A Travel Recommender system utilizes cosine similarity to suggest destinations based on user preferences and similarities between travel options. By representing user preferences and travel options as vectors in a high-dimensional space, cosine similarity measures the cosine of the angle between two vectors, indicating their similarity. The system calculates the cosine similarity between the user's preferred travel features and the features of various destinations and recommends the destinations with the highest cosine similarity scores. This approach allows the recommender system to provide personalized recommendations by finding destinations that align closely with the user's interests, resulting in a more tailored and satisfying travel experience.
Recommender System Files: -
- Recommender.py
- rec2.py
Flask App Files: -
- app.py