Smart Tour Route Planning Algorithm Based on Naïve Bayes Interest Data Mining Machine Learning

Abstract

A smart tour route planning algorithm based on a Naïve Bayes interest data mining machine learning is brought forward in the paper, according to the problems of current tour route planning methods. A machine learning model of Naïve Bayes interest data mining is set up by learning a mass of training data on tourists’ interests and needs. Through the recommended interest tourist site classifications from the machine learning module, the optimal tourist site mining algorithm based on the membership degree searching propagating tree of a tourist’s temporary accommodation is set up, which mines and outputs the optimal tourist sites. The mined optimal tourist sites are taken as seed points to set up a tour route planning algorithm based on the optimal propagating tree of a closed-loop structure. Through the proposed algorithm, an experiment is designed and performed to output optimal tour routes conforming to tourists’ needs and interests, including the propagating tree closed-loop structures, a minimum heap of propagating tree weight function value, and a weight function value complete binary tree. We prove that the proposed algorithm has the features of intelligence and accuracy, and it can learn tourists’ needs and interests to output optimal tourist sites and tour routes and ensure that tourists can get the best motive benefits and travel experience in the tour process, by analyzing the experiment data and results.

Towards teaching analytics: a contextual model for analysis of students’ evaluation of teaching through text mining and machine learning classification

“…Also, taking into account the connectedness between the text mining technique (e.g., sentiment analysis) and machine learning or classification models (Ofli et al, 2016 ), the study of Dey et al ( 2016 ) notes that the sentiments which are often found in the comments or feedbacks (e.g., SET) can be categorized by polarity (i.e., positive, neutral, or negative Kalaivani, 2013 ; Litman & Forbes-Riley, 2004 ; Okoye et al, 2020 ), and then utilized to provide valuable pointers or indicators in connection to the various reasons or purposes for which the datasets are analyzed (e.g., the advances in teaching analytical methods and/or students’ evaluation of teaching described in this study). Besides, the authors (Dey et al, 2016 ) also used a statistical method that supports the K-nearest neighbour (KNN) (Abu Alfeilat et al, 2019 ; Ghosh et al, 2020 ; Viji et al, 2020 ) and Naïve Bayes’(Zhou et al, 2020 ) supervised machine learning algorithms to capture the different words/sentence polarities and elements of the subjective styles or patterns.…”

An Improved HMM-Based Approach for Planning Individual Routes Using Crowd Sourcing Spatiotemporal Data

“…Ding et al [31] and zhang et al [32] used an improved neural network for POI recommendation. Based on naïve Bayes interest data mining machine learning, a smart tour route planning algorithm was proposed by Zhou et al [1]. In addition, there are other studies on POI and route recommendations [33][34][35][36][37][38][39].…”

“…, p ijk ), which are the candidate points. The projection proj(p ij , r j ) of point p ij onto r j : proj p ij , r j = arg min ed p ij , p ijk p ijk ∈r j (1) where ed(p ij , p ijk ) are the Euclidean distances between a POI and its projected points on the adjacent road segments.…”

“…In previous studies, many achievements have been reached in successive POI recommendations and tourism route planning. It has fascinated scholars, starting with existing traditional approaches [1,2], and expressly proposing novel optimal route planning methods for moving objects [3,4]. However, important issues are still open.…”

How Social Networks Affect the Spatiotemporal Planning of Smart Tourism: Evidence from Shanghai

“…In traditional sightseeing route planning, the landscape architects or planners usually provide differently themed sightseeing routes according to the special categories of scenic spots-for example, natural, cultural, aesthetic, and recreational spots, are the main focus [8]. This kind of method focuses on a landscape's features rather than the tourists' needs and interests [9]. Moreover, for a diversified demand of visiting time, tourism guides also provide the sightseeing routes classified by the expected consumed time, such as a Table 1.…”