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How Meta’s latest research proves you can use generative AI to understand user intent
By thinking about recommendation as a generative problem, you can tackle it from new angles and use LLMs to better understand user intent.
It also enhances the ability to capture deeper semantic relationships within the data and provides other benefits of generative models, such as modifying the temperature to adjust the diversity of recommendations. The researchers note that “the fusion of dense and generative retrieval methods holds tremendous potential for advancing recommendation systems” and as the models evolve, “they will become increasingly practical for real-world applications, enabling more personalized and responsive user experiences.” “Our contributions pave the way for a new class of generative retrieval models that unlock the ability to utilize organic data for steering recommendation via textual user preferences,” the researchers write.
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