Touring has become a very important part of our life. By taking a nap from our daily work life we often want to prefer spending some time somewhere along a sea coast or on a hill station and be at complete peace. But planning a tour all by ourselves is a very difficult & time-consuming task. Generally, while planning for a tour, we often prefer taking recommendations from our friends, but these suggestions are often limited to the places they have visited.
Also, the suggestions taken from travel agents are sometimes biased as their objective is to sell their packages & make money out of them. Furthermore, doing our own research over the vast internet for tour planning leaves the user even more frustrated & confused to come towards a definite conclusion as there are tons of websites to browse through on the web. Hence it is necessary to come up with an automated user-friendly solution for tour recommendations for solving this issue. Tourism has always been an integral part of many countries’ economy across the globe. Therefore, it has become very important to attract different tourists from all over the world, for which we can utilize the vast data available on the internet by using data mining & data science for generating user-friendly results based on one’s interest for people surfing for tour planning on the internet. To solve this, we at Nevon Projects have come up with a Tour Recommendation System using collaborative filtering which will generate user-friendly preferences & recommendations.
The rapidly growing usage of the web and its applications has become a major source of user’s information available on the internet. This information can be easily utilized to understand different user’s persona based on their internet activity using data mining techniques and thus by identifying their interest we can suggest them various offers based on their preferences. Hence this recommender system can be very helpful in attracting the tourist by recommending them the right options to choose from, thereby increasing the rate of conversion significantly.
This tour recommending system will play an important role in generating user-friendly results by analysing the user’s online activity and collecting information related to their interests & locations. This system based on data science is developed as an online application, which is capable of producing a personalized list of recommendations by mining & analysing the user’s data from their social network data history to perform better analysis & predictions.
In this system, the user can make a day plan by selecting his list of places based on his preferences of Food & place type. There can be any number of plans made and all the places are fetched using Google place API and based on Highest rating. The Plan considered your total number of hours added, so that it can calculate your travelling time + time spend on a particular location. The final places can be manually sorted by the user or can use auto sort to get the proper route. The system will give suggestions of places where and when required based on other users plans.
- List of places are based on Preferences.
- List of places are from Google, so we will get authentic & a variety of places.
- Places can be auto sorted by TSP Algorithm.
- Recommendation using Collaborative Filtering
- Travel salesman Problem using Greedy Algorithm