The significance of an improved user experience in recommender systems
will continue to grow as tourism advances. Most such systems generally offer
information to users that benefits third-party firms' commercial interests, often
involving the implicit compromise of user privacy. In the proposed work, a tourism
recommendation system is developed by the authors to deliver personal
recommendations to users. For designing the proposed recommendation system, the
authors have considered parameters such as ratings, user activity, time utilized by the
user, certifications, and other relevant attributes. Four methods: Memory-based
Collaborative Filtering (MCF), Matrix factorization Collaborative Filtering (MFCF),
Cosine Similarity Content Filtering (CSCF), and Term Frequency-Inverse Document
Frequency (TF-IDF) are used by the authors for designing a tourist recommendation
system. For the assessment of the work, the authors have calculated Mean Average
Error (MAE) and Root Mean Square Error (RMSE).
Keywords: Tourism recommended model, Content-based Filtering (CBF), Collaborative Filtering (CF), Web scraping, and cold-start problem.
