preset indices, obtain a first recommended interest user portrait and add it to the multiple recommended interest user portraits, wherein the clustering indices include a cluster quantity index and a cluster distance index.

[0033] Optionally, after obtaining multiple cluster feature sets, the system terminal evaluates the quality and effectiveness of these clusters. This is achieved by calculating cluster indicators, which include cluster quantity indicators and cluster distance indicators, which can reflect the number of clusters and the closeness of the internal features of the clusters. Specifically, the system terminal first calculates the number of features in each cluster feature set, and then adds these numbers to obtain the total number of features. Subsequently, the number of features in each cluster feature set is divided by the total number of features to obtain the cluster quantity indicator of each cluster feature set. This indicator reflects the proportion of each cluster feature set in the overall features. For each pair of cluster feature sets, the system terminal calculates the maximum distance between them as the cluster distance indicator through the Euclidean distance, and this maximum distance refers to the minimum similarity between the features in the two cluster feature sets. Afterwards, according to the preset cluster indicator threshold, the cluster feature sets with cluster indicators greater than the preset value are screened out. These cluster feature sets have good clustering effects and representativeness, and can more accurately reflect the interests and preferences of users. For the screened cluster feature sets, feature merging operations are performed. Feature merging is completed by taking the features with the highest frequency in each cluster feature set. The merged feature set will form a comprehensive and more representative user portrait. Then, based on the merged feature set, a first recommended interest user portrait is generated. This portrait will contain the user's main interests, preferences, and behavioral characteristics for subsequent recommendations and displays. Finally, the generated first recommended interest user portrait is added to the existing multiple recommended interest user portraits. These portraits can more accurately reflect the user's interests and preferences, and therefore have a higher recommendation value. Through the above process, the system terminal successfully uses clustering indicators to screen and merge the cluster feature set, and obtains a recommended interest user portrait with a higher recommendation value.

[0034] Furthermore, the present application provides for establishing a plurality of recommended interest user portraits of the plurality of cultural and creative works, the method further comprising:

[0035] Identifying display frequency information of historical display records of the plurality of cultural and creative works;

[0036] Extracting low-frequency cultural and creative works whose display frequency is less than a preset frequency based on the display frequency information;

[0037] Optionally, when identifying the historical display records of multiple cultural and creative works, the system terminal pays attention to the display frequency information of each of the multiple works. This information reflects the number of times the work has been displayed in the past period of time, and is an important basis for evaluating the popularity and display effect of the work. Subsequently, based on this display frequency information, low-frequency cultural and creative works with a display frequency lower than the preset frequency are screened out. This preset frequency is set according to actual needs, aiming to identify those works that are relatively rarely displayed or receive less attention. Through this screening process, low-frequency cultural and creative works can be effectively identified, which may not have enough display opportunities due to content, style or other reasons. Understanding the characteristics of these low-frequency works will help to better adjust the display strategy, increase their exposure and attention, and thus promote the diversity and richness of cultural and creative works. In summary, identifying low-frequency cultural and creative works is a screening process based on the display frequency information in the historical display records, aiming to discover those works that are less displayed and provide a basis for subsequent display strategy adjustments.

[0038] Mining similar works of the low-frequency literary and creative works to identify user portraits of similar works, and obtaining reference user portraits;

[0039] The reference user portrait is used to compensate the recommended interest user portrait of the low-frequency cultural and creative works.

[0040] Optionally, in order to gain a deeper understanding of the audience characteristics of low-frequency cultural and creative works, the system terminal mines other works similar to these works. These similar works may have similar themes, styles or cultural connotations, so their audiences may also overlap with potential users of low-frequency cultural and creative works. Subsequently, by identifying the user portraits of these similar works, a series of reference user portraits can be obtained. These reference user portraits describe the audience characteristics of similar works, including their interests, preferences, behavior patterns, etc. Afterwards, using these reference user portraits, the recommended interest user portraits of low-frequency cultural and creative works can be compensated. This means that the system terminal can adjust or improve the recommended interest user portraits of low-frequency cultural and creative works in combination with the information of the reference user portraits to make them more accurate and comprehensive. In this way, when the system terminal recommends low-frequency cultural and creative works, it can more accurately find potential interested users and improve the accuracy and effectiveness of the recommendations. In summary, by mining similar works of low-frequency cultural and creative works and identifying the user portraits of these similar works, valuable reference information can be provided for the recommended interest user portraits of low-frequency cultural and creative works, thereby optimizing the recommendation strategy and improving user satisfaction and experience.

