Intelligent film watching recommendation system and method

Abstract

The invention discloses an intelligent film watching recommendation system and method, and the system comprises a user behavior analysis module which is used for collecting and analyzing the behavior data of a user in a television list square and a special square, and constructing a user portrait; the real-time data processing module is used for performing real-time analysis on the user behavior data and dynamically updating a recommendation strategy; the personalized recommendation module is used for generating personalized television list and thematic content recommendation based on the user portrait and the real-time analysis result; and the content auditing and scale combining block is used for automatically detecting and filtering non-compliant contents by utilizing a machine learning model, and introducing an artificial rechecking mechanism to the uncertain contents.

Description

Technical Field

[0001] This invention relates to the field of smart TVs and video content distribution, specifically to a smart movie recommendation system and method. Background Technology

[0002] Currently, content distribution on smart TVs and video platforms primarily relies on manually curated recommendation lists and themed content. This method has the following problems:

[0003] Lack of personalization

[0004] In traditional content distribution models, platforms primarily rely on human editors who, based on their experience and simple rules, push out generic lists and themed content to users. While this approach can reach a broad user base to some extent, it fails to delve into users' personalized preferences. With the booming development of smart TVs and video platforms, the user base is increasingly large and diverse, and content selection is no longer limited to mainstream popular works, but rather leans towards film and television resources that match their unique tastes. However, existing content recommendation systems have significant shortcomings in personalization, struggling to provide users with accurately tailored content recommendations based on their viewing history, preferences, and real-time behavior. For example, a user might have a particular fondness for niche independent films, but the platform may fail to accurately capture this preference, making it difficult for them to quickly find suitable works among the vast amount of content.

[0005] Untimely updates

[0006] Updating content through manual editing is often a slow and tedious process. Editorial teams need to spend a significant amount of time and effort to organize, filter, and publish new recommendations. In today's information-saturated world, user viewing behavior changes rapidly, popular film and television content evolves at an extremely fast pace, and user interests shift swiftly. For example, a newly released hit movie may generate considerable discussion and enthusiasm among audiences in a short period. However, in such cases, manually edited recommendations often struggle to be updated in a timely manner, failing to immediately recommend the movie to users with relevant interests. This lag significantly reduces the relevance of recommended content to users' current interests, directly impacting the user's viewing experience and leading to decreased user satisfaction with the platform.

[0007] Low review efficiency

[0008] Content moderation on smart TV platforms largely relies on human teams. With the increasing number of applications and the growing diversity of content on smart TVs, the workload for moderation is growing exponentially. Human moderation is not only inefficient but also prone to errors or omissions. For example, video clips containing vulgar content might slip onto the platform due to human oversight, harming user experience and potentially damaging the platform's reputation.

[0009] Limitations of user experience

[0010] Smart TV users typically expect a smooth and convenient viewing experience. However, existing content distribution models have many shortcomings in terms of user experience. For example, the accuracy of content recommendations is not high, causing users to spend a lot of time searching for the content they want to watch across different channels and apps; the updates to recommended content are not timely, making it difficult for users to get the latest film and television recommendations immediately; in addition, some smart TV platforms lack sufficient interactivity in content display, preventing users from filtering and sorting recommended content according to their preferences. Summary of the Invention

[0011] The purpose of this invention is to provide an intelligent movie recommendation system and method. This system optimizes the content distribution mechanism of TV ranking squares and special topic squares through key technologies such as user movie viewing behavior analysis, real-time data processing, personalized recommendations, and intelligent content review, thereby improving user experience and platform operation efficiency.

[0012] To achieve the above objectives, the present invention adopts the following technical solution:

[0013] An intelligent movie recommendation system includes:

[0014] The user behavior analysis module is used to collect and analyze user behavior data in the TV ranking square and special topic square to build user profiles;

[0015] The real-time data processing module is used to analyze user behavior data in real time and dynamically update the recommendation strategy.

