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The purpose of this paper is to present a method that addresses the data sparsity problem in points of interest (POI) recommendation by introducing spatiotemporal context features based on location-based social network (LBSN) data. The objective is to improve the accuracy and effectiveness of POI recommendations by considering both spatial and temporal aspects.

To achieve this, the paper introduces a model that integrates the spatiotemporal context of POI records and spatiotemporal transition learning. The model uses graph convolutional embedding to embed spatiotemporal context information into feature vectors. Additionally, a recurrent neural network is used to represent the transitions of spatiotemporal context, effectively capturing the user’s spatiotemporal context and its changing trends. The proposed method combines long-term user preferences modeling with spatiotemporal context modeling to achieve POI recommendations based on a joint representation and transition of spatiotemporal context.

Experimental results demonstrate that the proposed method outperforms existing methods. By incorporating spatiotemporal context features, the approach addresses the issue of incomplete modeling of spatiotemporal context features in POI recommendations. This leads to improved recommendation accuracy and alleviation of the data sparsity problem.

The research has practical implications for enhancing the recommendation systems used in various location-based applications. By incorporating spatiotemporal context, the proposed method can provide more relevant and personalized recommendations, improving the user experience and satisfaction.

The paper’s contribution lies in the incorporation of spatiotemporal context features into POI records, considering the joint representation and transition of spatiotemporal context. This novel approach fills the gap left by existing methods that typically separate spatial and temporal modeling. The research provides valuable insights into improving the effectiveness of POI recommendation systems by leveraging spatiotemporal information.

The purpose of this article is to contribute to the digital development and utilization of China’s intangible cultural heritage resources, research on the theft of intangible cultural heritage resources and knowledge integration based on linked data is proposed to promote the standardized description of intangible cultural heritage knowledge and realize the digital dissemination and development of intangible cultural heritage.

In this study, firstly, the knowledge organization theory and semantic Web technology are used to describe the intangible cultural heritage digital resource objects in metadata specifications. Secondly, the ontology theory and technical methods are used to build a conceptual model of the intangible cultural resources field and determine the concept sets and hierarchical relationships in this field. Finally, the semantic Web technology is used to establish semantic associations between intangible cultural heritage resource knowledge.

The study findings indicate that the knowledge organization of intangible cultural heritage resources constructed in this study provides a solution for the digital development of intangible cultural heritage in China. It also provides semantic retrieval with better knowledge granularity and helps to visualize the knowledge content of intangible cultural heritage.

This study summarizes and provides significant theoretical and practical value for the digital development of intangible cultural heritage and the resource description and knowledge fusion of intangible cultural heritage can help to discover the semantic relationship of intangible cultural heritage in multiple dimensions and levels.

The aim of this study was to investigate consumer preferences and profile their food-related lifestyles, as well as to identify consumer groups with similar attitudes/behaviours in the Euro-Mediterranean fruit and vegetable market.

A structured questionnaire was designed drawing from the food related lifestyles instrument and including other factors relevant to fruit and vegetable consumer preferences. The data were collected in an online survey with 925 participants in France, Greece, and Italy. A principal component analysis was conducted to interpret and examine consumers' fruit and vegetable related lifestyles. In addition, a cluster analysis was performed to identify different consumer segments, based on the core dimensions of the food-related lifestyle approach.

In each country, three primary consumer segments were distinguished. Health-conscious individuals were predominant in France and Greece, while quality-conscious consumers were prevalent in Italy. These classifications were determined considering various factors such as purchase motivation, perception of product quality, health concerns, environmental certifications, and price sensitivity.

The food-related lifestyle approach has been adapted instrument to create a customised survey instrument specifically designed to capture the intricacies of fruit and vegetable consumer preferences and priorities in three Euro-Mediterranean Countries.

Despite the increasing demand for renewable energy (RE) as a low-carbon energy source, the transition to RE is very slow in many regions, including Oman, the case country for this study. It is critical to offer strategic insights to energy supply chain participants towards the sustainable transition to renewable energy (STRE). The purpose of this study is to identify viable RE sources in Oman as a case study of a GCC member country, develop a comprehensive framework of STRE, and suggest future research opportunities.

