Aim to develop a personalized travel planning system that simultaneously considers all categories of user requirements and provides users with a travel schedule planning service.This will enable the user in finding what they are looking for, easily without spending time and effort.
- Dataset Extraction
- Content Based Filtering-
- Cosine Similarity
- Weighted Ratings
- Location API
- Collaborative Filtering-
- Similarity between users- correlation
- K-Nearest Neighbour
- Dataset used-
- data_collaborative - this has the userId, the itemId, the rating and the date that rating was given
- data_content - this has the itemId, title, category, p_rating etc.
- Web Scraping - technique employed to extract large amounts of data from websites whereby the data is extracted and saved to a local file
- Scrapy - Python framework used to efficiently extract data from websites
- Extracted the data of best tourist places in Jaipur from https://www.trawell.in/rajasthan/jaipur/places-to-visit-things-to-do
- Dataset contains title ,category, distance(from railway station), duration, nearby places, rating etc
Content-based filtering is used to calculate a degree of similarity between the users and the items to be recommended. The process is carried out by comparing the features of the item with respect to the user’s preferences.
Collaborative filtering techniques are recommendation methods based on the opinions of a set of users.The methods based on items predict the interest of the user on an activity a considering the evaluation that this user has given to similar activities
