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UI Enhancement with Emoticons for Improved User Experience

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This pull request introduces significant improvements to the user interface and visuals of the "Green City Finder" page. The key changes include:

Addition of Emoticons:
- Emoticons have been added to make the content more engaging and visually appealing.
- Specific emoticons are used to highlight key points and enhance text readability.
Improved Styling:
- Minor enhancements to the existing CSS to ensure a modern and responsive display.
- The use of existing HTML tags has been maintained, with the addition of emoticons for a more pleasant user experience.

Files changed (1) hide show
  1. src/ui/templates/intro.html +136 -58
src/ui/templates/intro.html CHANGED
@@ -1,61 +1,139 @@
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  <!-- intro.html -->
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  <body>
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- <h1 style='font-size:xx-large; color: green; text-align: center'>🍀 Green City Finder 🍀</h1>
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- <h3 style="text-align: center">AI Sprint 2024 submissions by Ashmi Banerjee.<sup>*</sup></h3>
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- <br>
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-
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- <p style="text-align: justify">
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- Tourism Recommender Systems (TRS) have traditionally focused on providing personalized travel suggestions, often
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- prioritizing user preferences without considering broader sustainability goals.
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- Integrating sustainability into TRS has become essential with the increasing need to balance environmental impact,
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- local community interests, and visitor satisfaction.
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- We enhance the traditional RAG system by incorporating a sustainability metric based on a city’s popularity and
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- seasonal demand during the prompt augmentation phase.
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- This modification, called <b>Sustainability Augmented Reranking (SAR)</b>, ensures the system's recommendations align with
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- sustainability goals.
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- </p>
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-
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- <p style="text-align: justify"><a href="https://arxiv.org/pdf/2403.18604">Sustainability score</a> for the retrieved
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- destinations is calculated based on the following parameters:
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- <ul>
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- <li>Carbon footprint from the starting points to the retrieved cities using the greenest mode of travel (fly, drive,
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- train)
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- </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>
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- <p style="text-align: justify">
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- We test our implementation with Google's <b>Gemini</b> models
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- through VertexAI to generate sustainable travel recommendations.
31
- We use the Wikivoyage dataset to provide city recommendations based on user queries.
32
- The vector embeddings are stored and accessed in a VectorDB (LanceDB) hosted in Google Cloud.
33
- </p>
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-
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- <p style="text-align: justify">This is an extension of the following work. To <b>cite</b>, please use the following:</p>
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-
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- <blockquote>
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- <p> [1] <b>Enhancing sustainability in Tourism Recommender Systems,</b> <i>Ashmi Banerjee, Adithi Satish, Wolfgang
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- Wörndl</i>, In Proceedings of the 1st International Workshop on Recommender Systems for Sustainability and Social
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- Good (RecSoGood 2024), co-located with ACM RecSys 2024, Bari, Italy.
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+ .sustainability-list {
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+ padding-left: 20px;
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+ }
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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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+
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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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+
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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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+
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+ .instructions ul {
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+ padding-left: 20px;
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+ }
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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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+
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+ .note span {
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+ text-decoration: underline;
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+ }
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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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+
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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>
101
+ <h3>AI Sprint 2024 submissions by Ashmi Banerjee.<sup>*</sup></h3>
102
+ </header>
103
+ <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>
105
+
106
+ <p>📈 <a href="https://arxiv.org/pdf/2403.18604">Sustainability score</a> for the retrieved destinations is calculated based on the following parameters:</p>
107
+ <ul classclass="sustainability-list">
108
+ <li>🌿 Carbon footprint from the starting points to the retrieved cities using the greenest mode of travel (fly, drive, train).</li>
109
+ <li>🏆 Overall popularity of the retrieved destinations based on their aggregated Tripadvisor reviews and opinions.</li>
110
+ <li>📅 Seasonal footfall for the intended month of travel (if present).</li>
111
+ </ul>
112
+
113
+ <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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+
115
+ <p>📝 This is an extension of the following work. To <b>cite</b>, please use the following:</p>
116
+ <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>
118
+ </div>
119
+ <div class="citation">
120
+ <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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+
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+ <p style="font-weight: bold"><sup>*</sup> Google Cloud credits are provided for this project.</p>
124
+
125
+ <h2 class="instructions-header">📌 Instructions</h2>
126
+ <div class="instructions">
127
+ <ul>
128
+ <li>🌍 Select the country and city where you're located.</li>
129
+ <li>🔍 Enter the search query; it has to be something for which the system can recommend cities.</li>
130
+ <li>🖱️ Click the <b>Search</b> button to find the most sustainable recommendations for your <b>starting position</b>.</li>
131
+ <li>❌ Click the <b>Clear</b> button to clear the fields.</li>
132
+ </ul>
133
+ </div>
134
+
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+ <p class="note">⚠️ Note that this works best if you ask it for <span>city</span> recommendations.</p>
136
+ </main>
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  </body>
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+ </html>
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+