Pathfinder-Navigating Tourism with Machine Learning Recommendations

Registration ID: IJNRD_216644

Published ID: IJNRD2403636

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P.DIVYA, R.Ashwin raj, J.Parvin yakop

evaluation matrics,accuracy,precision,F1-score,collaboratve filtering, clustering algorithm, matrix factorization tecnique

Tourist Recommendation Systems (TRS) play a crucial role in modern tourism by assisting travelers in discovering relevant destinations, attractions, accommodations, and activities. This report presents a comprehensive overview of the design, development, and evaluation of a TRS driven by machine learning algorithms. The TRS leverages a diverse array of data sources, including user preferences, historical booking data, location-based information, and user-generated content from social media platforms. Through advanced machine learning techniques such as collaborative filtering, content-based filtering, and hybrid models, the TRS generates personalized recommendations tailored to each user's unique preferences and constraints. Additionally, the report explores the challenges associated with data preprocessing, feature selection, and algorithm optimization in the context of building an effective TRS. Evaluation methodologies, including offline metrics and user studies, are employed to assess the accuracy, relevance, and user satisfaction of the recommendation system. Through experimentation and analysis, we demonstrate the effectiveness and feasibility of utilizing machine learning algorithms. The evaluation metrics are used to evaluate the performance of different algorithms based on metrics such as accuracy, precision, recall, and F1-score. Insights gained from the development and evaluation process provide valuable guidance for researchers, practitioners, and stakeholders involved in the design and implementation of tourist recommendation systems. Overall, this report offers a deep dive into the technical intricacies and practical considerations of leveraging machine learning algorithms to deliver personalized tourist recommendations, contributing to the advancement of tourism technology and user-centric travel experiences.

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P.DIVYA, R.Ashwin raj, & J.Parvin yakop (March-2024). Pathfinder-Navigating Tourism with Machine Learning Recommendations. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(3), g304-g309. https://ijnrd.org/papers/IJNRD2403636.pdf

Volume 9 Issue 3, March-2024

Pages : g304-g309

Paper Reg. ID: IJNRD_216644

Published Paper Id: IJNRD2403636

Research Area: Engineering

Author Type: Indian Author

Country: CUDDALORE, TAMILNADU, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2403636.pdf

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