[0016] The personalized recommendation module generates personalized TV lists and special content recommendations based on user profiles and real-time analysis results.

[0017] The content moderation and compliance module uses machine learning models to automatically detect and filter non-compliant content, and introduces a manual review mechanism for uncertain content.

[0018] In some embodiments, the user behavior analysis module includes:

[0019] The data acquisition unit collects user viewing behavior data through an application programming interface;

[0020] The data preprocessing unit cleans, standardizes, and removes duplicates from the data.

[0021] The feature extraction unit extracts user behavior features, content features, and contextual features;

[0022] The user profile building unit combines user registration information to build and dynamically update user profiles.

[0023] In some embodiments, the real-time data processing module includes:

[0024] The stream processing framework unit receives user behavior data streams;

[0025] The real-time analysis algorithm unit extracts real-time features based on sliding window or event-triggered mechanisms;

[0026] It is recommended to update the trigger unit and recalculate the priority of the recommendation list based on real-time features.

[0027] In some embodiments, the personalized recommendation module includes:

[0028] The recommendation model training unit employs collaborative filtering algorithms, content recommendation algorithms, or deep learning models.

[0029] The recommendation strategy formulation unit formulates strategies based on viewing history, similar users, popular content, or context.

[0030] The recommendation result generation unit outputs a recommendation list with relevance scores;

[0031] The recommendation results display unit shows the sorted recommended content through the user interface.

[0032] In some embodiments, the content moderation and compliance module includes:

[0033] The audit model training unit trains a support vector machine or deep learning model based on labeled samples.

[0034] The content feature extraction unit extracts keywords, image, and audio features;

[0035] The automated review process unit automatically categorizes content compliance.

[0036] The manual review mechanism unit conducts a second review of uncertain content in the model.

[0037] This embodiment also provides an intelligent movie recommendation method, including:

[0038] Collect user behavior data in the TV rankings and special topics sections to build dynamically updated user profiles;

[0039] Stream processing technology is used to analyze user behavior data in real time and dynamically adjust recommendation strategies.

[0040] Based on user profiles and real-time analysis results, personalized TV lists and special content recommendations are generated.

[0041] Machine learning models are used to automatically filter out non-compliant content, and uncertain content is manually reviewed.

[0042] In some embodiments, building a user profile includes:

[0043] Data on viewing duration, number of pauses, and search keyword behavior are collected through application programming interfaces (APIs).

[0044] The data is cleaned, standardized, and deduplicated.

[0045] Extract user behavior features, content features, and contextual features;

[0046] User profiles are built and dynamically updated based on user registration information.

[0047] In some embodiments, the dynamically adjusted recommendation strategy includes:

[0048] Access user behavior data streams through a stream processing framework;

[0049] Real-time behavioral characteristics are calculated based on sliding windows or event-triggered mechanisms;

[0050] The priority of the recommendation list is recalculated based on real-time features.

[0051] In some embodiments, generating personalized recommendations includes:

[0052] The recommendation model is trained using collaborative filtering algorithms, content recommendation algorithms, or deep learning models.

[0053] Develop diverse recommendation strategies based on user behavior characteristics;

[0054] Generate a recommendation list with relevance scores and display it in order of priority.

[0055] In some embodiments, filtering non-compliant content includes:

[0056] A content moderation model was trained based on labeled samples.

[0057] Extract multi-dimensional features from the content to be reviewed;

[0058] Compliance is automatically categorized using a model;

[0059] For uncertain content, initiate manual review and provide feedback to optimize the model.

[0060] The beneficial effects that the intelligent movie recommendation system and method disclosed in this application may bring include, but are not limited to:

[0061] The implementation of this technical solution has significantly improved the personalization level and content update speed of the intelligent movie recommendation system, increased content review efficiency, reduced operating costs, and enhanced user experience and the platform's market competitiveness. Attached Figure Description

[0062] Figure 1 This is a data processing flowchart for data collection and user profile construction in an embodiment of this application. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0064] Conversely, this application covers any substitutions, modifications, equivalent methods, and schemes made within the spirit and scope of this application as defined in the claims. Furthermore, to provide the public with a better understanding of this application, certain specific details are described in detail below. However, this application can be fully understood by those skilled in the art even without these detailed descriptions.

