Real-time hard negative sampling with LLM for two-tower models

In the field of large-scale recommendation systems, two-tower models have proven to be particularly effective during the retrieval phase. However, traditional negative sampling techniques, such as in-batch or out-of-batch sampling, frequently generate negative examples that are too easy, which the model quickly learns without offering a real challenge. To overcome this limitation, an innovative approach has been proposed that uses a large language model (LLM) to generate hard negative samples in real time, based on semantic clusters during training. This method not only improves the quality of learning but also reduces popularity bias and breaks the feedback loops inherent in recommendations. The practical implementation of such solutions requires deep expertise in enterprise artificial intelligence, as well as in the development of custom applications that efficiently integrate complex models. At Q2BSTUDIO, we combine AWS and Azure cloud services with AI capabilities, intelligent agents, and business analytics with Power BI to offer comprehensive solutions that optimize everything from infrastructure to recommendation logic, ensuring scalability and performance in real-world environments.