[0041] Establish a user profile of exhibitors;

[0042] In one embodiment, the system terminal collects relevant data of the exhibitors, including their basic information, exhibition history, behavior records at the exhibition, such as the exhibition areas visited, stay time, interaction, etc., as well as feedback and opinions that may be collected through questionnaires or interviews. Subsequently, these data are sorted and analyzed to identify the common characteristics and differences of the exhibitors. For example, it is found that some users prefer certain types of cultural and creative products, or their activity patterns at the exhibition have certain regularities. Afterwards, based on these analysis results, user portraits of the exhibitors are constructed. These portraits will describe the characteristics of the users in detail, such as their interests, purchasing habits, and expectations for the exhibition. At the same time, the portraits will also reveal the differences between different user groups, helping the system terminal to understand the needs of each group more accurately. In summary, establishing the exhibitor user portrait of the exhibitor is a process of understanding and describing the characteristics and behaviors of the exhibitors by collecting and analyzing data. These portraits not only help to understand the users more deeply, but also provide strong support for the optimization and improvement of the exhibition.

[0043] Identify common features of the exhibitor user portrait and the multiple recommended interest user portraits to obtain multiple recommendation probabilities of the multiple cultural and creative works;

[0044] In one embodiment, in order to more accurately recommend cultural and creative works to exhibitors, the system terminal identifies common features of the exhibitor user portraits and multiple existing recommended interest user portraits. Common features refer to features that appear in different user portraits, which can reflect the similarities and common needs of user groups in certain aspects. By comparing and analyzing the common features of these user portraits, the potential connection between the exhibitor users and different recommended interest user groups can be understood. For example, it is found that some exhibitors and user portraits that like a certain type of cultural and creative works have a large overlap in age, occupation or interest, which suggests that these exhibitors may also be interested in this type of cultural and creative works. Based on the identification of these common features, the system terminal further calculates the recommendation probability of multiple cultural and creative works for the exhibitors. The recommendation probability is calculated based on the matching degree of the user portraits, which reflects the degree of fit between the works and the potential interests of the exhibitors. By comprehensively considering the similarity of the common features, a corresponding recommendation probability can be calculated for each cultural and creative work. Finally, multiple recommendation probabilities for multiple cultural and creative works are obtained, and these probabilities can help the system terminal more accurately determine which works are more likely to be favored by the exhibitors.

[0045] Furthermore, the present application provides for identifying common features of the exhibitor user portrait and the plurality of recommended interest user portraits to obtain a plurality of recommendation probabilities of the plurality of cultural and creative works, and the method further includes:

[0046] The exhibitor user portrait includes multiple user features of multiple users;

[0047] The multiple user features are compared with the multiple recommended interest user portraits, the proportions of the multiple user features falling into the multiple recommended interest user portraits are identified, and the multiple recommendation probabilities are generated.

