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Effective construction safety training has been considered to play a significant role in reducing the incidence of accidents. However, the current safety training methods pay less attention to the relationship between workers' personalized characteristics and their learning needs, which results in workers' low learning participation and poor training effect. The purpose of this paper is to improve the participation and effect of safety training for construction workers with a persona-based approach.

This paper presents a persona-based approach to safety tag generation and training material recommendation. By extracting the demographic characteristics and behavior patterns tags of construction workers, a neural network algorithm is introduced to calculate the learning needs tags of workers, and the collaborative filtering recommendation method is integrated to enrich the innovation of recommendation results. Offline experiments and online experiments are designed to verify the rationality of the proposed method.

The results show that the learning needs of workers are closely related to their background. The proposed method can effectively improve workers' interest in materials and the training effect compared with conventional safety training methods. The research provides a theoretical and practical reference for promoting active safety management and achieving worker-centered safety management.

First, a persona-based approach is introduced to establish a novel framework for solving the problem of personalized construction safety management. Second, an artificial intelligence algorithm is used to automatically extract the learning needs tag values and design a hybrid recommendation method for construction workers' personalized safety training. The collaborative filtering method is integrated to enrich the innovation of recommendation results.

Knowledge is companies’ crucial asset, especially when they are inserted in continuous collaboration and value co-creation. However, problems related to knowledge may occur without proper management, which can compromise the strategic objectives associated with a business collaboration network. Given the presented gap, this study aims to propose and test a business-to-business (B2B) knowledge management (KM) framework focused on value co-creation. Therefore, this study seeks to answer the following guiding questions: what are the main elements that a KM model should present in a context of value co-creation between companies? What are the limitations? What are the advantages and disadvantages? Is there any group that would benefit most from it?

This is an exploratory study grounded on mixed methods, having a qualitative approach (systematic literature review and content analysis) followed by a quantitative approach (exploratory and confirmatory factor analysis), which grounded the proposed framework.

The qualitative approach grounded on the systematic literature review resulting in 38 articles that were submitted to content analysis, which resulted in six record units: active communication between the organization, employees and other stakeholders; documents and organizational knowledge stored; knowledge map; collaborative network; searching tools and database, which provided the KM elements to develop and test the proposed framework by the quantitative approach. The results have shown that the framework may assist in managing knowledge in B2B value co-creation relationships.

As an exploratory study, the chosen research approach used nonprobabilistic for convenience sampling. Therefore, the results may lack generalizability. Thus, researchers are encouraged to use probabilistic sampling techniques to ensure generability. Also, more and better items should be used to upgrade the initial questionnaire, improving it and, by doing so, have a better scale.

Assuming the proposed framework’s effectiveness, company managers can use it to drive knowledge within the network of interested parties to promote cooperative products and services. In addition, due to the theoretical framework’s broad vision, it can serve as a strategic aid to leverage innovation, productivity and competitive advantage. This study also provides an initial instrument that assists in understanding KM elements, which may assist in value co-creation.

It was learned that the elements, tools, concepts and KM preconized solutions can assist in value co-creation. Considering that value assists business performance, and value co-creation is one way to enhance it, furthermore, by knowledge sharing, the value co-creation may occur in the B2B ecosystem. Also, it is the first theoretical KM framework proposed to assist companies to understand better ways that could get advantages on structuring knowledge, meaning mapping it, sharing it through a system that can retain what is needed and release it to the ones that need and have the defined access to receive it.

This article aims to help ensure high-quality subject access to Swedish lesbian, gay, bisexual, transgender, queer and intersexual (LGBTQI) fiction, and aims to identify challenges that librarians consider important to address, on behalf of themselves and end users.

A web-based questionnaire comprising 35 closed and open questions, 22 of which were required, was sent via online channels in January 2022. By the survey closing date, 20 March 2022, 82 responses had been received. The study was intended to complement an earlier study targeting end users.

Both this study of librarians and the previous study of end users have painted a dismal image of online search services when it comes to searching for LGBTQI fiction. The need to consult different channels (e.g. social media, library catalogues and friends), the inability to search more specifically than for the broad LGBTQI category and suboptimal search interfaces were among the commonly reported issues. The results of these studies are used to inform the development of a dedicated Swedish LGBTQI fiction database with an online search interface.

