This involves issues of user privacy

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Finally, in order to continuously optimize the recommendation effect, the recommendation system will continue to collect user feedback, such as click-through rate, purchase conversion rate, user satisfaction and other indicators, and adjust the recommendation algorithm based on these feedbacks. This iterative process ensures that the recommendation system can adapt to changes in user behavior and provide more accurate and personalized recommendations. Case : Intelligent customer service system In intelligent customer service systems, the key to the


application of Embedding technology is to achieve efficient, Afghanistan WhatsApp Number accurate and humanized customer service. Its core goal is to understand and respond to user queries, providing immediate and accurate assistance. In order to achieve this goal, the system first needs to have an in-depth understanding of the user's natural language input. This usually involves multiple steps of natural language processing (NLP), including word segmentation, part-of-speech tagging, named entity recognition, dependency syntax analysis, etc. After the above-mentioned word segmentation,





part-of-speech tagging and other processing, Embedding technology comes into play. The system uses pre-trained word embedding models such as WordVec, GloVe or BERT to convert each word in the text into a vector in a high-dimensional space. and data security, especially in scenarios involving sensitive information. preventive solution: Privacy protection technology: Differential privacy, homomorphic encryption and other technologies are used to protect user data and ensure that the model can be trained without leaking personal privacy.