[0048] Preferably, in order to provide more accurate cultural and creative works recommendations for exhibitors, the system terminal compares these user features with multiple existing recommended interest user portraits. During the comparison process, each user feature in the exhibitor user portrait is checked one by one to analyze whether they can find matching or similar features in the recommended interest user portrait. By counting the proportion of user features in the exhibitor user portrait that fall into each recommended interest user portrait, it is possible to preliminarily understand which recommended interest user groups the exhibitor is closer to. Subsequently, based on these proportion information, multiple recommendation probabilities are generated. The recommendation probability is calculated based on the degree of user feature matching, which reflects the degree of fit of the exhibitor user for a specific recommended interest user portrait. If multiple user features of the exhibitor user have a high degree of matching in a recommended interest user portrait, then the recommendation probability of the cultural and creative work for this exhibitor user will be correspondingly high. Afterwards, by comparing the exhibitor user portrait with the recommended interest user portrait and calculating the corresponding recommendation probability, a more personalized and accurate cultural and creative work recommendation can be provided to the exhibitor. These recommendation probabilities not only take into account the degree of matching of user features, but also combine the characteristics of the recommended interest user portrait, thereby improving the accuracy and effectiveness of the recommendation. In summary, by comparing the user characteristics in the exhibition user portrait and the recommended interest user portrait, and calculating the recommendation probability, we can provide more accurate and personalized cultural and creative work recommendation services for exhibition users.

[0049] Extracting a first collection of works whose recommendation probability is greater than a first preset probability, and performing work similarity clustering on the first collection of works to generate a similar work distribution index;

[0050] In one embodiment, after generating the recommendation probabilities of multiple cultural and creative works, the system terminal sets a first preset probability, which is set according to the actual recommendation needs and is used to screen out the works that are most likely to be of interest to the exhibitors. By comparing the recommendation probability of each of the multiple works with the first preset probability, a collection of works with a recommendation probability greater than the preset probability can be extracted, namely, the first collection of works. Subsequently, in order to gain a deeper understanding of the relationship and distribution between the works in the first collection of works, the system terminal performs work similarity clustering. The system terminal calculates the similarity between each pair of works in the first collection of works through a work similarity analysis network. Afterwards, based on these similarity values, the works are clustered so that the works in the same cluster are relatively close in similarity, while the works in different clusters have relatively large differences. Through the work similarity clustering, similar work distribution indicators are generated. These indicators describe the distribution and clustering structure of the works in the first collection of works, which helps to understand which works are relatively close in style, theme, etc., so as to provide exhibitors with more diversified and differentiated recommendations.

[0051] Furthermore, the present application provides clustering the similarity of the first collection of works to generate a similar work distribution index, and the method further includes:

[0052] Enumerate and combine the works in the first collection of works in pairs to obtain multiple combinations of works;

[0053] Building a work similarity analysis network based on the twin network, performing similarity identification on the plurality of work combinations, and obtaining the similarities of the plurality of works;

[0054] Preferably, the system terminal enumerates and combines each work in the first work in pairs to form multiple work combinations. The purpose of this is to find all possible work pairs, so as to more comprehensively evaluate the similarity between the works. Subsequently, in order to perform similarity analysis on these work combinations, the system terminal collects a large number of work pairs with similarity labels as training data, and divides a part as a verification set. After that, two sub-networks with the same structure and shared parameters are designed to process the input work pairs respectively. Then, the structure of the sub-network is determined, including a convolutional layer (for image works), a fully connected layer, a recurrent neural network (for text works), etc., to effectively extract the features of the works. Then, the sub-network is trained to learn how to extract meaningful features, which are essential for distinguishing different works and judging the similarity between works. Further, a module is designed to calculate the similarity between feature vectors extracted from the sub-network. This module has a built-in Euclidean distance for calculating the similarity score between the work pairs. Subsequently, the entire twin network is trained using the training data with similarity labels. And a loss function is defined to measure the difference between the similarity score output by the network and the true label. The parameters of the network are adjusted through gradient descent to minimize the loss function, so that the network can better predict the similarity between works. After the training is completed, the system terminal outputs these multiple work similarity analysis networks. After that, the system terminal inputs these work combinations into the constructed work similarity analysis network for similarity identification. The network extracts and compares the features of each pair of works and outputs the similarity value between them. These similarity values ​​reflect the similarity of the works in terms of content, style, theme, etc., and provide the system terminal with important information about the relationship between the works. Then, the system terminal obtains multiple work similarity values, which can help further understand the similarity relationship between the works in the first collection. By analyzing and processing these similarity values, more personalized and accurate work recommendations can be provided to exhibitors, and basic data support can be provided for further recommendations and optimization.

