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A deep architecture that is able to jointly optimize cost functions and route-ranking model towards any route preference is proposed, and a novel loss function is proposed that optimizes a single-objective variable, with other variables strictly under constraints.
IntHQ's innovative architecture not only mitigates common pitfalls in multi-task learning but also delivers a measurable 1.60% lift in user engagement for travel recommendations at scale.
LLMs can handle basic route planning, but fall apart when user preferences enter the mix, as shown by a new benchmark based on real-world queries.
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