Data cleaning refers to the removal of

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The difficulty of data collection is to solve the problems of data security, legality, privacy and ethics so as to protect the rights and dignity of users and content. 2. Data processing After data collection, it is necessary to process the data to improve the quality and availability of the data. Data processing refers to the use of artificial intelligence big models to quickly and efficiently process user data and content data, including data cleaning, data fusion, data compression, data enhancement, etc. , so as to provide better quality and more applicable data for personalized content page display.



anomalies, duplications and missing in the data to improve israel cell phone number the accuracy and consistency of the data. There are many ways to clean data. For example, you can use the autoencoder denoising of the artificial intelligence big model and the autoencoder anomaly detection technology to automatically identify and repair errors and defects in the data. Data fusion refers to the integration and unification of data from different channels and methods to improve the completeness and richness of the data. There are many methods for data fusion.



For example, the multimodal fusion knowledge graph entity linking and other technologies of artificial intelligence big models can be used to automatically identify and associate different modes and entities in the data. Data compression refers to reducing the dimension and simplifying the data to improve the efficiency and interpretability of the data. There are many methods for data compression. For example, the principal component analysis, autoencoder, variational autoencoder and other technologies of artificial intelligence big models can be used to automatically extract and retain the main features and information in the data. Data enhancement refers to expanding and transforming the data to increase the quantity and diversity of the data.