[0055] Cluster the works whose work similarity is greater than the work similarity threshold, and use the average work similarity in the clustering results to identify the work similarity;

[0056] The similar works distribution index is generated based on the clustering result and the works similarity identifier.

[0057] Preferably, after completing the analysis of the similarity of the works, the system terminal obtains the similarity score between each pair of works. In order to further understand and organize these works, clustering operations are performed using the same method as the aforementioned method for obtaining multiple clustering feature sets, and works with higher similarity are clustered together to form different work clusters. During the clustering process, the system terminal sets one or more work similarity thresholds. This threshold determines which works should be classified into the same cluster. When the similarity scores of two or more works are higher than this threshold, they will be regarded as similar works and classified into the same cluster. After clustering, each cluster contains a group of works with higher similarity. In order to more intuitively represent the level of similarity of the works in each cluster, the system terminal calculates the average value of the similarity of the works in each cluster, and uses this average value as the work similarity identifier of the cluster. This identifier can help quickly understand the overall similarity of the works in each cluster. Finally, based on these clustering results and the work similarity identifiers, the system terminal generates similar work distribution indicators. These indicators reflect the distribution of different similarity levels in the works, such as how many clusters there are, the size of each cluster, and the work similarity identifier of each cluster. These indicators provide the system terminal with comprehensive and in-depth information about the similarity of works, which helps to better understand the relationship and distribution between works and provide strong support for subsequent recommendation and display strategies.

[0058] Based on the similar works distribution index, performing similar works elimination analysis, deleting eliminated works from the first collection of works, and obtaining a first optimized collection of works;

[0059] In one embodiment, based on the obtained similar works distribution index, the system terminal performs a similar works elimination analysis. The purpose of this analysis is to ensure that the types of works displayed are rich enough while maintaining the user's interest, and to avoid being too concentrated on a certain type of work. During the elimination analysis process, the system terminal examines the scale and work similarity identifier of each multiple work cluster. For those clusters that are too large in scale and too similar in work, resulting in too concentrated types, appropriate works are eliminated. Specifically, the system terminal formulates a set of elimination strategies based on the average value of the cluster's work similarity and the cluster size, and preferentially eliminates those works that have high similarity but high duplication with other cluster works. Through this elimination analysis, some works are successfully deleted from the first collection of works, thereby obtaining a more optimized first optimized collection of works with richer types. This optimized collection of works not only retains the works that users are interested in, but also avoids the simplification of work types to a large extent, providing users with more diverse and interesting choices.

[0060] Obtain the display location distribution map of the display module and count the number of display locations;

[0061] In one embodiment, the system terminal collects specific information about the display modules at various different locations. By collating these data, a display location distribution map is obtained. This distribution map presents the distribution of display modules at various locations. Through this map, you can intuitively see which locations have more display modules, which locations have fewer, and the relative relationship between them. At the same time, the number of display locations is also counted. This statistical result can help understand the overall scale of the display module and the specific number at each location. By comparing the number of displays at different locations, it is possible to further analyze whether the layout of the display module is reasonable and whether it needs to be optimized and adjusted. In summary, the display location distribution map of the display module is obtained, and the number of display locations is counted. These tasks not only help the system terminal understand the actual layout of the display module, but also provide an important basis for subsequent optimization and adjustment. By making rational use of this information, the display effect of the display module can be further improved and the user experience can be improved.

