International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024
p-ISSN: 2395-0072
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EPIC Destinations- Tour Recommendation System using Collaborative Filtering A. Haritha1, Dr. D Jaya Kumari2, P.Divya Priyanka3, S.Harshitha4, P.Adithya5, P.Hema Vamsi6 1Student,Department of Computer Science and Engineering, Sri Vasavi Enginnering College, Pedatadepalli,
Tadepalligudem, AndhraPradesh, India
2-6Department of Computer Science and Engineering, Sri Vasavi Engineering College, Pedatadepalli,
Tadepalligudem, AndhraPradesh, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Tourism is a thriving global industry, offering a
recommendations based on the plans of other users. Our solution is designed to simplify and enhance the tour planning experience, delivering customized and impartial recommendations that streamline the process, making it both efficient and enjoyable.
delightful escape from our daily routines. However, planning a trip can be a daunting and time-consuming task. Relying solely on recommendations from friends, limits our choices to their personal experiences, while travel agents may provide biased suggestions with the goal of selling packages. Navigating the vast expanse of the internet for research often leaves travellers frustrated and overwhelmed. To address these challenges, we propose "EPIC Destinations," an intelligent tour recommendation system that utilizes collaborative filtering to provide personalized suggestions for travellers. Our website optimizes itineraries, recommends the best places to visit, and offers insights on hotels, local cuisine, ideal timings, and transportation options. By leveraging advanced algorithms and data analysis, EPIC Destinations considers factors such as user feedback, popular attractions, local events, and travel patterns to ensure accurate and unbiased recommendations that cater to individual preferences.
2. LITERATURE SURVEY AbhishekKulkarni[1],proposed a research article,” A Machine Learning Approach to Building a Tourism Recommendation System using Sentiment Analysis” consisting analysis of various machine learning and deep learning algorithms has been done and their behaviour has been studied. Through the results obtained recommendation system which generates a unique tourism based on user's interests is built. Vasileios Komi Anos [2], through his article,” Constrained interest-based tour recommendations in large scale cultural heritage virtual environments” explained how to deal with the issues by providing users with constrained interest-based tour recommendations. A recommendation model is proposed to deal with the user’s information overload issue. The model and algorithm are tested with a wide range of cases to obtain interesting outcomes.
Therefore, through the power of collaboration, our website enhances the overall travel experience, empowering users to optimize their adventures and create unforgettable memories. Key Words: Tour recommendation, Collaborative filtering, Wonders of India, Seasonal escapes, Users Ratings, Tour description, Similarity scores.
3. PROPOSED SYSTEM We're creating a friendly website to help travellers plan their dream trips. Our site will suggest amazing places to visit that fit your preferences, taking into account things like how much money you want to spend and the best time of year to go. We'll also recommend delicious local foods to try. Our main goal is to make trip planning easy and fun for you, so you don't have to spend a lot of time searching all over the internet for information. We want to be your one-stop shop for creating a personalized and enjoyable travel experience.
1.INTRODUCTION Tourism has become an integral part of our lives, providing us with the opportunity to take a break from our daily routines and find tranquillity, whether by the sea or in the mountains. However, planning these trips has become a complex and time-consuming task. Recommendations from friends and travel agents are often limited and potentially biased, while sifting through the vast amount of information on the internet can be overwhelming. To tackle this issue, we have developed a user-friendly Tour Recommendation System that employs collaborative filtering. This system takes into account various factors such as individual interests, preferred destinations, food choices, and optimal travel times to create personalized suggestions and daily itineraries. It also calculates travel durations and time allocations for each location, facilitating efficient trip planning. Furthermore, the system provides real-time
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