AI-Powered Content Creation and Recommendation System

Abstract

The present invention relates to an AI-based content creation and recommendation system. The system of the present invention includes an AI content creation unit capable of automatically generating text content, image content, and video content using AI; a user preference analysis unit that analyzes user tastes or preferences using machine learning; a personalized content recommendation unit that provides user-customized content based on analyzed preferences; a recommendation algorithm used by the personalized content recommendation unit; and a real-time data learning unit that optimizes the recommendation algorithm by reflecting real-time data learning results into the recommendation algorithm. Accordingly, content creation time can be shortened, user engagement can be enhanced, and content marketing efficiency can be improved.

Description

Technology Field

[0001] The present invention relates to AI-based content creation and recommendation technology, and more specifically to an AI-utilized content creation and recommendation system that can be applied to improve the productivity and efficiency of content in fields such as news, entertainment, social media, and e-commerce.

[0002] The present invention relates to a technology that automatically generates content using AI and recommends optimal content by analyzing user preferences. Background Technology

[0003] In various service sectors where content is provided, technology is required to rapidly produce content and recommend suitable content to users.

[0004] Conventional content creation and recommendation methods need improvement in terms of content creation time, user engagement, and content marketing efficiency. The problem to be solved

[0005] The objective of the present invention is to provide an AI-based content creation and recommendation system that can shorten content creation time and reduce production costs through an AI automatic generation function.

[0006] Another objective of the present invention is to improve user engagement and customer satisfaction by analyzing user preferences using machine learning and providing customized recommended content.

[0007] Another objective of the present invention is to improve content marketing efficiency by optimizing the recommendation algorithm through real-time data learning. means of solving the problem

[0008] To solve the above problem, an AI-based content creation and recommendation system (100) according to one embodiment of the present invention includes an AI content creation unit (110), a user preference analysis unit (120), a customized content recommendation unit (130), a real-time data learning unit (140), and a recommendation algorithm (150).

[0009] The AI ​​content generation unit (110) can automatically generate text content (111), image content (112), and video content (113) using AI.

[0010] The user preference analysis unit (120) analyzes the user's taste or preference using machine learning.

[0011] The customized content recommendation unit (130) provides customized content to the user using the analysis results of the user preference analysis unit (120) and the recommendation algorithm (150).

[0012] The real-time data learning unit (140) performs real-time data learning and reflects the learning results in the recommendation algorithm (150) to optimize the recommendation algorithm (150). Effects of the invention

[0013] According to the present invention, content production time can be shortened and production costs reduced through an AI automatic generation function.

[0014] According to the present invention, user engagement and customer satisfaction can be improved by providing customized recommended content.

[0015] According to the present invention, the efficiency of a company's content marketing can be improved through data-based recommendation optimization. Brief explanation of the drawing

[0016] FIG. 1 is a block diagram showing the overall configuration of an AI-based content creation and recommendation system according to one embodiment of the present invention. FIG. 2 is a diagram showing a configuration in which an AI content generation unit according to an embodiment of the present invention generates text content, image content, and video content and interacts with a customized content recommendation unit. FIG. 3 is a diagram showing the interconnected configuration of user preference analysis, customized content recommendation, real-time data learning, and recommendation algorithm optimization according to one embodiment of the present invention. Specific details for implementing the invention

[0017] Embodiments of the present invention will be described in detail below with reference to the attached drawings. The present invention relates to an AI-based content creation and recommendation system that automatically creates content using AI, analyzes user tastes or preferences to recommend content suitable for the user, and continuously optimizes a recommendation algorithm by learning real-time data generated during the operation of the system. In this specification, the term "content" may include information, advertisements, guides, posts, product descriptions, video materials, promotional materials, or service-related materials that can be provided to a user, and may include text content (111), image content (112), and video content (113) depending on the format of expression. Furthermore, in the present invention, automatic content generation using AI means that content in the form of text, images, or videos can be automatically generated according to the service purpose, field of provision, or situation of content provision, even if a person does not directly write or create the entire content. For convenience of explanation, embodiments of the present invention are described with reference to the components shown in FIGS. 1 to 3, but each component is not limited to being a physically independent device only, and may be implemented as a configuration that operates functionally separated within a single system or as a configuration combining multiple processing functions.