[0062] Determine whether the number of display positions is less than or equal to the number of first works in the first optimized work set, and if so, assign a recommendation probability to the first optimized work set based on the multiple recommendation probabilities to obtain a first display probability set;

[0063] In one embodiment, the number of display positions is compared with the number of works in the first optimized collection. The purpose of this step is to determine whether the display positions are sufficient to accommodate all optimized works. If the number of display positions is less than or equal to the number of works in the first optimized collection, it means that each display position can correspond to one or more works for display. At this time, the system terminal extracts the second collection of works with a probability greater than the second preset probability from the cultural and creative works database, and repeats the similar works elimination analysis to increase the number of works until the number of display positions is less than the number of works in the optimized collection. Subsequently, the system terminal assigns a recommendation probability to each of the multiple works in the first optimized collection based on the multiple recommendation probabilities calculated previously. This assignment process is determined based on the recommendation probability of each of the multiple works. The higher the recommendation probability, the greater the chance of the work to be displayed. Through this step, the system terminal obtains a first display probability set. Each element in this set corresponds to the display probability of one or more works in the first optimized collection, which reflects the priority and possibility of the work when it is displayed. In summary, when the number of display positions is sufficient or less than the number of works in the first optimized collection, they are assigned according to the recommendation probability of the works to obtain a display probability set, which provides an important basis for the subsequent display of works.

[0064] Further, the present application provides a method for determining whether the number of the display positions is less than the number of first works in the first optimized work set, the method further comprising:

[0065] If the number of display positions is greater than the number of first works in the first optimized collection of works, extracting a second collection of works with a probability greater than a second preset probability from the cultural and creative works database, wherein the second preset probability is less than the first preset probability;

[0066] Performing similar works elimination analysis on the second collection of works to obtain a second optimized collection of works;

[0067] Determine whether the number of display positions is less than the number of second works in the second optimized work set. If not, update the second preset probability according to a preset reduction step, and repeat the similar work elimination analysis until the number of display positions is less than the number of works in the optimized work set.

[0068] Preferably, when the number of display positions exceeds the number of works in the first optimized collection, the system terminal extracts works with a recommendation probability greater than a second preset probability from the cultural and creative works database to form a second collection. The second preset probability here is lower than the first preset probability, which means that the system terminal relaxes the screening conditions to obtain more candidate works. Subsequently, the second collection is subjected to similar works elimination analysis. The purpose of this step is to remove works that are too similar or repeated to the first optimized collection, ensuring that the works finally displayed are both diverse and in line with the interests of users. Through the elimination analysis, the second optimized collection is obtained. Afterwards, it is determined again whether the number of remaining display positions is still greater than the number of works in the second optimized collection. If the display positions are still sufficient, it is necessary to further adjust the screening conditions to expand the range of works. Specifically, the system terminal updates the second preset probability according to the preset reduction step size, and repeats the similar works elimination analysis. This process will continue until the number of display positions is less than the number of works in the optimized collection. In summary, when there are too many display positions, the range of works is gradually narrowed by extracting the second collection, performing similar works elimination analysis, and adjusting the screening conditions until the display position requirements are met.

[0069] Taking the number of display positions as a constraint, the first optimized portfolio is randomly selected based on the first display probability set, and the selected works are displayed and managed through the display module.

[0070] In one embodiment, based on the known number of display locations, the system terminal performs targeted work extraction and display management. First, according to the limitation of the display locations, the number of display locations cannot be exceeded for display of works. Therefore, the system terminal randomly extracts the first optimized work set according to the first display probability set. In this process, the chance of each multiple work being extracted is proportional to its probability value in the first display probability set, which means that works with higher recommendation probabilities are more likely to be selected for display. After the extraction is completed, the extracted works are displayed and managed using the display module. This includes determining the position, arrangement, and interactive functions of each multiple work on the display interface, ensuring that the works can be presented in a preset manner and that users can browse and interact conveniently.

