Accurately identifies hotel aliases, encyclopedia entries, and other ambiguous information, giving LLMs and other base models “thinking ability” to deeply understand room classifications and eliminate mismatches at the source.
Combines traditional and vector searches to perform millisecond-level screening from billion-scale room inventories, achieving a recall rate of up to 99%.
Integrates dozens of features such as room type, bed type, and view, using deep neural network models to optimize matches from massive candidate data sets, achieving a confidence level of up to 99.8%.
Leverages the powerful reasoning and generation capabilities of large language models to conduct rigorous verification of complex and easily confused matches, automatically generating clear justification for decisions.
Based on millions of room type samples from global suppliers, this leverages multi-dimensional features (such as facilities, room structure, and view) to achieve cross-platform, precise, and intelligent matching.