Independent travel recommendation algorithm based on analytical hierarchy process and simulated annealing for professional tourist

Pan, Qingyi; Wang, Xiaoying

doi:10.1007/s10489-017-1014-0

Cited by 18 publications

(12 citation statements)

References 23 publications

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“…If the values of δ and χ are equal to one, then it is a TFN otherwise, the number is TSFN. The values of (l,m,u) are taken as (1,2,3). Similarly, this method can be extended to Trapezoidal Fuzzy Numbers (TrFN).…”

Section: Algorithm 1: Modified Monte Carlo Simulationmentioning

confidence: 99%

See 1 more Smart Citation

Obtaining Crisp Priorities for Triangular and Trapezoidal Fuzzy Judgments

Goyal¹,

Singh²,

Kalra³

et al. 2022

Computer Systems Science and Engineering

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This paper proposes anoptimal fuzzy-based model for obtaining crisp priorities for Fuzzy-AHP comparison matrices. Crisp judgments cannot be given for real-life situations, as most of these include some level of fuzziness and complexity. In these situations, judgments are represented by the set of fuzzy numbers. Most of the fuzzy optimization models derive crisp priorities for judgments represented with Triangular Fuzzy Numbers (TFNs) only. They do not work for other types of Triangular Shaped Fuzzy Numbers (TSFNs) and Trapezoidal Fuzzy Numbers (TrFNs). To overcome this problem, a sum of squared error (SSE) based optimization model is proposed. Unlike some other methods, the proposed model derives crisp weights from all of the above-mentioned fuzzy judgments. A fuzzy number is simulated using the Monte Carlo method. A threshold-based constraint is also applied to minimize the deviation from the initial judgments. Genetic Algorithm (GA) is used to solve the optimization model. We have also conducted casestudiesto show the proposed approach's advantages over the existingmethods. Results show that the proposed model outperforms other models to minimize SSE and deviation from initial judgments. Thus, the proposed model can be applied in various real time scenarios as it can reduce the SSE value upto 29% compared to the existing studies.

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Section: Algorithm 1: Modified Monte Carlo Simulationmentioning

confidence: 99%

“…Multi-Criteria decision-making (MCDM) methods are used to solve complex decision-making systems. Rao [1] has listed many decision-making methods like AHP [2][3] ELECTRE [4], TOPSIS [5], PROMETHEE [6] and GRA [7] etc. AHP is the most popular method among these MCDM methods.…”

Section: Introductionmentioning

confidence: 99%

Obtaining Crisp Priorities for Triangular and Trapezoidal Fuzzy Judgments

Goyal¹,

Singh²,

Kalra³

et al. 2022

Computer Systems Science and Engineering

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show abstract

“…e data source in our experiment is part of the product data and user behavior data from Jing Dong Mall, which is the largest independent business-to-consumer e-commerce business in China by transaction volumes [24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42].…”

Section: Datasets and Parameter Setupmentioning

confidence: 99%

Cross-Border E-Commerce Personalized Recommendation Based on Fuzzy Association Specifications Combined with Complex Preference Model

Xiang

Zhang

2020

Mathematical Problems in Engineering

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Since cross-border e-commerce involves the export and import of commodities, it is affected by many policies and regulations, resulting in some special requirements for the recommendation system, which makes the traditional collaborative filtering recommendation algorithm less effective for the cross-border e-commerce recommendation system. To address this issue, a simple yet effective cross-border e-commerce personalized recommendation is proposed in this paper, which integrates fuzzy association rule and complex preference into a recommendation model. Under the constraint of fuzzy association rules, a hybrid recommendation model based on user complex preference features is constructed to mine user preference features, and personalized commodities recommendation is realized according to user behavior preference. Compared with the traditional recommendation algorithm, the improved algorithm reduces the impact of data sparsity. The experiment also verifies that the improved fuzzy association rule algorithm has a better recommendation effect than the existing state-of-the-art recommendation models. The recommendation system proposed in this paper has better generalization and has the performance to be applied to real-life scenarios.

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“…The intelligent recommendation system proposed by Chen et al in [21] is based on Hadoop in order to mend the scalability of the recommendation facility. A three-step algorithm for the recommendation of independent travel routes is proposed by Pan et al [22]. In the first step, a 0-1 knapsack problem is modeled that under precise conditions selects landmarks in the destination.…”

Section: Related Workmentioning

confidence: 99%

Optimal Travel Route Recommendation Mechanism Based on Neural Networks and Particle Swarm Optimization for Efficient Tourism Using Tourist Vehicular Data

Malik

Kim

2019

Sustainability

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With the swift growth in tourism all around the world, it has become vital to introduce advancements and improvements to the services provided to the tourists, in order to ensure their ease of travel and satisfaction. Optimal travel route identification and recommendation is one of these amenities, which requires our attention as a basic and much-needed facility to improve the experience of travelers. In this work, we propose an optimal route recommendation mechanism for the prediction of the next tourist attraction and optimal route recommendation to the predicted tourist attraction. The algorithms used in the proposed methodology are neural networks for prediction and particle swarm optimization for finding the optimal route. We design an objective function for the route optimization based on the five route parameters of distance, road congestion, weather conditions, route popularity, and user preference. The data used is the tourism data of Jeju Island from December 2016 to December 2017. The performance analysis in the prediction mechanism is performed based on the accuracy of test data results with varying route sizes, while for route optimization, the obtained results are compared with the non-optimized technique. Also, comparisons analysis is performed by comparing the performance of the applied particle swarm optimization algorithm with an identical system-level implementation of the genetic algorithm, which is one of most widely used optimization algorithms. An extended comparative analysis with some related recommendation system studies is also performed based on key optimization factors in route optimization.

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Independent travel recommendation algorithm based on analytical hierarchy process and simulated annealing for professional tourist

Cited by 18 publications

References 23 publications

Obtaining Crisp Priorities for Triangular and Trapezoidal Fuzzy Judgments

Obtaining Crisp Priorities for Triangular and Trapezoidal Fuzzy Judgments

Cross-Border E-Commerce Personalized Recommendation Based on Fuzzy Association Specifications Combined with Complex Preference Model

Optimal Travel Route Recommendation Mechanism Based on Neural Networks and Particle Swarm Optimization for Efficient Tourism Using Tourist Vehicular Data

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