Summary of Forecasting Unseen Points Of Interest Visits Using Context and Proximity Priors, by Ziyao Li et al.
Forecasting Unseen Points of Interest Visits Using Context and Proximity Priors
by Ziyao Li, Shang-Ling Hsu, Cyrus Shahabi
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed machine learning model predicts future Points of Interest (POIs) that individuals are likely to visit by analyzing their historical visit patterns. Unlike previous studies, the model is designed to predict new POIs outside the training data as long as their context aligns with the user’s interests. The approach first forecasts the semantic context of potential future POIs and then combines this with a proximity-based prior probability distribution to determine the exact POI. Experimental results on real-world visit data demonstrate that the model outperforms baseline methods, achieving a 17% improvement in accuracy. Furthermore, the model remains robust when new POIs are introduced over time, exhibiting a lower decline rate in prediction accuracy compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A machine learning model can predict where people will go based on what they’ve done before. This helps with things like planning events and understanding how diseases spread. The problem is that most models only work for places they’ve seen before. Our new model predicts not just the place, but why someone might want to go there. It’s more accurate than other methods and works better when new places open up. |
Keywords
» Artificial intelligence » Machine learning » Probability
