Abstract
One of the most difficult tasks for tourists when preparing travel, both before and during travel, is selecting a tourist destination from the information that is accessible on the Internet and from other outlets. Previous Travel Recommendation Systems (TRSs) have tried to resolve this issue. We are applying the C4.5 decision tree algorithm in this paper with the collection of MRMR features to propose tourist travel areas by using datasets from previous tourist encounters. Both existing algorithms, such as interactive or content filtering algorithms, use data from current users' previous history to suggest new locations to them. If this current user has no data from previous encounters, these algorithms won't work. To solve the above problem, we use C4.5 decision tree algorithms that take previous user interactions and then generate a model and if new users enter their criteria, the decision tree will predict the best position based on its feedback. Decision Tree does not require previous history data from new users. The framework is built using a two-step process of feature selection to minimize the number of inputs to the system and Decision Tree C4.5 makes recommendations.
Highlights
Recommender Systems (RSs) are digital instruments and techniques that offer recommendations for a user's use of products
In this paper author is implementing C4.5 decision tree algorithm with MRMR features selection to recommend travel areas to tourist by using dataset from past tourist experiences
To overcome from above problem author is asking to use C4.5 decision tree algorithms which take experiences of previous users and build a model and if new user enter his requirements decision tree will predict best location based on his given input
Summary
Recommender Systems (RSs) are digital instruments and techniques that offer recommendations for a user's use of products. The immense growth of the web and its user base has become the source of vast amounts of online content. This information can be helpful in recommending products or services according to their expectations. By gathering user data such as tastes, desires, and places, the recommendation system plays the role of producing recommendations. A dynamic challenge for recommendation systems is to produce recommendations according to user tastes. Visitors and tourism suppliers are scanned, selected, evaluated and determined more effectively than ever for the production of Decision Support Instruments, known as Recommendation Systems (RS). Both can maximize system satisfaction in return for the consistency of advice and use speed
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