[0018] Referring to FIG. 1, an AI-based content creation and recommendation system (100) according to an embodiment of the present invention includes an AI content creation unit (110), a user preference analysis unit (120), a customized content recommendation unit (130), a real-time data learning unit (140), and a recommendation algorithm (150). The AI ​​content creation unit (110) performs the function of creating content that can be provided to a user based on AI, and the user preference analysis unit (120) performs the function of analyzing the user's taste or preference using machine learning. The customized content recommendation unit (130) performs the function of selecting or providing content suitable for the user by using the content created by the AI ​​content creation unit (110) and the analysis results of the user preference analysis unit (120). The real-time data learning unit (140) learns the recommendation results of the customized content recommendation unit (130), data generated during system operation, or data regarding user reactions, so that it can be used to improve the recommendation algorithm (150). The recommendation algorithm (150) functions as a recommendation criterion, recommendation procedure, or recommendation judgment means used for recommending user-customized content, and the suitability of the content provided to the user can be improved by reflecting the learning results of the real-time data learning unit (140).

[0019] The AI ​​content generation unit (110) can automatically generate at least one of text content (111), image content (112), and video content (113) when content production is required. For example, text content (111) may include content composed of text, such as news-style sentences, explanatory text, notices, posts, promotional phrases, and product or service introduction phrases. Image content (112) may include visual content that can be used for content provision screens, posts, advertisements, informational materials, or product / service descriptions. Video content (113) may include promotional videos, informational videos, short video content, service introduction videos, or content provided to users in a viewing format. Depending on the field or purpose for which the content is provided, the AI ​​content generation unit (110) may generate only one content format, or it may generate text content (111), image content (112), and video content (113) together for the same or related topics. Accordingly, the need for content creators to manually produce each content format separately may be reduced, and service providers may obtain content of various formats more quickly.

[0020] Referring to FIG. 2, text content (111), image content (112), and video content (113) generated by the AI ​​content generation unit (110) can be linked with the customized content recommendation unit (130). That is, the content generated by the AI ​​content generation unit (110) is not merely stored or provided in bulk, but can be utilized as content to be recommended to provide content suitable for the user. For example, when multiple types of content are generated in a specific service, the customized content recommendation unit (130) can determine whether to prioritize providing text-centered content, image-centered content, or video-centered content to the user by considering the analysis results of the user preference analysis unit (120) and the recommendation algorithm (150). Additionally, even if the content has the same content, different formats of content may be more suitable depending on the user's taste or service usage characteristics, so the customized content recommendation unit (130) can select and provide content suitable for the user from among the multiple generated contents. In this way, by linking the AI ​​content generation unit (110) and the customized content recommendation unit (130), the automatic content generation function and the user-customized provision function can be combined within a single system.

[0021] The user preference analysis unit (120) analyzes the user's taste or preference using machine learning. The user preference analysis unit (120) can analyze the user's preference based on information such as the type of content the user has shown interest in, the content the user has selected or viewed, the content fields the user has repeatedly used, the format of the content the user has responded to, and the user's service usage trends. Here, user preference may include the degree of interest in a specific content field, the preferred content format among text, images, and videos, the reaction trend to a specific topic, and the interest trend to a specific service or product group. The user preference analysis unit (120) does not view the user's taste as fixed, but analyzes it as something that can change depending on the user's usage situation or recent reactions. Therefore, the customized content recommendation unit (130) can use the analysis results of the user preference analysis unit (120) to provide content that matches the individual user's taste or preference, rather than simply providing general popular content.

[0022] The customized content recommendation unit (130) provides customized content to the user by utilizing the content generated by the AI ​​content generation unit (110) and the analysis results of the user preference analysis unit (120). The customized content recommendation unit (130) can determine the content to be provided to the user among the recommended content using a recommendation algorithm (150), and can ensure that content suitable for the user's taste or preference is provided first. For example, if the user shows high interest in text content on a specific topic, the customized content recommendation unit (130) can provide text content related to that topic first. If the user shows a high response to content in the form of images or videos, the customized content recommendation unit (130) can provide image content (112) or video content (113) first. In addition, if the user's preference changes, the customized content recommendation unit (130) can change the type or order of the provided content by reflecting the changed analysis results. In this way, the customized content recommendation unit (130) can efficiently provide content suitable for the user by using the results of automatic content generation and user preference analysis together, compared to cases where content creation and content recommendation are operated separately.

