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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.

This study addresses how algorithmic recommendations and their affordances shape everyday information practices among young people.

Thematic interviews were conducted with 20 Finnish young people aged 15–16 years. The material was analysed using qualitative content analysis, with a focus on everyday information practices involving online platforms.

The key finding of the study is that the current affordances of algorithmic recommendations enable users to engage in more passive practices instead of active search and evaluation practices. Two major themes emerged from the analysis: enabling not searching, inviting high trust, which highlights the how the affordances of algorithmic recommendations enable the delegation of search to a recommender system and, at the same time, invite trust in the system, and constraining finding, discouraging diversity, which focuses on the constraining degree of affordances and breakdowns associated with algorithmic recommendations.

This study contributes new knowledge regarding the ways in which algorithmic recommendations shape the information practices in young people's everyday lives specifically addressing the constraining nature of affordances.

This study aims to explore the application of emojis to mood descriptions of fiction. The three goals are investigating whether Cho et al.'s model (2023) is a sound conceptual framework for implementing emojis and mood categories in information systems, mapping 30 mood categories to 115 face emojis and exploring and visualizing the relationships between mood categories based on emojis mapping.

An online survey was distributed to a US public university to recruit adult fiction readers. In total, 64 participants completed the survey.

The results show that the participants distinguished between the three families of fiction mood categories. The three families model is a promising option to improve mood descriptions for fiction. Through mapping emojis to 30 mood categories, the authors identified the most popular emojis for each category, analyzed the relationships between mood categories and examined participants' consensus on mapping.

This study focuses on applying emojis to fiction reading. Emojis were mapped to mood categories by fiction readers. Emoji mapping contributes to the understanding of the relationships between mood categories. Emojis, as graphic mood descriptors, have the potential to complement textual descriptors and enrich mood metadata for fiction.

The authors’ goal is to investigate variations in the writing style of book reviews published on different social reading platforms and referring to books of different genres, which enables acquiring insights into communication strategies adopted by readers to share their reading experiences.

The authors propose a corpus-based study focused on the analysis of A Good Review, a novel corpus of online book reviews written in Italian, posted on Amazon and Goodreads, and covering six literary fiction genres. The authors rely on stylometric analysis to explore the linguistic properties and lexicon of reviews and the authors conducted automatic classification experiments using multiple approaches and feature configurations to predict either the review's platform or the literary genre.

The analysis of user-generated reviews demonstrates that language is a quite variable dimension across reading platforms, but not as much across book genres. The classification experiments revealed that features modelling the syntactic structure of the sentence are reliable proxies for discerning Amazon and Goodreads reviews, whereas lexical information showed a higher predictive role for automatically discriminating the genre.

The high availability of cultural products makes information services necessary to help users navigate these resources and acquire information from unstructured data. This study contributes to a better understanding of the linguistic characteristics of user-generated book reviews, which can support the development of linguistically-informed recommendation services. Additionally, the authors release a novel corpus of online book reviews meant to support the reproducibility and advancements of the research.

Librarianship’s dominant conception of the freedom to read is governed by a liberal principle of noninterference, wherein free readers are those who face no intentional intervention in their choice of materials. The purpose of this paper is to demonstrate how this account fails to adequately capture systemic threats that impoverish people’s reading lives.

This conceptual paper deploys informal argumentation to expose a flaw in the dominant account of the freedom to read. The author uses a case study of comparative titles or comps, an editorial decision-making and justificatory convention that reproduces racial inequality in Anglophone trade publishing.

Comps present one example of how everyday norms and practices of literary production render people’s reading lives pervasively unfree, even absent some intent to interfere in them. The going account of the freedom to read calls, at best, for a greater diversity of book-commodities from which consumers may choose. However, the comp case suggests that this distributive remedy will be insufficient without relevant changes to the institutional arrangements that condition readers' choices in the first place.

This paper draws together insights from Library and Information Science, political philosophy and print culture studies to illuminate limitations in librarianship’s standard conception of the freedom to read. This reveals the need for an alternative, structural account of that freedom with significant implications for practice.

The purpose of this paper is to provide adaptive access to learning resources in the digital library.

A novel method using ontology-based multi-attribute collaborative filtering is proposed. Digital libraries are those which are fully automated and all resources are in digital form and access to the information available is provided to a remote user as well as a conventional user electronically. To satisfy users' information needs, a humongous amount of newly created information is published electronically in digital libraries. While search applications are improving, it is still difficult for the majority of users to find relevant information. For better service, the framework should also be able to adapt queries to search domains and target learners.

This paper improves the accuracy and efficiency of predicting and recommending personalized learning resources in digital libraries. To facilitate a personalized digital learning environment, the authors propose a novel method using ontology-supported collaborative filtering (CF) recommendation system. The objective is to provide adaptive access to learning resources in the digital library. The proposed model is based on user-based CF which suggests learning resources for students based on their course registration, preferences for topics and digital libraries. Using ontological framework knowledge for semantic similarity and considering multiple attributes apart from learners' preferences for the learning resources improve the accuracy of the proposed model.

The results of this work majorly rely on the developed ontology. More experiments are to be conducted with other domain ontologies.

The proposed approach is integrated into Nucleus, a Learning Management System (https://nucleus.amcspsgtech.in). The results are of interest to learners, academicians, researchers and developers of digital libraries. This work also provides insights into the ontology for e-learning to improve personalized learning environments.

