A C4.5 Decision Tree Algorithm with MRMR Features Selection Based Recommendation System for Tourists

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B. Santhosh Kumar, M. Raghavendra Reddy


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.


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