The paper addressed this problem through a country/regional study of Oman by conducting a systematic literature review (SLR) of RE-related peer-reviewed publications spanning over 21 years from January 2000 to February 2021. The qualifying articles are evaluated using template analysis qualitatively to identify viable renewable energy sources, build a holistic framework of STRE and recommend future research opportunities.

Findings confirm the potential of solar, wind, biomass and geothermal energies driven by environmental, economic and social sustainability concerns. However, results suggest that to fast-track the STRE, more emphasis should be accorded to solar and wind energies owing to the geographical composition of Oman. Findings reveal that policies and regulations, advanced and cost-effective technologies, subsidy regimes, grid connectivity and capacity, storage capacity and land availability influence the STRE. Gaps in the literature are identified from the results to clarify and suggest future research opportunities.

To the best of the authors’ knowledge, this is the first study that conducted an SLR that was evaluated using the template analysis technique to build a novel and updated framework that facilitates a crystalline understanding of STRE to guide policymakers and professionals in strategic decision-making.

The purpose of this study was a longitudinal study on knowledge organization (KO) realm structure and cluster concepts and emerging KO events based on co-occurrence analysis.

This longitudinal study uses the co-occurrence analysis. This research population includes keywords of articles indexed in the Web of Science Core Collection 1975–1999 and 2000–2018. Hierarchical clustering, multidimensional scaling and co-occurrence analysis were used to conduct the present research. SPSS, UCINET, VOSviewer and NetDraw were used to analyze and visualize data.

The “Information Technology” in 1975–1999 and the “Information Literacy” in 2000–2018, with the highest frequency, were identified as the most widely used keywords of KO in the world. In the first period, the cluster “Knowledge Management” had the highest centrality, the cluster “Strategic Planning” had the highest density in 2000–2018 and the cluster “Information Retrieval” had the highest centrality and density. The two-dimensional map of KO’s thematic and clustering of KO topics by cluster analysis method indicates that in the periods examined in this study, thematic clusters had much overlap in terms of concept and content.

The present article uses a longitudinal study to examine the KO’s publications in the past half-century. This paper also uses hierarchical clustering and multidimensional scaling methods. Studying the concepts and thematic trends in KO can impact organizing information as the core of libraries, museums and archives. Also, it can scheme information organizing and promote knowledge management. Because the results obtained from this article can help KO policymakers determine and design the roadmap, research planning, and micro and macro budgeting processes.

Space tourism is currently experiencing significant attention because of its rapid and burgeoning development in the present era. This surge has resulted in an unprecedented growth in publications dedicated to unravelling the intricacies of space tourism. However, there is a conspicuous absence of a large-scale bibliometric analysis focusing on space tourism research from 1993 to 2022. Therefore, the aim of this study is to fill this research gap by examining and mapping the scholarly output published across the world in the spectrum of space tourism over the past 30 years (1993–2022).

A corpus of 7,438 publications pertaining to space tourism published from 1993 to 2022 was gathered from the Web of Science Core Collection. Accordingly, bibliometrix package in R and VOSviewer software were used to conduct a comprehensive bibliometric analysis.

The current study highlights a significant surge in publications related to space tourism, indicating a heightened scholarly interest and a significant paradigm shift in its exploration. Scott M. Smith, affiliated with National Aeronautics Space Administration Johnson Space Center, emerges as the most prolific author. Leading journals in disseminating space tourism research are Acta Astronautica and Aviation Space and Environmental Medicine. Keyword analysis revealed hotspots such as “space flight”, “simulated microgravity”, “weightlessness” and “stress”, while research gaps include “skylab”, “shuttle”, “cartilage”, “herpes virus” and “herniation”, offering potential avenues for exploration.

This study’s implications empower stakeholders with actionable insights and deepen the understanding of the evolving landscape of space tourism research, fostering an environment conducive to continuous exploration and innovation in this burgeoning field.

This study enriches the understanding of global space tourism research and offers valuable insights applicable to a diverse audience, including researchers, policymakers and industry stakeholders. The broad applicability of the study’s findings underscores its significance, serving as a guide for strategic decision-making and shaping research agendas in the dynamic realm of space tourism.

This study aims to use geographical information on social media for public opinion risk identification during a crisis.