The subject searching of fiction via online services is usually limited to genre with facets for time and place, while users are often seeking characteristics such as pacing, characterization, storyline, frame/setting, tone and language/style. LGBTQI fiction is even more challenging to search because indexing practices are not really being standardized or disseminated worldwide. This study helps address this important gap, in both research and practical applications.

The explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.

We propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.

Expanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.

This study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.

This study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.

The purpose of this research study is to explore simple collaborative technique for teaching mathematics and thus improving the problem solving skills of the students. Better pedagogic activities are required to motivate the students to perceive mathematics as a subject that stimulates problem-solving skills required for engineers.

This paper presents a research study on the application of technology-supported collaborative learning in improving the problem-solving skills of first-year engineering students in a mathematics course. The experiment was conducted in a mathematics course “Engineering Calculus” with 286 first-year engineering students in two groups: experimental group (N = 60) and controlled group (N = 226). The academic performance of the students was measured and analyzed with statistical techniques.

From the results obtained, it was found that the academic performance of the experimental group was better than the controlled group. Also, interest shown by the students in the topic that dealt with collaborative learning was more than in other topics taught using conventional teaching methods.

The teachers are required to find effective pedagogical activities to improve the problem-solving skills in mathematics. The research work proposes a collaborative method in mathematics for attaining higher cognitive level in an entry level engineering course. The limitation of this study lies in group formation techniques and the grading policy which deals only with individual assessment scores.

Practice of collaborative learning is made easy with simple technology. A clear strategy for the conduct of collaborative learning sessions has been presented. The solutions recommended are practically feasible and does not require any special infrastructure or specific training.

Using technology in mathematics teaching may not be very easy for all teachers. Especially, for an undergraduate engineering fresher, mathematics may not be a very easy task. This work shall bridge the gap with simple technology-assisted collaborative learning. The teachers need not spend too much time and effort in learning technology for mathematics teaching. The effect of this learning is significant in terms of the performance and satisfaction evaluation.

This work presents a systematic implementation of collaborative learning that shall result in improved problem-solving skills and engaging learning sessions. The statistical analysis methods and the visualization of obtained results shall help in understanding the implications of the presented work. Practice of collaborative learning is made easy with simple technology. The solutions recommended are practically feasible and does not require any special infrastructure or specific training.

The interpretation of any emerging form or period in art history was never a trivial task. However, in the case of digital art, technology, becoming an integral part, multiplied the complexity of describing, systematizing and evaluating it. This article investigates the most common metadata standards for the documentation of art as a broad category and suggests possible next steps toward an extended metadata standard for digital art.

Describing several techno-cultural phenomena formed in the last decade, manifesting the extendibility of digital art (its ability to be easily extended across multiple modalities), the article, at first, points to the long overdue need to re-evaluate the standards around it. Then it suggests a deeper analysis through a comparative study. In the scope of the study three artworks, The Arnolfini Portrait (Jan van Eyck), an iconic example of the early Renaissance, The World's First Collaborative Sentence (Douglas Davis), a classic example of early Internet art and Fake It Till You Make It (Maya Man), a prominent example of the blockchain art, are examined following the structure of the VRA Core 4.0 standard.

The comparative study demonstrates that digital art is more multi-semantic than traditional physical art, and requires new taxonomies as well as approaches for data acquisition.

Acknowledging that digital art simply has not yet evolved to the stage of being systematically collected by cultural institutions for documentation, curation and preservation, but otherwise, in the past few years, it has been at the front-center of social, economic and technological trends, the article suggests looking for hints on the future-proof extended metadata standard in some of those trends.

The research on social media-based academic communication has made great progress with the development of the mobile Internet era, and while a large number of research results have emerged, clarifying the topology of the knowledge label network (KLN) in this field and showing the development of its knowledge labels and related concepts is one of the issues that must be faced. This study aims to discuss the aforementioned issue.

From a bibliometric perspective, 5,217 research papers in this field from CNKI from 2011 to 2021 are selected, and the title and abstract of each paper are subjected to subword processing and topic model analysis, and the extended labels are obtained by taking the merged set with the original keywords, so as to construct a conceptually expanded KLN. At the same time, appropriate time window slicing is performed to observe the temporal evolution of the network topology. Specifically, the basic network topological parameters and the complex modal structure are analyzed empirically to explore the evolution pattern and inner mechanism of the KLN in this domain. In addition, the ARIMA time series prediction model is used to further predict and compare the changing trend of network structure among different disciplines, so as to compare the differences among different disciplines.