[0071] In summary, the embodiments of the present application have at least the following technical effects:

[0072] The embodiment of the present application obtains a database of cultural and creative works and establishes user portraits of recommended interests for multiple cultural and creative works. These portraits are based on the identification of interested users and the extraction of user features of historical display records. For low-frequency cultural and creative works, compensation is made by mining user portraits of similar works. Subsequently, a user portrait of the participating users is established, and common features are identified with the recommended interest user portraits to obtain the recommendation probability of each cultural and creative work. After that, works with a recommendation probability greater than a first preset probability are extracted to form a first collection of works, and similarity clustering analysis is performed on them. A work similarity analysis network is constructed through a twin network, and the works are compared pairwise to identify similarities, and works with high similarity are clustered to generate similar work distribution indicators. Based on the similar work distribution indicators, elimination analysis is performed to optimize the first collection of works. Then, the number of display positions of the display module is obtained. If it is less than or equal to the number of works in the optimized collection, the collection is assigned a value based on the recommendation probability to form a display probability set. With the number of display positions as a constraint, the collection is randomly selected based on the display probability set, and the work display management is performed through the display module. If the number of display positions is greater than the number of works in the optimized collection, a second collection of works with a recommendation probability greater than the second preset probability is extracted from the database, and similar works are eliminated and analyzed to obtain a second optimized collection of works. If there are still too many display positions, the second preset probability is reduced and the process is repeated until the display position requirements are met. These technical effects jointly solve the technical problems in the existing technology that the display is affected by subjective factors and no accurate recommendation is made, resulting in poor display effects, and realize the automatic screening, recommendation and display management of cultural and creative works through artificial intelligence technology, and improve the display effect and management efficiency.

[0073] Embodiment 2

[0074] Based on the same inventive concept as the method for displaying and managing cultural and creative works based on artificial intelligence in the aforementioned embodiment, Figure 2 As shown, the present application provides a cultural and creative works display management system based on artificial intelligence, the system comprising:

[0075] Database acquisition component 1: the database acquisition component 1 is used to acquire a cultural and creative work database, wherein the cultural and creative work database includes a plurality of cultural and creative works, and establish a plurality of recommended interest user portraits of the plurality of cultural and creative works;

[0076] Portrait building component 2: the portrait building component 2 is used to build a portrait of the exhibitor user;

[0077] Feature recognition component 3: the feature recognition component 3 is used to identify common features of the exhibitor user portrait and the multiple recommended interest user portraits, and obtain multiple recommendation probabilities of the multiple cultural and creative works;

[0078] Similarity clustering component 4: the similarity clustering component 4 is used to extract a first collection of works whose recommendation probability is greater than a first preset probability, and perform work similarity clustering on the first collection of works to generate a similar work distribution index;

[0079] Elimination analysis component 5: the elimination analysis component 5 is used to perform elimination analysis of similar works based on the similar works distribution index, delete the eliminated works from the first work collection, and obtain a first optimized work collection;

[0080] Quantity statistics component 6: the quantity statistics component 6 is used to obtain the display location distribution map of the display module and count the number of display locations;

[0081] Probability assignment component 7: the probability assignment component 7 is used to determine whether the number of display positions is less than or equal to the number of first works in the first optimized work set, and if so, assign a recommendation probability to the first optimized work set based on the multiple recommendation probabilities to obtain a first display probability set;

[0082] Random extraction component 8: the random extraction component 8 is used to randomly extract the first optimized work set based on the first display probability set and the number of display positions as a constraint, and to display and manage the extracted works through the display module.

[0083] Furthermore, the database acquisition component 1 is also used to execute the following method:

[0084] Collecting historical display records of the plurality of cultural and creative works to identify interested users, and obtaining a plurality of interested user sets;

[0085] Extracting user features from the multiple sets of interested users to obtain multiple user feature sets;

[0086] The multiple user feature sets are centrally merged with similar features to obtain the multiple recommended interest user portraits.

[0087] Furthermore, the database acquisition component 1 is also used to execute the following method:

[0088] Extracting a first user feature set from the multiple user feature sets, and performing a similarity comparison on any two user features in the first user feature set to generate multiple first feature similarities;

[0089] Clustering user features whose first feature similarity is greater than a preset similarity to obtain multiple cluster feature sets;

[0090] Calculate clustering indices of multiple clustering feature sets, merge features of clustering feature sets whose clustering indices are greater than preset indices, obtain a first recommended interest user portrait and add it to the multiple recommended interest user portraits, wherein the clustering indices include a cluster quantity index and a cluster distance index.

