A simulated annealing-based recommender system for solving the tourist trip design problem

Tlili, Takwa; Krichen, Saoussen

doi:10.1016/j.eswa.2021.115723

Cited by 31 publications

(14 citation statements)

References 34 publications

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“…The rough set is a fuzzy analysis method, which realizes the estimation and budget of data, finds out the characteristic data from the massive data, and achieves the purpose of reducing the data scale [ 9 ]. At present, the rough set is widely used in the field of big data analysis, which can filter big data and improve the calculation accuracy [ 10 ]. In addition, increasing the threshold in the rough set can adjust the calculation accuracy and meet the needs of different calculation models.…”

Section: Literature Reviewmentioning

confidence: 99%

[Retracted] Analysis and Research on Audience Satisfaction of Performing Arts Projects in Tourist Scenic Spots Based on the ASCI Model and Big Data

Ding

2022

Journal of Environmental and Public Health

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The development trend of tourism performance networking, although convenient for audience consumption, also makes the performance information present the development trend of big data. In the mass of information, how to accurately locate products and improve audience satisfaction is an urgent problem to be solved. In order to better explore the evaluation of tourism performance by the customer satisfaction evaluation model, analyze the development prospect of tourism in Jiangxi Province in the future, improve the customer satisfaction evaluation model with rough set, and propose a composite customer satisfaction evaluation model. By setting the adjustment value of the evaluation index, the model not only avoids the “false eigenvalue” of the satisfaction evaluation result but also simplifies the calculation process of the model and improves the accuracy, calculation efficiency, and single data processing capacity of the satisfaction evaluation. According to the MATLAB simulation results, the composite customer satisfaction evaluation model constructed in this study is better, the calculation accuracy is >97%, and the calculation time is 40 seconds, which are better than the original customer satisfaction evaluation model. Therefore, the composite customer satisfaction evaluation model can be applied to the evaluation of tourism performance products to provide data support for the evaluation price of audience satisfaction in Jiangxi Province.

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Section: Literature Reviewmentioning

confidence: 99%

[Retracted] Analysis and Research on Audience Satisfaction of Performing Arts Projects in Tourist Scenic Spots Based on the ASCI Model and Big Data

Ding

2022

Journal of Environmental and Public Health

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“…A ui � r ui , user u interested on activity i, if such rating exists, ?, if no such rating. 􏼨 (4) CF systems try to replace all the "question marks" in A with some optimal guesses; the goal is to minimize the RMSE (root mean square error) when predicting the user interests on a test set (which is, of course, unknown during the training phase), that is to minimize…”

Section: Metrics Let T Be a Multilabel Dataset Consistingmentioning

confidence: 99%

“…RS can be de ned as a personalization tool that provides people with a list of items that best t their individual preferences, restrictions, or tastes [3]. One of the interesting applications of RS lies in the trip planning area [4].…”

Section: Introductionmentioning

confidence: 99%

Developing Tourism Users’ Profiles with Data-Driven Explicit Information

Norouzi

Baziyad

Noghabi

et al. 2022

Mathematical Problems in Engineering

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In recommender systems (RSs), explicit information is often preferred over implicit because it is much more accurate than implicit or predicted information; for example, the user can enter information about his interests directly into the system, and the system will generate accurate recommendations for him. Receiving explicit information, however, may be difficult for a system. Explicit demographic information might be uncomfortable for some users, and extremely common questions, such as race, gender, income, and age, can lead to bias and unfair recommendations. As a result, in this study, we propose a method, in which information collected from a new user does not contain demographic information, and enquired explicit information is data driven. Users’ interest in tourism activities is used to identify seven categories of tourism. The mapping between extracted categories and activities is established with a multilabel classification (MLC) algorithm. The user’s interest in 18 tourism activities is predicted by rating only seven tourism categories. Common MLC algorithms with different classifiers were used to evaluate the proposed method. The best result relates to binary relevance with the Naïve Bayes classifier, which also outperforms the entitled algorithms in collaborative filtering (CF) systems as baseline models. The proposed method can capture users’ interests and develop their profiles without receiving demographic information. Also, compared to CF, in addition to a slight advantage in metrics, it only requires seven ratings to predict user interest in 18 activities. In contrast, CF algorithms require at least 15 user ng records to predict user interest in unknown activities (3-4 activities) to achieve a performance close to the proposed method.

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“…In the recommendation of tourist routes, the historical interest characteristics of users are also taken into account, and at the same time, the current and historic interest is achieved [24]. In the process of attribute scoring prediction, it is necessary to consider not only the user's own rating of the item attributes but also the rating of the user's nearest neighbor on the item attributes, to obtain the target user's prediction score of unknown items.…”

Section: Mobile Information Systemsmentioning

confidence: 99%

Tourism Destination Recommendation Based on Association Rule Algorithm

Lou

2022

Mobile Information Systems

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With the development of information technology and the arrival of intelligent era, the tourism industry has also changed from traditional information service to intelligence. In this study, the recommendation system is studied. By comparing the advantages and disadvantages of various recommendation methods, the association rule algorithm is selected as the recommendation method in this study. Then, the user’s operation behavior is obtained and the personalized tourism destination recommendation model is established. This model is based on the user’s access operation behavior data, and it introduces the user’s interest degree as a parameter to measure the operation behavior data, forming the basic data of data mining. The popularity of tourist routes and the change weight of longitudinal interest characteristics among users are introduced into the algorithm. The change weight of user’s lateral interest characteristics is added to the prediction of target users’ future interest. Compared with the classified recommendation method, users can easily locate the information of scenic spots they are interested in under the guidance of scenic spot recommendation. The results further verify that the research of this model has certain practical significance and practical value.

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A simulated annealing-based recommender system for solving the tourist trip design problem

Cited by 31 publications

References 34 publications

[Retracted] Analysis and Research on Audience Satisfaction of Performing Arts Projects in Tourist Scenic Spots Based on the ASCI Model and Big Data

[Retracted] Analysis and Research on Audience Satisfaction of Performing Arts Projects in Tourist Scenic Spots Based on the ASCI Model and Big Data

Developing Tourism Users’ Profiles with Data-Driven Explicit Information

Tourism Destination Recommendation Based on Association Rule Algorithm

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