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<!-- intro.html -->
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<body>
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</body>
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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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<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 Sustainability Augmented Reranking (SAR), ensures the system's recommendations align with
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sustainability goals.
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</p>
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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.
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We use the Wikivoyage dataset to provide city recommendations based on user queries.
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The vector embeddings are stored and accessed in a VectorDB (LanceDB) hosted in Google Cloud.
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</p>
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<p style="text-align: justify">This is an extension of the work by <a href="https://arxiv.org/abs/2403.18604">Banerjee
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et al.</a></p>
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<p style="text-align: justify">To cite, please use the following:</p>
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<blockquote>
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<p>Enhancing sustainability in Tourism Recommender Systems, Ashmi Banerjee, Adithi Satish, Wolfgang Wörndl.</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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