The results show that the degree sequence distribution of the KLN is power-law distributed during the growth process, and it performs better in the mature stage of network development, and the network shows more stable scale-free characteristics. At the same time, the network has the characteristics of “short path and high clustering” throughout the time series, which is a typical small-world network. The KLN consists of a small number of hub nodes occupying the core position of the network, while a large number of label nodes are distributed at the periphery of the network and formed around these hub nodes, and its knowledge expansion pattern has a certain retrospective nature. More knowledge label nodes expand from the center to the periphery and have a gradual and stable trend. In addition, there are certain differences between different disciplines, and the research direction or topic of library and information science (LIS) is more refined and deeper than that of journalism and media and computer science. The LIS discipline has shown better development momentum in this field.

KLN is constructed by using extended labels and empirically analyzed by using network frontier conceptual motifs, which reflects the innovation of the study to a certain extent. In future research, the influence of larger-scale network motifs on the structural features and evolutionary mechanisms of KLNs will be further explored.

Given the critical role of user-generated content (UGC) in e-commerce, exploring various aspects of UGC can aid in understanding user purchase intention and commodity recommendation. Therefore, this study investigates the impact of UGC on purchase decisions and proposes new recommendation models based on sentiment analysis, which are verified in Douban, one of the most popular UGC websites in China.

After verifying the relationship between various factors and product sales, this study proposes two models, collaborative filtering recommendation model based on sentiment (SCF) and hidden factors topics recommendation model based on sentiment (SHFT), by combining traditional collaborative filtering model (CF) and hidden factors topics model (HFT) with sentiment analysis.

The results indicate that sentiment significantly influences purchase intention. Furthermore, the proposed sentiment-based recommendation models outperform traditional CF and HFT in terms of mean absolute error (MAE) and root mean square error (RMSE). Moreover, the two models yield different outcomes for various product categories, providing actionable insights for organizers to implement more precise recommendation strategies.

The findings of this study advocate the incorporation of UGC sentimental factors into websites to heighten recommendation accuracy. Additionally, different recommendation strategies can be employed for different products types.

This study introduces a novel perspective to the recommendation algorithm field. It not only validates the impact of UGC sentiment on purchase intention but also evaluates the proposed models with real-world data. The study provides valuable insights for managerial decision-making aimed at enhancing recommendation systems.

Online news websites provide huge amounts of timely news, bringing the challenge of recommending personalized news articles. Generative adversarial networks (GAN) based on collaborative filtering (CFGAN) can achieve effective recommendation quality. However, CFGAN ignores item contents, which contain more latent preference features than just user ratings. It is important to consider both ratings and item contents in making preference predictions. This study aims to improve news recommendation by proposing a GAN-based news recommendation model considering both ratings (implicit feedback) and the latent features of news content.

The collaborative topic modeling (CTM) can improve user preference prediction by combining matrix factorization (MF) with latent topics of item content derived from latent topic modeling. This study proposes a novel hybrid news recommendation model, Hybrid-CFGAN, which modifies the architecture of the CFGAN model with enhanced preference learning from the CTM. The proposed Hybrid-CFGAN model contains parallel neural networks – original rating-based preference learning and CTM-based preference learning, which consider both ratings and news content with user preferences derived from the CTM model. A tunable parameter is used to adjust the weights of the two preference learnings, while concatenating the preference outputs of the two parallel neural networks.

This study uses the dataset collected from an online news website, NiusNews, to conduct an experimental evaluation. The results show that the proposed Hybrid-CFGAN model can achieve better performance than the state-of-the-art GAN-based recommendation methods. The proposed novel Hybrid-CFGAN model can enhance existing GAN-based recommendation and increase the performance of preference predictions on textual content such as news articles.

As the existing CFGAN model does not consider content information and solely relies on history logs, it may not be effective in recommending news articles. Our proposed Hybrid-CFGAN model modified the architecture of the CFGAN generator by adding a parallel neural network to gain the relevant information from news content and user preferences derived from the CTM model. The novel idea of adjusting the preference learning from two parallel neural networks – original rating-based preference learning and CTM-based preference learning – contributes to improve the recommendation quality of the proposed model by considering both ratings and latent preferences derived from item contents. The proposed novel recommendation model can improve news recommendation, thereby increasing the commercial value of news media platforms.