A novel recommendation algorithm frame for tourist Spots based on multi-clustering bipartite graphs

Xiong, Han; Zhou, Ya; Hu, Cailin; Wei, Xiafei; Li, Lin

doi:10.1109/icccbda.2017.7951924

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…(2) content [30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47], (3) context [48][49][50][51][52][53][54][55][56][57] and hybrid models [58][59][60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75]. In smart e-tourism, undeniable smart key concept attributes variations are observed for smart-based TRS and tourism marketing strategies.…”

Section: Introductionmentioning

confidence: 99%

Based on neutrosophic fuzzy environment: a new development of FWZIC and FDOSM for benchmarking smart e-tourism applications

Alamoodi

Mohammed

Albahri

et al. 2022

Complex Intell. Syst.

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The task of benchmarking smart e-tourism applications based on multiple smart key concept attributes is considered a multi-attribute decision-making (MADM) problem. Although the literature review has evaluated and benchmarked these applications, data ambiguity and vagueness continue to be unresolved issues. The robustness of the fuzzy decision by opinion score method (FDOSM) and fuzzy weighted with zero inconsistency (FWZIC) is proven compared with that of other MADM methods. Thus, this study extends FDOSM and FWZIC under a new fuzzy environment to address the mentioned issues whilst benchmarking the applications. The neutrosophic fuzzy set is used for this purpose because of its high ability to handle ambiguous and vague information comprehensively. Fundamentally, the proposed methodology comprises two phases. The first phase adopts and describes the decision matrices of the smart e-tourism applications. The second phase presents the proposed framework in two sections. In the first section, the weight of each attribute of smart e-tourism applications is calculated through the neutrosophic FWZIC (NS-FWZIC) method. The second section employs the weights determined by the NS-FWZIC method to benchmark all the applications per each category (tourism marketing and smart-based tourism recommendation system categories) through the neutrosophic FDOSM (NS-FDOSM). Findings reveal that: (1) the NS-FWZIC method effectively weights the applications’ attributes. Real time receives the highest importance weight (0.402), whereas augmented reality has the lowest weight (0.005). The remaining attributes are distributed in between. (2) In the context of group decision-making, NS-FDOSM is used to uniform the variation found in the individual benchmarking results of the applications across all categories. Systematic ranking, sensitivity analysis and comparison analysis assessments are used to evaluate the robustness of the proposed work. Finally, the limitations of this study are discussed along with several future directions.

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Section: Introductionmentioning

confidence: 99%

Based on neutrosophic fuzzy environment: a new development of FWZIC and FDOSM for benchmarking smart e-tourism applications

Alamoodi

Mohammed

Albahri

et al. 2022

Complex Intell. Syst.

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Managing customer knowledge through the use of big data analytics in tourism research

Centobelli

Ndou

2019

Current Issues in Tourism

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A Systematic Survey of Tourism Recommender System Techniques and Challenges

Singh¹,

Kumar²,

Sharma³

et al. 2022

JISMAC

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Since the epidemic, there has been a significant drop in the tourism sector, thereby affecting several tourism-dependent nations. However, after COVID-19, people have been wanting to go out to visit places. Since the online era has been trending, people might take help from some websites that use recommender systems to suggest the desired destination which includes basic amenities like the places to visit, hotels, traveling, food, etc. to the user. This paper focuses on some key ideologies that are mostly used in most of the user searches. It discusses the latest machine learning and deep learning techniques used in tourism recommendation and the challenges that are being faced to date by the recommender systems. Recommender systems help to organize a large amount of data available online just by getting the reviews and looking at the previous history of the users. The latest papers have been classified and the papers are analyzed and categorized according to their applications.

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A novel recommendation algorithm frame for tourist Spots based on multi-clustering bipartite graphs

Cited by 3 publications

References 2 publications

Based on neutrosophic fuzzy environment: a new development of FWZIC and FDOSM for benchmarking smart e-tourism applications

Based on neutrosophic fuzzy environment: a new development of FWZIC and FDOSM for benchmarking smart e-tourism applications

Managing customer knowledge through the use of big data analytics in tourism research

A Systematic Survey of Tourism Recommender System Techniques and Challenges

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