This paper computes learner similarity and learning resources similarity based on ontological knowledge, feedback and ratings on the learning resources. The predictions for the target learner are calculated and top N learning resources are generated by the recommendation engine using CF.

While there has been great attention directed at innovation within organisations in business contexts, there has been less focus on the role of innovation at higher education institutions. A changing and turbulent environment is also placing more pressure on universities and business schools to re-imagine, inspire and design an innovation culture. This empirical case presents the innovation leadership team’s point of view on the effect of innovation leadership when integrating the ISO 56000 series-based innovation management system at the University of Ruse in Bulgaria. Several face-to-face interviews and questionnaires are conducted to uncover gaps in innovation leadership based on a comprehensive literature review and the current state of the innovation management system. These gaps hold potential for improvement of the existing management system which are discussed, and recommendations are formulated in the conclusions section of this chapter. Taking into consideration the specific context in which this case study has evolved can help readers and other innovation leaders adapt the facts, conclusions, and lessons learned for universities and business schools. This chapter presents lessons learned and best practices by the innovation leadership team, as demonstrated in several cases at the University of Ruse ‘Angel Kanchev’, Bulgaria.

For a better understanding of the preferences and differences of young consumers in emerging wine markets, this study aims to propose a clustering method to segment the super-new generation wine consumers based on their sensitivity to wine brand, origin and price and then conduct user profiles for segmented consumer groups from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences.

We first proposed a consumer clustering perspective based on their sensitivity to wine brand, origin and price and then conducted an adaptive density peak and label propagation layer-by-layer (ADPLP) clustering algorithm to segment consumers, which improved the issues of wrong centers' selection and inaccurate classification of remaining sample points for traditional DPC (DPeak clustering algorithm). Then, we built a consumer profile system from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences for segmented consumer groups.

In this study, 10 typical public datasets and 6 basic test algorithms are used to evaluate the proposed method, and the results showed that the ADPLP algorithm was optimal or suboptimal on 10 datasets with accuracy above 0.78. The average improvement in accuracy over the base DPC algorithm is 0.184. As an outcome of the wine consumer profiles, sensitive consumers prefer wines with medium prices of 100–400 CNY and more personalized brands and origins, while casual consumers are fond of popular brands, popular origins and low prices within 50 CNY. The wine sensory attributes preferred by super-new generation consumers are red, semi-dry, semi-sweet, still, fresh tasting, fruity, floral and low acid.

Young Chinese consumers are the main driver of wine consumption in the future. This paper provides a tool for decision-makers and marketers to identify the preferences of young consumers quickly which is meaningful and helpful for wine marketing.

In this study, the ADPLP algorithm was introduced for the first time. Subsequently, the user profile label system was constructed for segmented consumers to highlight their characteristics and demand partiality from three aspects: demographic characteristics, consumers' eating habits and consumers' preferences for wine attributes. Moreover, the ADPLP algorithm can be considered for user profiles on other alcoholic products.

This article draws upon our perspective as academic-practitioners working in the fields of food insecurity, food systems, and inequality to comment, in the early stages of the pandemic and associated lockdown, on the empirical and ethical implications of COVID-19 for socio-economic inequalities in access to food in the UK. The COVID-19 pandemic has sharpened the profound insecurity of large segments of the UK population, an insecurity itself the product of a decade of “austerity” policies. Increased unemployment, reduced hours, and enforced self-isolation for multiple vulnerable groups is likely to lead to an increase in UK food insecurity, exacerbating diet-related health inequalities. The social and economic crisis associated with the pandemic has exposed the fragility of the system of food charity which, at present, is a key response to growing poverty. A vulnerable food system, with just-in-time supply chains, has been challenged by stockpiling. Resultant food supply issues at food banks, alongside rapidly increasing demand and reduced volunteer numbers, has undermined many food charities, especially independent food banks. In the light of this analysis, we make a series of recommendations. We call for an immediate end to the five week wait for Universal Credit and cash grants for low income households. We ask central and local government to recognise that many food aid providers are already at capacity and unable to adopt additional responsibilities. The government's – significant – response to the economic crisis associated with COVID-19 has underscored a key principle: it is the government's responsibility to protect population health, to guarantee household incomes, and to safeguard the economy. Millions of households were in poverty before the pandemic, and millions more will be so unless the government continues to protect household incomes through policy change.

This chapter will provide an analysis of current educational conditions of Black males within our K-12 school system and the ongoing instructional implications for school leaders working to address literacy leadership and practice. It will provide insight and motivation for school leaders as they navigate political, social, and policy systems that surround the current educational climate and instructional expectations. Readers will engage in a reflective, collaborative, and constructive learning process related to how successful school leaders enhance teaching and learning on their school campuses for Black males and what behaviors are critical to supporting teachers and students through the process. Specifically at the elementary level where foundational decoding, fluency, and comprehension skills are developed and cultivated for early learners we must be intentional with all aspects of the learning process and our instructional pedagogy. This chapter will work to expand the body of knowledge surrounding Black males as we commit to deconstructing existing and reconstructing inclusive, equitable, and just learning environments. Implications for research, policy and practice, including recommendations to support ongoing formal and informal professional learning opportunities for educators to openly discuss their understanding of Black males, challenges they face, and strategies that they have found to be successful will be disclosed.