[0091] Furthermore, the database acquisition component 1 is also used to execute the following method:

[0092] Identifying display frequency information of historical display records of the plurality of cultural and creative works;

[0093] Extracting low-frequency cultural and creative works whose display frequency is less than a preset frequency based on the display frequency information;

[0094] Mining similar works of the low-frequency literary and creative works to identify user portraits of similar works, and obtaining reference user portraits;

[0095] The reference user portrait is used to compensate the recommended interest user portrait of the low-frequency cultural and creative works.

[0096] Furthermore, the feature recognition component 3 is also used to perform the following method:

[0097] The exhibitor user portrait includes multiple user features of multiple users;

[0098] The multiple user features are compared with the multiple recommended interest user portraits, the proportions of the multiple user features falling into the multiple recommended interest user portraits are identified, and the multiple recommendation probabilities are generated.

[0099] Furthermore, the similarity clustering component 4 is also used to perform the following method:

[0100] Enumerate and combine the works in the first collection of works in pairs to obtain multiple combinations of works;

[0101] Building a work similarity analysis network based on the twin network, performing similarity identification on the plurality of work combinations, and obtaining the similarities of the plurality of works;

[0102] Cluster the works whose work similarity is greater than the work similarity threshold, and use the average work similarity in the clustering results to identify the work similarity;

[0103] The similar works distribution index is generated based on the clustering result and the works similarity identifier.

[0104] Furthermore, the probability assignment component 7 is also used to perform the following method:

[0105] If the number of display positions is greater than the number of first works in the first optimized collection of works, extracting a second collection of works with a probability greater than a second preset probability from the cultural and creative works database, wherein the second preset probability is less than the first preset probability;

[0106] Performing similar works elimination analysis on the second collection of works to obtain a second optimized collection of works;

[0107] Determine whether the number of display positions is less than the number of second works in the second optimized work set. If not, update the second preset probability according to a preset reduction step, and repeat the similar work elimination analysis until the number of display positions is less than the number of works in the optimized work set.

[0108] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0109] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0110] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for displaying and managing cultural and creative works based on artificial intelligence, characterized in that: The method comprises: Acquire a cultural and creative work database, wherein the cultural and creative work database includes a plurality of cultural and creative works, and establish a plurality of recommended interest user portraits of the plurality of cultural and creative works; Establish a user profile of exhibitors; Identify common features of the exhibitor user portrait and the multiple recommended interest user portraits to obtain multiple recommendation probabilities of the multiple cultural and creative works; Extracting a first collection of works whose recommendation probability is greater than a first preset probability, and performing work similarity clustering on the first collection of works to generate a similar work distribution index; Based on the similar works distribution index, performing similar works elimination analysis, deleting eliminated works from the first collection of works, and obtaining a first optimized collection of works; Obtain the display location distribution map of the display module and count the number of display locations; Determine whether the number of display positions is less than or equal to the number of first works in the first optimized work set, and if so, assign a recommendation probability to the first optimized work set based on the multiple recommendation probabilities to obtain a first display probability set; Taking the number of display positions as a constraint, the first optimized portfolio is randomly selected based on the first display probability set, and the selected works are displayed and managed through a display module.

2. The method according to claim 1, characterized in that Determining whether the number of the display positions is less than the number of first works in the first optimized work set further includes: If the number of display positions is greater than the number of first works in the first optimized collection of works, extracting a second collection of works with a probability greater than a second preset probability from the cultural and creative works database, wherein the second preset probability is less than the first preset probability; Performing similar works elimination analysis on the second collection of works to obtain a second optimized collection of works; Determine whether the number of display positions is less than the number of second works in the second optimized work set. If not, update the second preset probability according to a preset reduction step, and repeat the similar work elimination analysis until the number of display positions is less than the number of works in the optimized work set.