[0023] Referring to FIG. 3, the recommendation results of the customized content recommendation unit (130) and real-time data generated during system operation can be provided to the real-time data learning unit (140). The real-time data may include whether the user has selected the recommended content, the extent to which the user has used the content, the user's reaction to a specific type of content, the results of using the recommended content, and changes in content usage that occur during the service provision process. The real-time data learning unit (140) can learn this data and reflect it in the recommendation algorithm (150). The recommendation algorithm (150) can update or optimize the criteria for selecting or recommending content to be provided to the user based on the learning results of the real-time data learning unit (140). Accordingly, the recommendation algorithm (150) can be continuously improved based on user reactions or changes in service usage, rather than relying solely on past fixed criteria. As a result, the customized content recommendation unit (130) can provide content more suitable to the user by using the analysis results of the user preference analysis unit (120) and the optimized recommendation algorithm (150) together.

[0024] To describe the overall operation of the system (100) of the present invention, first, an AI content generation unit (110) automatically generates at least one of text content (111), image content (112), and video content (113). Next, a user preference analysis unit (120) analyzes the user's taste or preference using machine learning. Subsequently, a customized content recommendation unit (130) utilizes the generated content and the analysis results of the user preference analysis unit (120) and provides content suitable for the user based on a recommendation algorithm (150). After the content is provided, the recommendation results or real-time data generated during system operation can be learned by a real-time data learning unit (140), and the learning results can be reflected in the recommendation algorithm (150) and used to update or optimize the recommendation algorithm (150). According to this configuration, during the content production process, production time and costs can be reduced through the AI ​​automatic generation function; during the content provision process, user engagement and customer satisfaction can be improved through user-customized recommendations; and during the system operation process, content marketing efficiency can be improved by continuously improving the recommendation algorithm through real-time data learning. The system (100) of the present invention can be used in various service fields requiring content creation and recommendation, such as news, entertainment, social media, and e-commerce. Industrial applicability

[0025] The present invention can be applied to news services, entertainment services, social media services, e-commerce services, etc., that require content creation and recommendation. Explanation of the symbols

[0026] 100: AI-powered content creation and recommendation system 110: AI Content Generation Unit 111: Text Content 112: Image content 113: Video content 120: User Preference Analysis Unit 130: Personalized Content Recommendation Unit 140: Real-time Data Learning Unit 150: Recommendation Algorithm

Claims

Claim 1 An AI-based content creation and recommendation system comprising: an AI content creation unit capable of automatically generating text content, image content, and video content using AI; a user preference analysis unit that analyzes a user's taste or preference using machine learning; a customized content recommendation unit that provides customized content to the user using the analysis results of the user preference analysis unit; a recommendation algorithm used by the customized content recommendation unit; and a real-time data learning unit that performs real-time data learning and optimizes the recommendation algorithm by reflecting the learning results in the recommendation algorithm. Claim 2 The AI-based content creation and recommendation system according to claim 1, wherein the AI ​​content generation unit is capable of generating text content, image content, and video content. Claim 3 The AI-based content creation and recommendation system according to claim 1, wherein the user preference analysis unit analyzes the user's taste or preference using machine learning. Claim 4 The AI-based content creation and recommendation system according to claim 1, wherein the customized content recommendation unit provides content based on the user's taste or preference analyzed by the user preference analysis unit and the recommendation algorithm. Claim 5 The AI-based content creation and recommendation system according to claim 1, wherein the real-time data learning unit updates or optimizes the recommendation algorithm through real-time data learning. Claim 6 The AI-powered content creation and recommendation system according to claim 1, characterized in that the system is applied to content creation and recommendation in at least one field among news, entertainment, social media and e-commerce.

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