<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
	<channel>
		<title>Tourism Prediction on Simone Mattioli - Adventures in Technology &amp; Humanity</title>
		<link>https://simo-hue.github.io/tags/tourism-prediction/</link>
		<description>Recent content in Tourism Prediction on Simone Mattioli - Adventures in Technology &amp; Humanity</description>
		<generator>Hugo</generator>
		<language>en-us</language>
		
		
		
		
			<lastBuildDate>Sun, 15 Dec 2024 00:00:00 -0500</lastBuildDate>
		
			<atom:link href="https://simo-hue.github.io/tags/tourism-prediction/index.xml" rel="self" type="application/rss+xml" />
			<item>
				<title>Understanding and Predicting Tourist Behavior through Large Language Models</title>
				<link>https://simo-hue.github.io/blog/publication/llm-tourism-mobility-predictor/</link>
				<pubDate>Sun, 15 Dec 2024 00:00:00 -0500</pubDate>
				<guid>https://simo-hue.github.io/blog/publication/llm-tourism-mobility-predictor/</guid>
				<description>&lt;h2 id=&#34;abstract&#34;&gt;Abstract&lt;/h2&gt;&#xA;&lt;p&gt;Understanding and predicting how tourists move through a city is a challenging task, as it involves a complex interplay of spatial, temporal, and social factors. Traditional recommender systems often rely on structured data, trying to capture the nature of the problem. However, recent advances in Large Language Models (LLMs) open up new possibilities for reasoning over richer, text-based representations of user context.&lt;/p&gt;</description>
			</item>
	</channel>
</rss>