This study constructs a double-layer network that associates the online public opinion with geographical information. In the double-layer network, Gaussian process regression is used to train the prediction model for geographical locations. Second, cross-space information flow is described using local government data availability and regional internet development indicators. Finally, the structural characteristics and information flow of the double-layer network are explored to capture public opinion risks in a fine-grained manner. This study used the early stages of the COVID-19 outbreak for validation analyses, and it collected more than 90,000 pieces of public opinion data from microblogs.

In the early stages of the COVID-19 outbreak, the double-layer network exhibited a radiating state, and the information dissemination was more dependent on the nodes with higher in-degree. Moreover, the double-layer network structure showed geographical differences. The risk contagion was more significant in areas where information flow was prominent, but the influence of nodes was reduced.

Public opinion risk identification that incorporates geographical scenarios contributes to enhanced situational awareness. This study not only effectively extends geographical information on social media, but also provides valuable insights for accurately responding to public opinion.

Due to the constantly increasing competitiveness along with the complexity of knowledge, firms perceive collaboration as a key strategy that preserves firms' radical innovation performance. In this context, this paper aims to examine how firms’ partners’ diversity in open innovation activities influences the development of radical innovations, critical for social development. In particular, this study analyzes how the functional and geographical breadth of the firm’s collaboration portfolio affects its radical innovation performance. Furthermore, it also explores the role of firm size as a moderator in the relationships proposed.

This research employs panel data analysis, using a sample of 4,677 Spanish firms, with data sourced from the PITEC database.

The results of this study show that there is an inverted U-shaped relationship between the functional and the geographical breadth of collaborations and the firms’ radical innovation performance. Moreover, this study finds partial support for the moderating role of firm size, in the sense that small and medium-sized enterprises (SMEs) and large firms vary in their optimal number of diversity of partners.

This research provides a better understanding on how partners’ functional and geographical diversity, along with organizational characteristics such as firm size, affect how firms benefit from collaboration for innovation. This study shows that both SMEs and large firms experience diminishing returns when their collaboration networks become overly diverse in pursuit of radical innovation, due to increased costs. However, in SMEs, the turning point occurs at a later stage, consistent with the idea that small firms need broader functional networks to access complementary and novel resources they usually lack.

This study looked at the potential applications of geographic information systems (GIS) and remote sensing (RS) for inclusive community development and participation, sustainable tourism, and rural community-based natural resource management (CBNRM) in sub-Saharan Africa and other rural areas worldwide.

To evaluate resource management systems for rural tourism and the environment in Africa and abroad. The study makes use of reviews of relevant literature and documents, and while linking applications for sustainable tourism and local community empowerment with CBNRM and GIS, vital content was manually analyzed.

The study shows a potential affinity between agricultural and tourism businesses that GIS in line with the CBNRM conception can strengthen. In many rural and underdeveloped regions of the continent, this highlights the need for a credible and varied tourism strategy to develop and empower the relevant communities.

Most agricultural communities in Africa are located in low-income regions. Such areas are rich in natural wildlife and have popular tourist destinations. A mix of regional community development initiatives can be built using GIS, sustainable tourism, CBNRM, and community-based tourism (CBT).

This study aims to conduct a geographical analysis of the distribution and type of activities developed by social enterprises in rural and urban areas of Ireland.

The study analyses data of more than 4,000 social enterprises against a six-tier rural/urban typology, using descriptive statistics and non-parametric tests to test six hypotheses.

The study shows a geographical rural–urban pattern in the distribution of social enterprises in Ireland, with a positive association between the remoteness of an area and the ratio of social enterprises, and a lack of capital-city effect related to the density of social enterprises. The analysis also shows a statistically significant geographical rural–urban pattern for the types of activities developed by social enterprises. The authors observe a positive association between the remoteness of the areas and the presence of social enterprises operating in the community and local development sector whereas the association is not significant for social enterprises developing welfare services.

The paper shows the potential of using recently developed rural–urban typologies and tools such as geographical information systems for conducting geographical research on social enterprises. The findings also have implications for informing spatially sensitive policymaking on social enterprises.

The merging of a large national data set of social enterprises with geographical tools and data at subregional level contributes to the methodological advancement of the field of social enterprises, providing tools and frameworks for a nuanced and spatially sensitive analysis of these organisations.