[0065] The following will provide a detailed description of a method for identifying the branch types of a three-dimensional fruit tree structure, as described in the embodiments of this application. Figure 1 As shown,

[0066] This embodiment proposes an intelligent recommendation system and method based on user viewing behavior, and the technical solution is as follows:

[0067] User behavior analysis module

[0068] The user behavior analysis module is the foundation of this system, responsible for collecting and analyzing user behavior data in the TV ranking and special topic sections, providing data support for personalized recommendations. The specific implementation steps of this module are as follows:

[0069] Data collection:

[0070] Data collection method: User viewing behavior data is collected in real time through the application programming interface (API) of smart TVs or video platforms. This data includes, but is not limited to, viewing duration, number of pauses, fast forward / rewind operations, likes / favorites, search keywords, and comment content.

[0071] Data types: The collected data can be divided into structured data (such as viewing time and number of pauses) and unstructured data (such as comment content and search keywords). Structured data can be used directly for analysis, while unstructured data requires further processing and analysis.

[0072] Data storage: The collected data will be stored in a distributed database or data warehouse in a structured or semi-structured form for subsequent analysis and processing.

[0073] Data preprocessing:

[0074] Data cleaning: Cleaning the collected raw data, removing noisy data, filling in missing values, correcting erroneous data, etc., to ensure data quality and integrity.

[0075] Data standardization: Standardizing data through normalization, encoding, and other processes to ensure that data from different sources can be processed uniformly. For example, timestamps are standardized to a uniform time format, and rating data is normalized to the [0, 1] interval.

[0076] Data deduplication: Remove duplicate data to prevent the same user behavior from being recorded multiple times.

[0077] Feature extraction:

[0078] User behavior features: Key features are extracted from the preprocessed data, such as user viewing preferences, viewing time periods, content type preferences, and playback behavior patterns. These features will be used to construct a user behavior feature vector.

[0079] Content features: Extract content features such as type, actors, directors, and tags so that subsequent recommendation models can better understand the attributes of the content.

[0080] Contextual features: Extract contextual information about user viewing behavior, such as viewing time, viewing device, and viewing location. This information can be used for more accurate recommendations.

[0081] User profile building:

[0082] User profile content: Based on the extracted feature vectors and combined with the user's registration information (such as age, gender, region, etc.), a detailed user profile is constructed. The user profile includes multi-dimensional information such as the user's interest tags, preference weights, and behavioral patterns.

[0083] Dynamic updates: User profiles will be dynamically updated based on users' real-time behavioral data to ensure the timeliness and accuracy of the profiles.

[0084] Real-time data processing module

[0085] The real-time data processing module employs stream processing technology to analyze and respond to user behavior data in real time, rapidly adjusting recommendation strategies. The specific implementation steps of this module are as follows:

[0086] Stream processing framework selection:

[0087] Framework Selection: Established stream processing frameworks (such as Apache Kafka and Apache Flink) will be used to build the real-time data processing platform. These frameworks feature high throughput, low latency, and high scalability, meeting the real-time processing needs of massive amounts of data.

[0088] Framework configuration: Configure the parameters of the stream processing framework according to system requirements, such as data buffer size, number of parallel tasks, data partitioning strategy, etc., to optimize performance.

[0089] Data stream access:

[0090] Data access: User behavior data is fed into the processing platform in the form of a stream. The data stream can be transmitted through message queues (such as Kafka) to ensure the real-time nature and continuity of the data.

[0091] Data formatting: The incoming data is formatted to ensure that it can be correctly parsed and processed by the stream processing framework.

