Summary of Nextlocllm: Next Location Prediction Using Llms, by Shuai Liu et al.
nextlocllm: next location prediction using LLMs
by Shuai Liu, Ning Cao, Yile Chen, Yue Jiang, Gao Cong
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 paper proposes a novel approach called NextLocLLM for predicting the next location based on human mobility analysis. It leverages large language models (LLMs) to process natural language descriptions of locations and their spatial relationships. The model encodes locations using continuous spatial coordinates, which enables robust generalization across cities. Additionally, it utilizes LLMs’ ability to encode POI categories into embeddings, capturing functional attributes of locations. Experiments show that NextLocLLM outperforms existing models in both supervised and zero-shot settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Next location prediction is important for various applications. Existing methods use IDs to represent locations, but this doesn’t account for spatial relationships or generalize across cities. The proposed model, NextLocLLM, uses LLMs to process natural language descriptions of locations and their spatial relationships. It encodes locations using continuous coordinates and captures functional attributes of POI categories. This helps the model predict the next location better than existing methods. |
Keywords
» Artificial intelligence » Generalization » Supervised » Zero shot
