Mobile Tourism Recommendation System for Visually Disabled

Abstract

Mobile Tourism Recommendation System recommends to a tourist the best attractions in a particular place according to his preferences, profile and interest. First, a Recommender system offers a list of the city places likely to interest the user. This list estimates the user demographic classification, likes in former trips, and preferences for the current visit. Second, a planning module schedules the list of recommended places according to their characteristics and user limitations. The planning system decides how and when to perform the recommended activities. For implementing these recommender methods, we have applied different machine learning algorithms, which are the K-nearest neighbors (K-NN) for both Clean Boot (CB) and Consolidation Function (CF) and the decision tree for all Data Framing (DF). Thus, executing a recommendation system for tourists helps them with user-friendly planning. Blind people can also use this. This application provides complete voice assistance for easy navigation via a simple button click. Vibratory and voice feedback is provided for accurate crash alerts for visually challenged people. The application extracts its smartness by incorporating Android and Internet of Things (IoT) support. Since blind supported applications and devices are more expensive and many blinds can not afford them, we aim to put forth a novel, low cost and reliable approach to help the blind explore the possibilities and power of smartphone technology in navigation. We additionally expect to find the static variables that should be tended to, food, tidiness, and opening times, and valuable to suggest a tourist place depending on the travel history of the client. In this investigation, we propose a cross-planning table methodology depending on the area’s prevalence, appraisals, idle points, and conclusion. A targeted work for proposal streamlining is defined as dependent on these mappings. Our outcomes show that the consolidated highlights of Latent Dirichlet Allocation (LDA), Support vector machines (SVM), appraisals, and cross mappings are helpful for upgraded execution. The fundamental motivation of this study was to help businesses related to tourism.