Intelligent Tourism Recommendation Algorithm based on Text Mining and MP Nerve Cell Model of Multivariate Transportation Modes

Zhou, Xiao; Su, Mingzhan; Feng, Guanghui; Zhou, Xinghan

doi:10.1109/access.2020.3047264

Cited by 14 publications

(10 citation statements)

References 58 publications

(191 reference statements)

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“…A good recommender system can not only provide users with personalized information service needs, but also allow users to have a high degree of trust in it and improve user stickiness. Whenever users cannot clarify their needs, they think of the recommender system [2]. Recommender systems have been widely used in many fields, among which the most typical and promising application field is digital marketing.…”

Section: Introductionmentioning

confidence: 99%

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Construction of Digital Marketing Recommendation Model Based on Random Forest Algorithm

Gao

Ding

2022

Security and Communication Networks

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The traditional marketing model can no longer meet the needs of users and can not add more benefits to the enterprise, and digital marketing came into being. At present, most of the marketing focus of various enterprises is still mainly on products, and the reflection arc to market changes is long. Therefore, the formulation of marketing activities should always pay attention to changes in user needs and combine corresponding activity planning, product planning, brand building, etc., according to Changes in the target market adjust the content of marketing activities and products in real-time and, at the same time, pay attention to user feedback on products in order to iteratively update products in time, improve product market competitiveness, and optimize the user experience. In this paper, through the study and research of the traditional random forest method and some data processing algorithms, the feature selection and class imbalance problems of random forest are improved, respectively. Through the study of feature selection methods, we can maintain a balance between feature strength and relevance during feature selection and improve the final model classification effect. And through the research and experiment of the imbalanced data classification problem and the random forest algorithm, the method of the random forest model to deal with the imbalanced problem has been improved. After experimental calculation and analysis, it is found that for the effect of the minimum number of samples required for node splitting with different numbers, the best results are obtained when 2 samples are taken as the minimum number of samples required for node splitting, and the average value of the F1 evaluation is 0.1038; for different specifications, the effect of the random forest is the best using the Gini index, and the average value of its F1 evaluation is 0.1033; for the effect analysis of random forests with different numbers of trees, 7 to 10 decision trees are the best, and the F1 evaluation is the best. The average is 0.10175.

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Section: Introductionmentioning

confidence: 99%

“…With replacement, n samples are extracted, and m decision trees are trained with these n samples, and finally, the prediction classification is obtained. Multiple random variables are usually calculated using the chain rule, as shown in equation (2).…”

Section: Introduction To Random Forest Algorithmmentioning

confidence: 99%

Construction of Digital Marketing Recommendation Model Based on Random Forest Algorithm

Gao

Ding

2022

Security and Communication Networks

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“…As can be seen from equation (5), there are mainly 4 parameters that determine the probability of path selection: pheromone value τ ij (t) of the edge (i, j) at time t , heuristic function η ij (t), information heuristic factor α, and expectation heuristic factor β.…”

Section: Solution Process Of Weighting Algorithmmentioning

confidence: 99%

“…In order to select the appropriate heuristic factor α and expected heuristic factor β, we use Oliver 30 in TSPLIB as test data. e default values of parameters are as follows: pheromone intensity Q 100, pheromone volatilization coe cient P 0.3, maximum number of iterations of the algorithm N c−max 100, weighted quantity m 30, and the combination of heuristic factor α and expected heuristic factor β is (α, β) ∈ (1, 3), (1,4), (1,5), (2, 3), (3, 4), (2, 5) { }. Each group of combinations is solved 10 times, and the mean value is obtained.…”

Section: Parameter Selectionmentioning

confidence: 99%

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Analysis and Study on Intelligent Tourism Route Planning Scheme Based on Weighted Mining Algorithm

2022

Scientific Programming

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In order to study the intelligent tourism route planning scheme based on a weighted mining algorithm, firstly, a traveling salesman problem based on an improved weighted mining algorithm is proposed. Then, based on the traveling salesman problem, the constraints of travel time and driving time are added, and the mathematical model aiming at the minimum number of travel days is established. The improved weighted mining algorithm is used to solve the model: firstly, the scenic spots are divided into regions according to provinces and solved; then, the regions of some scenic spots are modified and solved again according to the solution results, and the specific itinerary is given; finally, based on the route planning problem and considering a variety of transportation modes, a double objective mathematical model with the optimal tourism cost and the best tourism experience is established. When solving, the double objective is reasonably transformed into a single objective, the model is solved by comprehensively considering the tourism cost and tourism experience, and the specific schedule is given according to the solution results.

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Selection and visiting sequence of daily attractions: Multi-day travel itinerary recommendation based on multi-source online data

Xu,

Li,

Fan

2024

Expert Systems with Applications

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Intelligent Tourism Recommendation Algorithm based on Text Mining and MP Nerve Cell Model of Multivariate Transportation Modes

Cited by 14 publications

References 58 publications

Construction of Digital Marketing Recommendation Model Based on Random Forest Algorithm

Construction of Digital Marketing Recommendation Model Based on Random Forest Algorithm

Analysis and Study on Intelligent Tourism Route Planning Scheme Based on Weighted Mining Algorithm

Selection and visiting sequence of daily attractions: Multi-day travel itinerary recommendation based on multi-source online data

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