Spaces:
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UI Enhancement with Emoticons for Improved User Experience
#1
by
thesab
- opened
- src/ui/templates/intro.html +136 -58
src/ui/templates/intro.html
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<!-- intro.html -->
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<body>
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<
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</p>
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</blockquote>
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<blockquote>
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<p> [2] <b>Modeling Sustainable City Trips: Integrating CO2e Emissions, Popularity, and Seasonality into Tourism Recommender Systems,</b> <i>Ashmi Banerjee, Tunar Mahmudov, Emil Adler, Fitri Nur Aisyah, Wolfgang
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Wörndl</i>, arXiv preprint <a href="https://arxiv.org/abs/2403.18604">arXiv:2403.18604 (2024)</a>.
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</p>
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</blockquote>
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<br>
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<p style="text-align: justify; font-weight: bold"><sup>*</sup>Google Cloud credits are provided for this project.</p>
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<h2 style='font-size:large; color: black; text-align: left'>Instructions</h2>
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<ul>
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<li>Select the country and city where you're located.</li>
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<li>Enter the search query; it has to be something for which the system can recommend cities.</li>
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<li>Click the <b>Search</b> button to find the most sustainable recommendations for your <b>starting
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position</b>.
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</li>
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<li>Click the <b>Clear</b> button to clear the fields.</li>
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</ul>
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<p style="text-align: justify; color: darkred">Note that this works best if you ask it for <span
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style="font-weight: bold; color: darkred; text-underline: darkred">city</span> recommendations.</p>
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</body>
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<!-- intro.html -->
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<html>
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Green City Finder</title>
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<style>
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body {
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font-family: 'Arial', sans-serif;
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margin: 0;
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padding: 0;
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line-height: 1.6;
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color: #333;
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background-color: #f9f9f9;
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}
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header {
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text-align: center;
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padding: 30px 20px;
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background-color: #4CAF50;
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color: #fff;
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}
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header h1 {
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font-size: 2.5em;
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margin: 0;
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}
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header h3 {
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font-size: 1.2em;
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margin: 10px 0 0;
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}
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main {
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padding: 20px;
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max-width: 800px;
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margin: 0 auto;
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background-color: #fff;
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box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
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border-radius: 8px;
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}
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main p, main ul, main blockquote {
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text-align: justify;
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margin-bottom: 20px;
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}
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.sustainability-list {
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padding-left: 20px;
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}
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.citation {
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padding: 10px 20px;
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background-color: #f0f0f0;
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border-left: 5px solid #4CAF50;
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border-radius: 5px;
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margin-bottom: 20px;
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}
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.instructions {
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background-color: #e8f5e9;
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border-left: 5px solid #4CAF50;
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padding: 15px 20px;
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border-radius: 5px;
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margin-bottom: 20px;
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}
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.instructions-header {
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font-size: 1.5em;
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margin-bottom: 10px;
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}
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.instructions ul {
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padding-left: 20px;
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}
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.note {
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color: darkred;
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font-weight: bold;
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text-align: center;
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margin-top: 20px;
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}
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.note span {
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text-decoration: underline;
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}
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a {
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color: #4CAF50;
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text-decoration: none;
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}
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a:hover {
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text-decoration: underline;
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}
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</style>
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</head>
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<body>
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<header>
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<h1>🍀 Green City Finder 🍀</h1>
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<h3>AI Sprint 2024 submissions by Ashmi Banerjee.<sup>*</sup></h3>
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</header>
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<main>
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<p>🧳 Tourism Recommender Systems (TRS) have traditionally focused on providing personalized travel suggestions, often prioritizing user preferences without considering broader sustainability goals. 🌍 Integrating sustainability into TRS has become essential with the increasing need to balance environmental impact, local community interests, and visitor satisfaction. We enhance the traditional RAG system by incorporating a sustainability metric based on a city’s popularity and seasonal demand during the prompt augmentation phase. This modification, called <b>Sustainability Augmented Reranking (SAR)</b>, ensures the system's recommendations align with sustainability goals.</p>
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<p>📈 <a href="https://arxiv.org/pdf/2403.18604">Sustainability score</a> for the retrieved destinations is calculated based on the following parameters:</p>
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<ul classclass="sustainability-list">
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<li>🌿 Carbon footprint from the starting points to the retrieved cities using the greenest mode of travel (fly, drive, train).</li>
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<li>🏆 Overall popularity of the retrieved destinations based on their aggregated Tripadvisor reviews and opinions.</li>
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<li>📅 Seasonal footfall for the intended month of travel (if present).</li>
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</ul>
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<p>We test our implementation with Google's <b>Gemini</b> models through VertexAI to generate sustainable travel recommendations. We use the Wikivoyage dataset to provide city recommendations based on user queries. The vector embeddings are stored and accessed in a VectorDB (LanceDB) hosted in Google Cloud.</p>
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<p>📝 This is an extension of the following work. To <b>cite</b>, please use the following:</p>
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<div class="citation">
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<p>[1] <b>Enhancing sustainability in Tourism Recommender Systems,</b> <i>Ashmi Banerjee, Adithi Satish, Wolfgang Wörndl</i>, In Proceedings of the 1st International Workshop on Recommender Systems for Sustainability and Social Good (RecSoGood 2024), co-located with ACM RecSys 2024, Bari, Italy.</p>
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</div>
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<div class="citation">
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<p>[2] <b>Modeling Sustainable City Trips: Integrating CO2e Emissions, Popularity, and Seasonality into Tourism Recommender Systems,</b> <i>Ashmi Banerjee, Tunar Mahmudov, Emil Adler, Fitri Nur Aisyah, Wolfgang Wörndl</i>, arXiv preprint <a href="https://arxiv.org/abs/2403.18604">arXiv:2403.18604 (2024)</a>.</p>
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</div>
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<p style="font-weight: bold"><sup>*</sup> Google Cloud credits are provided for this project.</p>
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<h2 class="instructions-header">📌 Instructions</h2>
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<div class="instructions">
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<ul>
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<li>🌍 Select the country and city where you're located.</li>
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<li>🔍 Enter the search query; it has to be something for which the system can recommend cities.</li>
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<li>🖱️ Click the <b>Search</b> button to find the most sustainable recommendations for your <b>starting position</b>.</li>
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<li>❌ Click the <b>Clear</b> button to clear the fields.</li>
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</ul>
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</div>
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<p class="note">⚠️ Note that this works best if you ask it for <span>city</span> recommendations.</p>
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</main>
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</body>
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</html>
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