A Personalized Hybrid Tourism Recommender System

• Python
• Machine Learning

In today’s world most of the people use internet, technology, social networking and result in huge amount of tourist data like hotels, restaurants, transport, heritage, tourist event, etc. The main reason for production of large amount of tourist data is Online Travelling Agency. However, Web Search engine provide list of possibilities to tourist but it is very difficult to find best one. Web search engine slows down the selection of best place and create noise. The result of web search engine confused the tourist. There are various recommendation systems are developed to suggest or assist tourist to plane the trip and help to find the information they are looking for. We present a recommendation system which is the combination of various recommendation system used in the field of tourism. The main objective of this work is then to contribute to the design of tourism recommender systems by proposing a framework that clarifies how the hybrid recommendation process works. The proposed system goes beyond a list of recommended tourist attractions and can be seen as a planner that aims to build a complex and detailed program of a multiday visit. The recommender system is very helpful in planning a trip or for searching good services among many attractions, destinations and other activity. Hybrid system works on information filtering and it gives the best services and offers to users like when we buy any product and use it may be already bought and use by someone with same taste. This recommendation will gives the most relevant places and provide better experiences to users. The main objective of our system is to design a system which gives the clear idea of how recommendation system works.

Project Sample Pages :

Previous Next