[0092] Real-time analysis algorithm:

[0093] Algorithm Design: Design a real-time analysis algorithm for rapid processing of streaming data. This algorithm can utilize mechanisms such as sliding windows and event triggering to extract and analyze user behavior data in real time.

[0094] Feature calculation: Calculates real-time behavioral characteristics of users, such as current viewing duration, current content type, and current viewing frequency. These characteristics will be used to update user profiles and recommendation strategies in real time.

[0095] Anomaly detection: Real-time detection of abnormal user behavior, such as sudden and frequent content switching or prolonged inactivity. These abnormal behaviors may require special handling.

[0096] Triggering recommended update:

[0097] Update Trigger: Based on real-time analysis results, the recommendation system's update mechanism is triggered. The recommendation system will recalculate the priority of recommended content based on real-time features and adjust the recommendation list accordingly.

[0098] Dynamic adjustment: The recommendation system will dynamically adjust the recommendation strategy based on the user's real-time behavior to ensure that the recommended content is highly relevant to the user's current interests.

[0099] Personalized recommendation module

[0100] The personalized recommendation module provides users with accurate TV show lists and featured content recommendations based on user behavior analysis. The specific implementation steps of this module are as follows:

[0101] Recommended model training:

[0102] Algorithm selection: Collaborative filtering algorithm, content recommendation algorithm and machine learning technology (such as deep learning model) are used to train a personalized recommendation model by combining user profiles and behavioral data.

[0103] Data preparation: Prepare training data, including user behavior data, content feature data, user profile data, etc.

[0104] Model training: The recommendation model is trained using machine learning algorithms to learn patterns and characteristics of user behavior. During training, techniques such as cross-validation can be used to evaluate the model's performance and generalization ability.

[0105] Recommended strategy formulation:

[0106] Strategy Design: Develop diverse recommendation strategies based on user behavior characteristics and preferences. Recommendation strategies can include those based on viewing history, similar users, popular content, and context.

[0107] Strategy optimization: Dynamically adjust the recommendation strategy based on user feedback and system performance data to optimize recommendation performance.

[0108] Recommendation results generation:

[0109] Results Calculation: Based on the recommendation model and strategy, a personalized recommendation list is generated. The recommendation list will include TV rankings and special topics, with each recommended item accompanied by a relevance score and a reason for recommendation.

[0110] Results Ranking: The recommended results are sorted to ensure that the most relevant content is shown to users first. The ranking algorithm can be based on a comprehensive calculation based on multiple dimensions such as user ratings, content popularity, and user preferences.

[0111] Recommendation results display:

[0112] Interface Design: The generated recommendation list will be displayed to users through the user interface of a smart TV or video platform. Recommended content can be presented in the form of images and text, video thumbnails, etc., ensuring that the presentation is intuitive and easy to use.

[0113] Interaction optimization: Design a user-friendly interface that allows users to access and switch recommended content through simple operations (such as clicking and swiping).

[0114] Content moderation and compliance module

[0115] The content moderation and compliance module utilizes machine learning technology to automatically detect and filter non-compliant content, ensuring the safety and legality of recommended content. The specific implementation steps of this module are as follows:

[0116] Audit model training:

[0117] Sample collection: Collect a large number of labeled compliant and non-compliant content samples, including different types of non-compliant content such as advertisements.

[0118] Model selection: Machine learning methods such as support vector machines (SVM) and deep learning models (such as convolutional neural networks, recurrent neural networks, etc.) are used to train the content review model.

[0119] Model training: By learning from sample data, the model can identify the characteristics and patterns of non-compliant content.

[0120] Content feature extraction:

[0121] Feature extraction: Feature extraction is performed on the content to be reviewed, including multi-dimensional features such as keywords, images, and audio. For example, keywords in video titles and descriptions are extracted through text analysis, key image features in video frames are extracted through image recognition, and background sound effects and voice information are extracted through audio analysis.

[0122] Feature processing: The extracted features are preprocessed, such as feature normalization and feature dimensionality reduction, so that the model can process them better.

