Sherpa: Implementing a Hybrid Recommendation System for Next-Gen Tourist Experience
Keywords:
Artificial Intelligence, Digital Tourism, Collaborative Filtering, Content-Based Filtering, Hybrid Recommender System, Singular Value Decomposition, Cosine Similarity Matrix.Abstract
In the digital era, Sherpa revolutionizes personalized tourism with an AI-driven recommendation system, fostering meaningful connections between travelers and local guides. This study explores Sherpa's integration of collaborative and content-based filtering—specifically, singular value decomposition (SVD) and cosine similarity—to tailor travel experiences uniquely. Our methodology includes a detailed examination of Sherpa's algorithm and its implementation within a cross-platform, MERN Stack-powered backend. We assess the system's efficacy in aligning recommendations with individual user preferences, based on quantitative user feedback and engagement metrics. Initial results demonstrate a significant improvement in personalized experience satisfaction. The paper concludes that Sherpa's innovative approach not only enhances the quality of travel recommendations but also sets a new standard for interactive and adaptive tourism platforms. Through continuous algorithmic refinement, Sherpa is positioned to lead a transformative shift in how travelers explore new destinations, offering not just journeys, but transformative experiences.
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“TripAdvisor Dataset.” Accessed: May 05, 2024. [Online]. Available: https://www.researchgate.net/publication/308968574_TripAdvisor_Dataset
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