Summary of Itinera: Integrating Spatial Optimization with Large Language Models For Open-domain Urban Itinerary Planning, by Yihong Tang et al.
ITINERA: Integrating Spatial Optimization with Large Language Models for Open-domain Urban Itinerary Planning
by Yihong Tang, Zhaokai Wang, Ao Qu, Yihao Yan, Zhaofeng Wu, Dingyi Zhuang, Jushi Kai, Kebing Hou, Xiaotong Guo, Han Zheng, Tiange Luo, Jinhua Zhao, Zhan Zhao, Wei Ma
First submitted to arxiv on: 11 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Citywalk phenomenon has revolutionized urban travel, demanding a deeper understanding of personalized requests. This paper introduces Open-domain Urban Itinerary Planning (OUIP), generating customized itineraries from natural language user requests. The ITINERA system combines spatial optimization and large language models to provide tailored itineraries based on user needs. By decomposing requests, selecting candidate points of interest, optimizing POI ordering, and generating the itinerary, ITINERA outperforms current solutions. Experimental results on real-world datasets demonstrate its capacity for delivering personalized, spatially coherent itineraries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Citywalk has changed how we travel in cities. People want unique experiences tailored to their needs. This paper makes a special kind of computer program that can create personalized city tours based on what people ask for. It’s called ITINERA and it uses big data and map information to make sure the tour is fun, safe, and makes sense. The creators tested it with real data and showed that it’s better than current methods. |
Keywords
* Artificial intelligence * Optimization
