Travel-Recommendation-System

It is a common practice to seek recommendations when traveling to new places. We often turn to friends who have visited or lived there for advice on hotels, restaurants, and tourist attractions. Another approach is to gather information from online platforms like Yelp where reviews and recommendations about local business are posted by anonymous users. The second way is more effective, which enables us to find businesses that align with our tastes. This is the driving force behind this project: Building an intelligent travel recommendation system. The primary objective of this personalized recommendation system is to deliver precise and pertinent recommendations customized for each user. By gathering user preferences by allowing them to indicate their likes and dislikes during their initial interaction with the system. As users continue to use the system, have done the recommendations based on their past behavior and interests. In this way, hopefully this recommendation application can improve users’ traveling experience.

Technical_Report.pdf

Technical Report of Travel Recommendation System

Presentation_Slies.pptx

Presentation Slides of Travel Recommendation System

new_user_recommendation system.ipynb

Contains python code for new user recommendation

ALS_KNN.ipynb

Contains python code for ALS(Collaborative Filtering Technique) and K-Nearest Neighbor(Machine Learning Algorithm)

SVD_Collaborative_Filtering.ipynb

Contains Python code for SVD(Collaborative Filtering technique)

Content Based Jaccard Similarity.ipynb

Contains code for Jaccard Similarity(Content Based Filtering)

content_based_filtering_cosine_similarity.ipynb

Contains python code for Cosine similarity(Content Based filtering)