Smart city: Recommendation of personalized services in patrimony tourism

Benfares, Chaymae; Idrissi, Younès El Bouzekri El; Amine, Aouatif

doi:10.1109/cist.2016.7805003

Cited by 8 publications

(8 citation statements)

References 13 publications

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“…Equation ( 5) is an objective function that maximizes the POI distance and user interest preferences in the recommended route. Equations ( 6) to (10) are the constraints of equation (5). Equation (6) ensures that the user's travel starting point is 1 p and the end point is n p .…”

Section: Proposed Algorithmmentioning

confidence: 99%

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A Travel Route Recommendation Algorithm based on Interest Theme and Distance Matching

Cheng

2021

Preprint

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To solve the problem of low accuracy of traditional travel route recommendation algorithm, a travel route recommendation algorithm based on interest theme and distance matching is proposed in this paper. Firstly, the real historical travel footprints of users are obtained through analysis. Then, the user's preferences of interest theme and distance matching are proposed based on the user's stay in each scenic spot. Finally, the optimal travel route calculation method is designed under the given travel time limit, starting point and end point. Experiments on the real data set of the Flickr social network showed that the proposed algorithm has a higher accuracy rate and recall rate, compared with the traditional algorithm that only considers the interest theme and the algorithm which only considers the distance matching

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Section: Proposed Algorithmmentioning

confidence: 99%

“…), online data that can describe users' interest preferences is becoming more and more abundant. This makes the recommendation of tourism products become one of the hot spots in the research of recommendation system [4][5].…”

Section: Introductionmentioning

confidence: 99%

A Travel Route Recommendation Algorithm based on Interest Theme and Distance Matching

Cheng

2021

Preprint

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“…Collaborative filtering is the most popular approach, whereby recommendation is carried out through real-life collaboration such as user A recommends information to user B (Benfares et al 2016). This is interpreted in the systems as the situation that users are more likely to be interested in information that are already liked by other users Fig.…”

Section: Collaborative Filtering Recommendersmentioning

confidence: 99%

“…Furthermore, Benfares et al (2017) applied semantic of services to provide recommendations in smart cities to address the generation and selection of customized and relevant services to support real-time decision making of users. Additionally, Benfares et al (2016) designed a personalized architecture for patrimony tourism recommendation services in smart city. The architecture utilizes collaborative filtering method to provide personalized tourist recommendation services based on user's profile.…”

Section: Prior Recommender Systems In Smart City Domainmentioning

confidence: 99%

A case-based reasoning recommender system for sustainable smart city development

Anthony

2020

AI & Soc

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With the deployment of information and communication technologies (ICTs) and the needs of data and information sharing within cities, smart city aims to provide value-added services to improve citizens’ quality of life. But, currently city planners/developers are faced with inadequate contextual information on the dimensions of smart city required to achieve a sustainable society. Therefore, in achieving sustainable society, there is need for stakeholders to make strategic decisions on how to implement smart city initiatives. Besides, it is required to specify the smart city dimensions to be adopted in making cities smarter for sustainability attainment. But, only a few methods such as big data, internet of things, cloud computing, etc. have been employed to support smart city attainment. Thus, this study integrates case-based reasoning (CBR) as an artificial intelligence technique to develop a recommender system towards promoting smart city planning. CBR provides suggestions on smart city dimensions to be adopted by city planners/decision-makers in making cities smarter and sustainable. Accordingly, survey data were collected from 115 respondents to evaluate the applicability of the implemented CBR recommender system in relation to how the system provides best practice recommendations and retaining of smart city initiatives. Results from descriptive and exploratory factor analyses suggest that the developed system is applicable in supporting smart city adoption. Besides, findings from this study are expected to provide valuable insights for practitioners to develop more practical strategies and for researchers to better understand smart city dimensions.

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“…With the rise of more and more online travel websites (such as Expedia, Travelzoo, tuniu), more and more online data can describe users' interests and preferences. This makes tourism product recommendation become one of the hotspots of recommendation system research [4,5].…”

Section: Introductionmentioning

confidence: 99%

A travel route recommendation algorithm based on interest theme and distance matching

Cheng

2021

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

To solve the problem of low accuracy of traditional travel route recommendation algorithm, a travel route recommendation algorithm based on interest theme and distance matching is proposed in this paper. Firstly, the real historical travel footprints of users are obtained through analysis. Then, the user’s preferences of interest theme and distance matching are proposed based on the user’s stay in each scenic spot. Finally, the optimal travel route calculation method is designed under the given travel time limit, starting point, and end point. Experiments on the real data set of the Flickr social network showed that the proposed algorithm has a higher accuracy rate and recall rate, compared with the traditional algorithm that only considers the interest theme and the algorithm which only considers the distance matching.

show abstract

Smart city: Recommendation of personalized services in patrimony tourism

Cited by 8 publications

References 13 publications

A Travel Route Recommendation Algorithm based on Interest Theme and Distance Matching

A Travel Route Recommendation Algorithm based on Interest Theme and Distance Matching

A case-based reasoning recommender system for sustainable smart city development

A travel route recommendation algorithm based on interest theme and distance matching

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