A Novel Multi-Objective and Multi-Constraint Route Recommendation Method Based on Crowd Sensing

Zheng, Xiaoyao; Luo, Yonglong; Sun, Liping; Yu, Qingying; Ji, Zhang; Chen, Siguang

doi:10.3390/app112110497

Cited by 10 publications

(6 citation statements)

References 34 publications

(55 reference statements)

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“…e dynamic user interest weighting formula that combines the frequency of visits to the dynamic user interest model can consider not only the one factor of time, but also the frequency of visits to the interest. In this study, the dynamic user interest model [24] is constructed by combining the time forgetting factor and the frequency of access to the interest, as shown in (1)…”

Section: Dynamic User Interest Modelmentioning

confidence: 99%

[Retracted] Research on the Recommendation Algorithm of Rural Tourism Routes Based on the Fusion Model of Multiple Data Sources

Qiao

Peng

2022

Discrete Dynamics in Nature and Society

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Rural tourism has become an important force in implementing the rural revitalisation strategy and accelerating rural economic development. The hectic pace of life has made more and more city dwellers yearn for rural life, and travelling in the countryside has become their weekend choice. However, the current level of rural tourism informationization is low, the publicity is insufficient, the tourists’ awareness is low, and the source of customers is seriously insufficient. To this end, this paper proposes a relatively novel multidata source fusion tourism recommendation algorithm, which adopts the idea of tensor orthogonal decomposition and fuses multisource data models to predict the target domain’s for rating. The integrated consideration of multiple data sources under the do-it-yourself approach assists the target domain to discover the target user neighbourhood users more quickly and to discover the user’s interest degree more accurately. It is worth pointing out that the recommendation algorithm proposed in this paper under the fusion of multiple data sources is not necessarily applicable to data sources with weak correlation, such as travel data sources and music data sources, which are relatively weakly correlated, and the algorithm is slightly weak in making predictions of user preferences.

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Section: Dynamic User Interest Modelmentioning

confidence: 99%

[Retracted] Research on the Recommendation Algorithm of Rural Tourism Routes Based on the Fusion Model of Multiple Data Sources

Qiao

Peng

2022

Discrete Dynamics in Nature and Society

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“…If the ant has traveled all the points or is not allowed to visit any more, the ant has completed its journey. (iii) MOVNS [28]: In this paper, a novel multiobjective and multiconstraint tour route recommendation method is proposed. First, ArcMap was used to model the actual road network.…”

Section: Baseline and Proposed Algorithmsmentioning

confidence: 99%

Personalized Route Recommendation with Hybrid Tabu Search Algorithm Based on Crowdsensing

Han

Zheng

Guan

et al. 2023

International Journal of Intelligent Systems

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In the postmodern era of tourism, tourists’ behavior has undergone a substantial change and the demand of customized experience dominates the tourism market. The traditional single-objective travel route recommendation method fails to meet the multiobjective needs of users. To handle this problem, a multiobjective hybrid tabu search algorithm for urban travel route recommendations is proposed in this paper. First, the rating and level of attractions, as well as the corresponding information of hotels and restaurants within a certain radius, are considered. Then, based on this information, a crowdsensing scoring method is established. Second, the hybrid particle swarm genetic optimization algorithm is exploited to generate a single-object route, and the fast nondominated Pareto sorting algorithm is exploited to find the optimized solution. Then, the hybrid tabu algorithm is used to optimize the personalized route chosen according to multiple objects set by users. This algorithm combines the global search ability of the genetic algorithm and the neighborhood search ability of the tabu algorithm to prevent convergence from falling into a local optimum. Finally, the experiments are conducted on the real-world data collected from the Dianping and Ctrip web sites. The comparison with baseline algorithms indicates that the algorithm proposed in this paper provides accurate and reasonable route recommendations for users.

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“…Generally, it takes a value of 4, and a gradient particle swarm algorithm is constructed. d is assumed that in a dimensional gradient particle swarm search space, m represents the population composed of particles and S � P 1 , P 2 , • • • , P m 􏼈 􏼉 represents the clustering center of tourists' urban tourism interest preference intelligent search particles in the current dimensional solution space [16]. It represents the current optimization speed of the traversing particle…”

Section: Optimization Of the Recommendation Algorithmmentioning

confidence: 99%

A Real-Time Tourism Route Recommendation System Based on Multitime Scale Constraints

Song

Jiao

2023

Mobile Information Systems

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In order to increase the capability of real-time intelligent recommendation of tourists’ information on cross-regional city-level tourist routes with epidemic normalization, a real-time intelligent recommendation algorithm for cross-regional city-level tourist routes with epidemic normalization based on multi-time scale constraints is proposed. Under the training of limited samples, the tourist correlation model of the epidemic normalization of cross-regional city-level tourist routes is created. In addition, two kernel functions i.e. the mixed and the global are assembled to excerpt the correspondence features of the epidemic normalization cross-regional city-level tourist route recommendation information. As a result, the well-known particle swarm optimization (PSO) procedure and algorithm with multitime scale constraints are adopted to carry out the adaptive learning of the epidemic normalization cross-regional city-level tourist route recommendation, and the convergence control of the recommended method is comprehended through mining the geographic information data sets of cities. This paper analyzes the universality and ergodicity of tourists' personal interest preferences and social characteristics in urban tourism and combines a gradient algorithm to carry out particle swarm evolution and self-adaptive optimization for the recommendation of cross-regional city-level tourist routes with a normalized epidemic situation, so as to realize the group real-time intelligent recommendation of tourists’ information on cross-regional city-level tourist routes with the normalized epidemic situation. The model outcomes indicate that the exactitude and precision of cross-regional city-level tourism route information recommendation with this algorithm are decent, and the convergence of the swarm intelligence optimization (SIO) problem is robust, which can circumvent dipping into the local optimal solution in the process of real-time intelligent recommendation of tourism routes and improve the intelligence and global stability of cross-regional city-level tourism route recommendation with epidemic normalization.

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A Novel Multi-Objective and Multi-Constraint Route Recommendation Method Based on Crowd Sensing

Cited by 10 publications

References 34 publications

[Retracted] Research on the Recommendation Algorithm of Rural Tourism Routes Based on the Fusion Model of Multiple Data Sources

[Retracted] Research on the Recommendation Algorithm of Rural Tourism Routes Based on the Fusion Model of Multiple Data Sources

Personalized Route Recommendation with Hybrid Tabu Search Algorithm Based on Crowdsensing

A Real-Time Tourism Route Recommendation System Based on Multitime Scale Constraints

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