A Tourist Destination Recommender System for Nepali Tourism Industry

Abstract

Tourism is one of the major sources of revenue in developing countries with natural beauty like Nepal. Understanding tourist preferences is essential to optimize this industry. Many places suffer obscurity due to not being recommended to the tourists who would’ve liked the destination. Our goal is to analyse what every tourist likes and recommend them accordingly. In case of Nepal, since there are a lot of under-explored potential tourist destinations and not many data available for the reviews and ratings, the data becomes sparse and limited. To tackle the challenge, our system utilizes a blend of content-based and collaborative filtering, which is known as hybrid filtering. The system leverages the strengths of Cosine Similarity, k-Nearest Neighbors, and Matrix Factorization to personalize recommendations based on user preferences and available destination information even in a sparse as well as limited dataset.  This approach not only tackles the under-exploration of hidden gems but also presents a scalable framework applicable to any domain with limited data, potentially impacting personalized recommendations across various industries.