Madrid live: a context-aware recommender systems of leisure plans

Jorro-Aragoneses, Jose L.; Agudo, Maria Belen Diaz; García, Juan Ramón Carbó

doi:10.1109/ictai.2017.00125

Cited by 18 publications

(6 citation statements)

References 13 publications

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“…The go to frequency today's POI is also calculated by photograph statistics. [4] Used GPS trajectories to get special POI attributions. Different sources, such as Google Maps and Wikipedia articles, also are used to mix time windows, finances charges, and travel time between POIs.…”

Section: Literature Surveymentioning

confidence: 99%

See 1 more Smart Citation

An Efficient Two-Layer Framework for Tour Sense Recommendation

D¹,

Soundrya²,

Kumaran³

et al. 2022

ECS Trans.

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The paper shows a cutting edge prototype system, which can recommend most comprehensive travel plans that include brand new, diverse latest interest factors (POIs). It systematically gathers and analyzes data on thousands of cutting-edge tourism destinations and geographical nodes. Tour feel is a recommendation framework which examines the preference information modern day diverse tourists based totally on the transport records collected from various towns. Humans can get properly-in shape path plans, which consist of a sequence brand new points cutting-edge interest (POIs) primarily based on vacationers' constraints and goals, way to the advancement ultra-modern excursion recommendation. Multiple scenic spots and entrances are common in big-scale POIs, which are known as brilliant-POIs in this article. Maximum contemporary excursion advice algorithms, alternatively, forget the big expertise determined inside super POIs. A layer machine, which takes into account the ultra-modern route architecture (outer model) and panoramic routes within outstanding POIs (internal model). A full-on-integration-based Embedded Keep VND algorithm is used to merge templates. A greedy randomized adjustable path advent system (grasp) for local improvements in the internal version of the outer version and variable neighborhood descent (VND). The first rate-POI is considered "meta node" in the development of trendy outer routes. Dijkstra Algorithm with Pruning dynamically reviews the most satisfying path within the exquisite POI in the internal model to conform to the outer direction. The obtained end results provide the traveller with the ultimate quality route and the focus point chosen by the users.

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Section: Literature Surveymentioning

confidence: 99%

“…Randomized Greedy Adaptive quest (draw closely) is currently applied in order to compile a range of modern routes that are most desirable [4]. Any new release is added to a Candidates list and the promising vertices are randomly chosen to be inserted, ensuring a greedy and unpredictable mix of the construction.…”

Section: Grasp: Building Insert -Basedmentioning

confidence: 99%

An Efficient Two-Layer Framework for Tour Sense Recommendation

D¹,

Soundrya²,

Kumaran³

et al. 2022

ECS Trans.

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show abstract

“…The authors of [50] suggested a context-aware web services recommendation for modelling impact on the user's expectations on user location updates and location similarity mining based on the user location in the smart city context. The authors of [142][143][144][145][146] proposed a context-aware recommendation system using smartphone sensors integrated with smart city applications and e-tourism, another recommendation system based on tourist context [147,148]. The authors of [49,73,149,150] proposed a system of travel recommendations that mines appropriate locations, context, user preferences [151][152][153] users' reviews [154], sentiments analysis [1], and users' physical and psychological functionality levels [155].…”

Section: Traveling and Poimentioning

confidence: 99%

Systematic Review of Contextual Suggestion and Recommendation Systems for Sustainable e-Tourism

et al. 2021

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Agenda 2030 of Sustainable Development Goals (SDGs) 9 and 11 recognizes tourism as one of the central industries to global development to tackle global challenges. With the transformation of information and communication technologies (ICT), e-tourism has evolved globally to establish commercial relationships using the Internet for offering tourism-related products, including giving personalised suggestions. The contextual suggestion has emerged as a modified recommendation system that is integrated with information-retrieval techniques within large databases to provide tourists with a list of suggestions based on contexts, such as location, time of day, or day of the week (weekdays or weekends). This study surveyed literature in the field of contextual suggestion and recommendation systems with a focus on e-tourism. The concerns linked with approaches used in contextual suggestion and recommendation systems are highlighted in this systematic review, while motivations, recommendations, and practical implications in e-tourism are also discussed in this paper. A query search using the keywords “contextual suggestion system”, “recommendation system”, and “tourism” identified 143 relevant articles published from 2012 to 2020. Four major repositories are considered for searching, namely, (i) Science Direct, (ii) Scopus, (iii) IEEE, and (iv) Web of Science. This review was carried out under the protocols of four phases, namely, (i) query searching in major article repositories, (ii) removal of duplicates, (iii) scan of title and abstract, and (iv) complete reading of articles. To identify the gaps in current research, a taxonomy analysis was exemplified into categories and subcategories. The main categories were highlighted as (i) review articles, (ii) model/framework, and (iii) applications. Critical analysis was carried out on the basis of the available literature on the limitations of approaches used in contextual suggestion and recommendation systems. In conclusion, the approaches used are mainly based on content-based filtering, collaborative filtering, preference-based product ranking, and language modelling. The evaluation measures for the contextual suggestion system include precision, normalized discounted cumulative, and mean reciprocal rank, while test collections comprise Internet resources. Given that the tourism industry contributed to the environmental and social-economic development, contextual suggestion and recommendation systems have presented themselves to be relevant in integrating and achieving SDG 9 and SDG 11 in many ways such as web-based e-services by the government sector and smart gadgets based on reliable and real-time data and information for city planners as well as law enforcement personnel in a sustainable city.

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“…Previous literature used several criteria (i.e., smart key concepts) in e‐tourism development for different categories. Thus, multiple criteria should be considered when evaluating and benchmarking smart e‐tourism data management applications 1,20–24 . The data variation among these criteria in each smart e‐tourism data management application increases with the complex task.…”

Section: Introductionmentioning

confidence: 99%

“…11 Moreover, smart e-tourism includes multiple key concepts supported by information and communication technologies. 19 According to References [1,[20][21][22][23][24] the smart e-tourism sector can be defined using several concepts: social media, recommender systems, big data, privacy protection, Internet of Things (IoT), user modeling, augmented reality (AR), context awareness, real-time, user experience, theoretical contributions, and cultural heritage. Smart e-tourism includes two categories: tourism marketing and the smart-based tourism recommendation system (smart-based TRS).…”

Section: Introductionmentioning

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

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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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Madrid live: a context-aware recommender systems of leisure plans

Cited by 18 publications

References 13 publications

An Efficient Two-Layer Framework for Tour Sense Recommendation

An Efficient Two-Layer Framework for Tour Sense Recommendation

Systematic Review of Contextual Suggestion and Recommendation Systems for Sustainable e-Tourism

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

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