arXiv:2603.04946v2 Announce Type: replace Abstract: In local-life service platforms, query suggestion reduces user effort by generating candidate queries from input prefixes. Traditional multi-stage systems rely heavily on historical popular queries, limiting their ability to capture long-tail and emerging demand. Although LLMs provide strong semantic generalization, their deployment in local-life services faces three challenges: insufficient city-preference awareness, exposure bias in preference optimization, and strict online latency constraints. We propose LocalSUG, an LLM-based query sugge
LLMs are rapidly being adapted for specialized, real-world applications, and the industry is focused on overcoming challenges like latency and domain-specific biases in deployment.
Improving local search capabilities with LLMs can significantly enhance user experience and open new market opportunities for location-based services.
Local-life service platforms can move beyond historical query reliance, offering more dynamic and context-aware suggestions, particularly for long-tail demand.
- · Local-life service platforms
- · On-demand delivery services
- · Search engine developers
- · AI model developers
- · Traditional query suggestion systems
- · Platforms without LLM integration
More accurate and personalized local search results will improve user engagement with local services.
Increased efficiency in local search could lead to greater commercial activity for small local businesses previously overlooked by generic suggestions.
The success of specialized LLMs like LocalSUG could accelerate the development of other domain-specific AI models, disrupting various niche markets.
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Read at arXiv cs.CL