[0123] Automated approval process:

[0124] Model Judgment: The extracted features are input into the review model to automatically determine whether the content is compliant. The model will classify and label the content based on the features and patterns learned during training.

[0125] Results processing: For content deemed non-compliant, the system will automatically filter or flag it to prevent it from being recommended to users.

[0126] Manual review mechanism:

[0127] Verification Mechanism: For content where the model is uncertain, a manual verification mechanism is introduced. Human reviewers will conduct a second review of this content to ensure the accuracy of the verification results.

[0128] Results feedback: The results of manual review will be fed back to the review model to further optimize model performance and improve the accuracy and recall rate of the review.

[0129] System integration and optimization

[0130] Integrating the above modules forms a complete intelligent movie recommendation system. The specific implementation steps of this module are as follows:

[0131] Interface design:

[0132] Interface Definition: Design the communication interfaces between modules to ensure seamless data transmission and interaction. Interface design should consider the principles of high cohesion and low coupling to improve the maintainability and scalability of the system.

[0133] Interface documentation: Write detailed interface documentation, including the input and output formats, calling methods, error handling, etc., to facilitate development and maintenance.

[0134] System testing:

[0135] Functional testing: Conduct comprehensive functional testing on the integrated system to ensure that all functions of the system are working properly, including user behavior data collection, real-time data analysis, personalized recommendations, content moderation, and other functions.

[0136] Performance testing: This involves conducting performance tests on the system to ensure its response speed and processing capacity under high concurrency and large data volumes. Performance testing may include throughput testing, latency testing, and resource utilization testing.

[0137] Stability testing: Conducting stability tests on the system to ensure its reliability during long-term operation. Stability testing may include stress testing, fatigue testing, etc.

[0138] Optimization and adjustments:

[0139] Algorithm optimization: Based on the test results, optimize the algorithms in the system, such as parameter tuning of the recommendation algorithm and performance optimization of the stream processing algorithm.

[0140] Parameter tuning: Adjust system configuration parameters, such as the buffer size of the stream processing framework and the hyperparameters of the recommendation model, to improve system performance.

[0141] Hardware optimization: Optimize hardware resource configuration based on system performance requirements, such as increasing the number of servers or upgrading storage devices.

[0142] Deployment and launch:

[0143] Deployment environment: Deploy the optimized system to the actual operating environment. During the deployment process, the high availability and disaster recovery capabilities of the system should be considered to ensure the continuity and stability of services.

[0144] Monitoring and Maintenance: After deployment, the system will be monitored in real time to promptly identify and address any issues that arise during system operation. Regular maintenance and optimization will also be performed to ensure the system's long-term stable operation.

[0145] System security and privacy protection

[0146] In the design and implementation of the intelligent movie recommendation system, system security and user privacy protection are of paramount importance. The specific implementation steps of this module are as follows:

[0147] Data encryption:

[0148] Encryption during transmission: Encryption protocols such as SSL / TLS are used to encrypt the transmission of user behavior data, preventing the data from being stolen or tampered with during transmission.

[0149] Storage encryption: User behavior data stored in the database is encrypted to ensure data security.

[0150] Access control:

[0151] User authentication: Implement a user authentication mechanism to ensure that only authorized users can access the system and data.

[0152] Access control: Manage access permissions for different user roles in the system, restrict user access to data and functions, and ensure data security and system stability.

[0153] Privacy protection:

[0154] Data anonymization: When processing user behavior data, anonymization technology is used to remove unnecessary personal information and protect user privacy.

[0155] Data minimization: Only collect and store the user data necessary to implement the recommendation function, avoiding excessive collection of user information.

[0156] Privacy Policy: Develop a clear privacy policy that explains to users how data is collected, used, and stored, and ensures that users understand and agree to how the data is used.

[0157] Security Audit:

[0158] Log recording: Record system operation logs and data access logs for security auditing and problem tracking.

