Multi-Ontology Based Points of Interests (MO-POIS) and Parallel Fuzzy Clustering (PFC) Algorithm for Travel Sequence Recommendation with Mobile Communication on Big Social Media

Kumar, Neelesh; Thangamani, M.

doi:10.1007/s11277-018-5492-0

Cited by 7 publications

(1 citation statement)

References 28 publications

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“…For example, Li [23] reached a revenue sharing scheme between the government and the private sector through bargaining on concession contracts, but the subjectivity of learning efficiency in this method was determined to have a certain impact on its objectivity. Kumar et al [24] used the bargaining model to simulate the bargaining problem between the private partner and the government on the operating period under the condition of information asymmetry. Battiston et al [25] established a bargaining model for energy-saving profit allocation based on revenue sharing of energy performance contract (EPC), and determined the allocation time and proportion of energy-saving benefit.…”

Section: Research On Tot Project Revenue Allocationmentioning

confidence: 99%

Revenue Sharing of a TOT Project in China Based on Modified Shapley Value

Fang

Zhang

et al. 2020

Symmetry

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In recent years, China’s government has encouraged the adoption of the TOT (Transfer-Operate-Transfer) model to realize the marketization of China’s public service stock projects. The TOT model is a cooperation mechanism through sharing investment, revenue and risks between the government and private partner. Therefore, a fair and reasonable revenue sharing method (RSM) is the key to the success of the TOT project. This paper aims to provide a fair and reasonable RSM based on a modified Shapley value with a triangular symmetric fuzzy structure element, which has better motivation, flexibility, forecasting function and dynamic precise distribution function. According to the factors that affect revenue sharing, the Shapley value is improved with initial correction coefficient composed of investment ratio, risk-sharing ratio, execution degree, and fuzzy payment to achieve fairness and reasonableness. The methodology is illustrated by a case study of a TOT project selected from Laohekou city of Hubei province, China. The results testify that the revenue-sharing ratios of participants is positively correlated with the initial correction coefficient, which make the RSM more motivating; and the Shapley value with fuzzy payment by using triangular symmetric fuzzy element function make the RSM more flexible, and it has both forecasting function and precise dynamic distribution function under project revenue uncertainty.

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Section: Research On Tot Project Revenue Allocationmentioning

confidence: 99%

Revenue Sharing of a TOT Project in China Based on Modified Shapley Value

Fang

Zhang

et al. 2020

Symmetry

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WITHDRAWN: Design and development of bird ontology using protégé

Nandhinidevi¹,

Saraswathi²,

Thangamani³

et al. 2021

Materials Today: Proceedings

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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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Multi-Ontology Based Points of Interests (MO-POIS) and Parallel Fuzzy Clustering (PFC) Algorithm for Travel Sequence Recommendation with Mobile Communication on Big Social Media

Cited by 7 publications

References 28 publications

Revenue Sharing of a TOT Project in China Based on Modified Shapley Value

Revenue Sharing of a TOT Project in China Based on Modified Shapley Value

WITHDRAWN: Design and development of bird ontology using protégé

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

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