3. The method according to claim 1, characterized in that Establishing multiple recommended interest user portraits for the multiple cultural and creative works, including: Collecting historical display records of the plurality of cultural and creative works to identify interested users, and obtaining a plurality of interested user sets; Extracting user features from the plurality of interest user sets to obtain a plurality of user feature sets; The multiple user feature sets are centrally merged with similar features to obtain the multiple recommended interest user portraits.

4. The method according to claim 3, characterized in that The multiple user feature sets are centrally merged to obtain similar features, including: Extracting a first user feature set from the multiple user feature sets, and performing a similarity comparison on any two user features in the first user feature set to generate multiple first feature similarities; Clustering user features whose first feature similarity is greater than a preset similarity to obtain multiple cluster feature sets; Calculate clustering indices of multiple clustering feature sets, merge features of clustering feature sets whose clustering indices are greater than preset indices, obtain a first recommended interest user portrait and add it to the multiple recommended interest user portraits, wherein the clustering indices include a cluster quantity index and a cluster distance index.

5. The method according to claim 1, characterized in that Common features of the exhibitor user portrait and the multiple recommended interest user portraits are identified to obtain multiple recommendation probabilities of the multiple cultural and creative works, including: The exhibitor user portrait includes multiple user features of multiple users; The multiple user features are compared with the multiple recommended interest user portraits, the proportions of the multiple user features falling into the multiple recommended interest user portraits are identified, and the multiple recommendation probabilities are generated.

6. The method according to claim 4, characterized in that Establishing a plurality of recommended interest user portraits of the plurality of cultural and creative works further includes: Identifying display frequency information of historical display records of the plurality of cultural and creative works; Extracting low-frequency cultural and creative works whose display frequency is less than a preset frequency based on the display frequency information; Mining similar works of the low-frequency literary and creative works to identify user portraits of similar works, and obtaining reference user portraits; The reference user portrait is used to compensate the recommended interest user portrait of the low-frequency cultural and creative works.

7. The method according to claim 1, characterized in that The first collection of works is clustered according to the similarity of the works to generate a distribution index of similar works, including: Enumerate and combine the works in the first collection of works in pairs to obtain multiple combinations of works; Building a work similarity analysis network based on the twin network, performing similarity identification on the plurality of work combinations, and obtaining the similarities of the plurality of works; Cluster the works whose work similarity is greater than the work similarity threshold, and use the average work similarity in the clustering results to identify the work similarity; The similar works distribution index is generated based on the clustering result and the works similarity identifier.

8. The cultural and creative works display management system based on artificial intelligence is characterized by: The system is used to implement the steps of the method for displaying and managing cultural and creative works based on artificial intelligence as described in any one of claims 1 to 7, wherein the system comprises: Database acquisition component: acquiring a cultural and creative work database, wherein the cultural and creative work database includes a plurality of cultural and creative works, and establishing a plurality of recommended interest user portraits of the plurality of cultural and creative works; Portrait building component: build the portrait of the exhibitors; Feature recognition component: performing common feature recognition on the exhibition user portrait and the multiple recommended interest user portraits to obtain multiple recommendation probabilities of the multiple cultural and creative works; Similarity clustering component: extracting a first collection of works whose recommendation probability is greater than a first preset probability, and performing work similarity clustering on the first collection of works to generate a similar work distribution index; Elimination analysis component: based on the similar works distribution index, performing similar works elimination analysis, deleting eliminated works from the first work collection, and obtaining a first optimized work collection; Quantity statistics component: obtain the display location distribution map of the display module and count the number of display locations; Probability assignment component: determining whether the number of display positions is less than or equal to the number of first works in the first optimized work set, and if so, assigning recommendation probabilities to the first optimized work set based on the multiple recommendation probabilities to obtain a first display probability set; Random extraction component: taking the number of display positions as a constraint, randomly extracting the first optimized portfolio based on the first display probability set, and displaying and managing the extracted works through a display module.

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