[0159] Auditing mechanism: Regularly conduct security audits of the system to check for security vulnerabilities and privacy issues, and take timely measures to repair and improve them.

[0160] The implementation of this technical solution significantly improves the personalization level and content update speed of the intelligent movie recommendation system, increases content review efficiency, reduces operating costs, and enhances user experience and the platform's market competitiveness. Specifically, it includes:

[0161] Improve user experience

[0162] Personalized Recommendations: Through user viewing behavior analysis and personalized recommendation algorithms, the system can provide users with personalized TV show lists and featured content recommendations. This precise recommendation method replaces the traditional "one-size-fits-all" recommendation, better meeting the diverse needs of users and improving user satisfaction and loyalty to the platform.

[0163] Real-time updates: The real-time data processing module ensures the timeliness of recommended content. Users can receive the latest content recommendations related to their interests at any time during viewing, enhancing their viewing experience. For example, after a user watches a science fiction movie, the system can immediately update the recommendation list based on this action, recommending more similar science fiction films to the user.

[0164] Optimized interaction: Recommendation results are displayed through a carefully designed user interface, ensuring that the presented content is intuitive and easy to use. Users can easily browse recommended content and obtain detailed information through simple operations, enhancing the interaction between users and the platform.

[0165] Improve content update efficiency

[0166] Rapid Response: Employing stream processing technology to analyze user behavior data in real time, the system can quickly capture changes in user interests and adjust recommendation strategies accordingly. Compared to traditional manual editorial recommendation methods, this significantly shortens the content update cycle, ensuring that recommended content keeps pace with evolving user needs.

[0167] Dynamic Adjustment: The system dynamically updates the recommendation list based on the user's real-time behavior, ensuring that the recommended content remains highly relevant to the user's current interests. This not only improves the efficiency of content updates but also increases the opportunities for users to discover new content, further enhancing the user's viewing experience.

[0168] Improve content review efficiency

[0169] Automated content moderation: Utilizing machine learning technology, the system can quickly and accurately identify and filter non-compliant content, such as violence and advertisements. Compared to manual moderation, this significantly improves efficiency, reduces costs, and lowers the risk of errors.

[0170] Precise Identification: By training machine learning models, the system can accurately identify the characteristics and patterns of non-compliant content, improving the accuracy and consistency of review. This helps create a healthy and safe viewing environment and protects users' rights.

[0171] Reduce platform operating costs

[0172] Reduced manual labor: Automated recommendation and review functions reduce reliance on human editors and reviewers, lowering labor costs. The platform can then allocate more human resources to content creation and user experience optimization, enhancing its overall competitiveness.

[0173] Optimized resource allocation: The system can accurately recommend content based on users' actual needs, improving the efficiency of content resource utilization and avoiding waste. Simultaneously, through real-time data analysis, the platform can better understand user needs and behavioral patterns, providing strong data support for content procurement, production, and promotion, further optimizing resource allocation.

[0174] Enhance user stickiness and activity

[0175] Precise recommendations: By providing users with personalized content recommendations, the platform increases user dwell time and viewing frequency. Users are more likely to discover content that interests them, thereby increasing platform engagement and activity.

[0176] Discover new content: Based on users' viewing history and behavior patterns, the system can recommend new content that users may not have actively searched for but could be interested in. This helps users continuously discover new viewing options, broaden their viewing horizons, and further enhance their dependence on and enjoyment of the platform.

[0177] Improve system scalability and maintainability

[0178] Modular Design: The system adopts a modular design, with each functional module relatively independent, facilitating individual development, testing, and maintenance. When an upgrade or optimization of a certain function is required, it will not affect other modules, improving the system's maintainability.

[0179] Easy to Scalable: Built on a mature stream processing framework and machine learning algorithms, the system boasts excellent scalability. As the number of users and the scale of data increase, the system can be easily expanded by adding servers and optimizing algorithms to meet the platform's business growth needs.

