Designing the Content Analyzer of a Travel Recommender System

Binucci, Carla; Luca, Felice De; Giacomo, Emilio Di; Liotta, Giuseppe; Montecchiani, Fabrizio

doi:10.1016/j.eswa.2017.06.028

Cited by 30 publications

(11 citation statements)

References 27 publications

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“…Cultural heritage (C10): Integrating cultural heritage with current technology in smart e‐tourism data management applications to provide solutions to problems and challenges in smart tourism applications on the basis of cultural heritage 98 …”

Section: Methodsmentioning

confidence: 99%

“…Cultural heritage (C10): Integrating cultural heritage with current technology in smart e-tourism data management applications to provide solutions to problems and challenges in smart tourism applications on the basis of cultural heritage. 98 11. Privacy-preserving (C11): Protecting the users' privacy of smart e-tourism data management applications related to users' data, such as their qualifications, preferences, and GPS locations.…”

Section: Proposal Of Decision Matrices (Dm) Phasementioning

confidence: 99%

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Interval type 2 trapezoidal‐fuzzy weighted with zero inconsistency combined with VIKOR for evaluating smart e‐tourism applications

Krishnan

Mohammed

Alnoor

et al. 2021

Int J of Intelligent Sys

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The benchmarking of smart e‐tourism data management applications falls under the problem of multicriteria decision‐making (MCDM). This claim is supported by three issues: 12 smart key concepts need to be considered in the evaluation, criteria importance, and data variation among these criteria. Thus, an MCDM solution is essential to overcome problem complexity. To end this, this study presents a decision‐making framework on the basis of the extension of interval type 2 trapezoidal‐fuzzy weighted with zero inconsistency (IT2TR‐FWZIC) integrated with the Vlsekriterijumska Optimizcija I Kaompromisno Resenje (VIKOR) method for evaluating and benchmarking the smart e‐tourism data management applications. Our methodology comprises two consecutive phases. In the first phase, a decision matrix is constructed using the intersection between the 12 key concepts and smart e‐tourism data management applications of each category and subcategory in smart e‐tourism. In the second phase, the integration of the IT2TR‐FWZIC formulation and VIKOR is presented to compute the weights for the 12 key concepts and benchmark the smart e‐tourism data management applications for each category. The results are as follows: (1) A clear difference is found among the criteria weights (12 smart key concepts). Specifically, the real‐time criterion achieves the highest importance weight (0.098), whereas augmented reality obtains the lowest weight (0.068). The context‐awareness and recommender systems have the same weight value (0.087), and the other eight criteria are distributed in between. (2) The smart e‐tourism data management applications are evaluated and benchmarked effectively per category and subcategories. (3) Benchmarked applications in each category are subjected to a systematic ranking in the evaluation process. The sensitivity analysis has shown high correlation outcomes to the systematic ranking results over the 31 scenarios of criteria weight changing. Moreover, a comparative analysis of the proposed work with other existing studies is also discussed.

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Section: Methodsmentioning

confidence: 99%

Section: Proposal Of Decision Matrices (Dm) Phasementioning

confidence: 99%

Interval type 2 trapezoidal‐fuzzy weighted with zero inconsistency combined with VIKOR for evaluating smart e‐tourism applications

Krishnan

Mohammed

Alnoor

et al. 2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

show abstract

“…In the past decades, many research works employed content-based recommendation methods widely. There are several works related in news [29], TV program [30], product [31], and tourism domain [1], [32]- [34]. For instance, Binucci et al [32] designed the Content Analyzer and implemented the technique in a system called Cicero for recommending a travel destination.…”

Section: ) Content-based Methodsmentioning

confidence: 99%

“…There are several works related in news [29], TV program [30], product [31], and tourism domain [1], [32]- [34]. For instance, Binucci et al [32] designed the Content Analyzer and implemented the technique in a system called Cicero for recommending a travel destination. The Content Analyzer is a module that receives as input a set of point of interests and a set of topic of interests, and it computes the relevance of each point of interest with respect to each topic of interest.…”

Section: ) Content-based Methodsmentioning

confidence: 99%

Linked Open Data in Location-Based Recommendation System on Tourism Domain: A Survey

Yochum

Chang

et al. 2020

IEEE Access

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Linked open data is a relatively new topic area with great potential in a wide range of fields. In the tourism domain, many studies are using linked open data to address the problem of location-based recommendation by integrating data with other linked open datasets to enrich data and tourism content for reacting to the needs of tourists. This work aims not only to present a systematic review and mapping of the linked open data in location-based recommendation system on tourism domain, but also to provide an overview of the current research status in the area. First, we classify journal papers in this area from 2001 to 2018 by the year of publication. Second, we analyze and categorize journal papers by the different recommendation applications including problem formulations, data collections, proposed algorithms/systems, and experimental results. Third, we group the linked open data sources used in location-based recommendation system on tourism. Next, we summarize the research achievements and present the distribution of the different categories of location-based recommendation applications via linked open data. Last, we also guide the possible future research direction for the linked open data in location-based recommendations on tourism.

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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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Designing the Content Analyzer of a Travel Recommender System

Cited by 30 publications

References 27 publications

Interval type 2 trapezoidal‐fuzzy weighted with zero inconsistency combined with VIKOR for evaluating smart e‐tourism applications

Interval type 2 trapezoidal‐fuzzy weighted with zero inconsistency combined with VIKOR for evaluating smart e‐tourism applications

Linked Open Data in Location-Based Recommendation System on Tourism Domain: A Survey

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

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