[0180] Enhance the platform's market competitiveness

[0181] Differentiated Advantages: By providing personalized recommendations and efficient content review, this system allows the platform to stand out in the fierce market competition, forming a unique differentiated advantage. This helps attract more users and partners, increasing the platform's market share and brand influence.

[0182] Market Adaptability: The system can quickly respond to market changes and evolving user needs, adjusting recommendation strategies and content review standards in a timely manner. This enables the platform to remain competitive in the face of a constantly changing market environment, better meet user needs, and adapt to market changes.

[0183] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An intelligent movie recommendation system, characterized in that, include: The user behavior analysis module is used to collect and analyze user behavior data in the TV ranking square and special topic square to build user profiles; The real-time data processing module is used to analyze user behavior data in real time and dynamically update the recommendation strategy. The personalized recommendation module generates personalized TV lists and special content recommendations based on user profiles and real-time analysis results. The content moderation and compliance module uses machine learning models to automatically detect and filter non-compliant content, and introduces a manual review mechanism for uncertain content.

2. The system according to claim 1, characterized in that, The user behavior analysis module includes: The data acquisition unit collects user viewing behavior data through an application programming interface; The data preprocessing unit cleans, standardizes, and deduplicates the data; The feature extraction unit extracts user behavior features, content features, and contextual features; The user profile building unit combines user registration information to build and dynamically update user profiles.

3. The system according to claim 1, characterized in that, The real-time data processing module includes: The stream processing framework unit receives user behavior data streams; The real-time analysis algorithm unit extracts real-time features based on sliding window or event-triggered mechanisms; It is recommended to update the trigger unit and recalculate the priority of the recommendation list based on real-time features.

4. The system according to claim 1, characterized in that, The personalized recommendation module includes: The recommendation model training unit employs collaborative filtering algorithms, content recommendation algorithms, or deep learning models. The recommendation strategy formulation unit formulates strategies based on viewing history, similar users, popular content, or context. The recommendation result generation unit outputs a recommendation list with relevance scores; The recommendation results display unit shows the sorted recommended content through the user interface.

5. The system according to claim 1, characterized in that, The content moderation and compliance module includes: The audit model training unit trains a support vector machine or deep learning model based on labeled samples. The content feature extraction unit extracts keywords, image, and audio features; The automated review process unit automatically categorizes content compliance. The manual review mechanism unit conducts a second review of uncertain content in the model.

6. A smart movie recommendation method, characterized in that, include: Collect user behavior data in the TV rankings and special topics sections to build dynamically updated user profiles; Stream processing technology is used to analyze user behavior data in real time and dynamically adjust recommendation strategies. Based on user profiles and real-time analysis results, personalized TV lists and special content recommendations are generated. Machine learning models are used to automatically filter out non-compliant content, and uncertain content is manually reviewed.

7. The method according to claim 6, characterized in that, The construction of user profiles includes: Data on viewing duration, number of pauses, and search keyword behavior are collected through application programming interfaces (APIs). The data is cleaned, standardized, and deduplicated. Extract user behavior features, content features, and contextual features; User profiles are built and dynamically updated based on user registration information.

8. The method according to claim 6, characterized in that, The dynamically adjusted recommendation strategy includes: Access user behavior data streams through a stream processing framework; Calculate real-time behavioral characteristics based on sliding window or event triggering mechanisms; The priority of the recommendation list is recalculated based on real-time features.

9. The method according to claim 6, characterized in that, The generation of personalized recommendations includes: The recommendation model is trained using collaborative filtering algorithms, content recommendation algorithms, or deep learning models. Develop diverse recommendation strategies based on user behavior characteristics; Generate a recommendation list with relevance scores and display it in order of priority.

10. The method according to claim 6, characterized in that, The filtered non-compliant content includes: A content moderation model was trained based on labeled samples. Extract multi-dimensional features from the content to be reviewed; Compliance is automatically categorized using a model; For uncertain content, initiate manual review and provide feedback to optimize the model.

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