In the current digital era, the proliferation of online content has overwhelmed users with vast amounts of information, necessitating effective filtering mechanisms. Recommender systems have become indispensable in addressing this challenge, tailoring content to individual preferences and significantly enhancing user experience. This paper delves into the latest advancements in recommender systems, analyzing 115 research papers and 10 articles, and dissecting their application across various domains such as e-commerce, entertainment, and social media. We categorize these systems into content-based, collaborative, and hybrid approaches, scrutinizing their methodologies and performance. Despite their transformative impact, recommender systems grapple with persistent issues like scalability, cold-start problems, and data sparsity. Our comprehensive review not only maps the current landscape of recommender system research but also identifies critical gaps and future directions. By offering a detailed analysis of datasets, simulation platforms, and evaluation metrics, we provide a robust foundation for developing next-generation recommender systems poised to deliver more accurate, efficient, and personalized user experiences, inspiring innovative solutions to drive forward the evolution of recommender technology.
Category: Type: Research
In this category:
1. Peer-Reviewed:
Research Papers
Chapters
Conference Proceedings
2. Pre-Prints. Pre-Prints are standard in some fields. They are not always peer reviewed.
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From Crisis Management to the crisis of management: Accountability and Liberal Democracies in the Outbreak of the Covid-19 Pandemic
The outbreak of the COVID-19 pandemic shocked societies around the world. In their efforts to tailor their responses to the crisis to their own conditions for survival, from the outset governments tended to resort to arguments that limited accountability before their populations. Liberal democracies were no exception to this approach. In this context, their leaders used the metaphor of war to describe their position as guarantors of the population’s survival in the face of the new threat. Caught between uncertainty and the need to predict the nature and evolution of the invisible enemy, their responses called into question the political, professional and personal responsibility of leaders. This article offers a reflection on the level of responsibility of governments in liberal democracies in managing the pandemic. During the crisis, decision-makers tended to be driven by the narratives that were most beneficial to them in order to escape their responsibilities, thereby underpinning their short-term political needs through the use of bellicose metaphors, the blame game, competition with other countries, and the dispersion of sources in the decision-making process. This reality now calls for reflection by social actors, including experts, intellectuals and the media, to transcend the prevailing rhetoric in management of the pandemic and the “new normal” that followed, so that the dynamics of constant alterations in the rules of the game and responsibilities can give way, in the future, to a scenario with less arbitrariness and more accountability.
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The Climate Imperative: How AI Can Transform Africa’s Future
Africa contributes minimally to global greenhouse gas emissions but bears a disproportionate burden of climate change impacts. This article explores how artificial intelligence (AI) can bolster conservation and sustainability efforts across the continent. While challenges such as technological import reliance and digital divides persist, AI offers transformative potential by enhancing early prediction, disaster preparedness, and environmental management. Examples like Rwanda’s Wastezon, Ghana’s Okuafo Foundation, and Kenya’s Kuzi illustrate successful AI-driven initiatives. The article proposes adapting a public health prevention model-primary, secondary, and tertiary prevention-to structure AI-based environmental interventions. This approach would enable early detection of climate risks, timely mitigation efforts, and rehabilitation of damaged ecosystems. The authors also caution about AI’s environmental costs, including energy-intensive operations and resource extraction, advocating for ethical and Africa-centered AI solutions. Overall, the article argues that innovative, community-driven, and preventive uses of AI are essential for building climate resilience in Africa.
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GenAI and Religion: Creation, Agency, and Meaning
This paper explores the parallels between Generative Artificial Intelligence (GenAI) and religious systems in three domains: creation, agency, and meaning-making. Both offer frameworks for human engagement but differ in intent, autonomy, and moral accountability. Despite these differences, GenAI and religion share roles as creators, influencers, and meaning facilitators. We address and counter rebuttals to these parallels, highlighting GenAI’s co-constructed outputs and its impact on modern meaning-making. The paper concludes with the societal implications of these parallels in shaping future thought and action.
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Generative AI through the Lens of Institutional Theory
This study examines the adoption of Generative AI (GenAI) systems through the lens of Institutional Theory. Using a mixed-methods approach, we analyze how coercive, normative, and mimetic pressures influence GenAI integration in organizations. Key findings reveal:(1) regulatory frameworks significantly shape GenAI adoption strategies, with variations across industries and regions;(2) organizations balance conformity to institutional norms with innovation, often through strategic decoupling;(3) GenAI’s unique capabilities challenge traditional institutional pressures, necessitating new governance models; and (4) early GenAI adopters emerge as new sources of mimetic pressure, accelerating industry-wide adoption. We propose a novel framework capturing the interplay between GenAI characteristics and institutional dynamics, contributing to both Institutional Theory and AI adoption literature.
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Path to Personalization: A Systematic Review of GenAI in Engineering Education
This systematic review paper provides a comprehensive synthesis across 162 articles on Generative Artificial Intelligence (GenAI) in engineering education (EE), making two specific contributions to advance research in the space. First, we develop a taxonomy that categorizes the current research landscape, identifying key areas such as Coding or Writing Assistance, Design Methodology, and Personalization. Second, we highlight significant gaps and opportunities, such as lack of customer-centricity and need for increased transparency in future research, paving the way for increased personalization in GenAI-augmented engineering education. There are indications of widening lines of enquiry, for example into human-AI collaborations and multidisciplinary learning. We conclude that there are opportunities to enrich engineering epistemology and
competencies with the use of GenAI tools for educators and students, as well as a need for further research into best and novel practices. Our discussion serves as a roadmap for researchers and educators, guiding the development of GenAI applications that will continue to transform the engineering education landscape, in classrooms and the workforce. -
Toward a trustworthy and inclusive data governance policy for the use of artificial intelligence in Africa
This article proposes five ideas that the design of data governance policies for the trustworthy use of artificial intelligence (AI) in Africa should consider. The first is for African states to assess their domestic strategic priorities, strengths, and weaknesses. The second is a human-centric approach to data governance, which involves data processing practices that protect the security of personal data and the privacy of data subjects; ensure that personal data are processed in a fair, lawful, and accountable manner; minimize the harmful effect of personal data misuse or abuse on data subjects and other victims; and promote a beneficial, trusted use of personal data. The third is for the data policy to be in alignment with supranational rights-respecting AI standards like the African Charter on Human and Peoples Rights, the AU Convention on Cybersecurity, and Personal Data Protection. The fourth is for states to be critical about the extent to which AI systems can be relied on in certain public sectors or departments. The fifth and final proposition is for the need to prioritize the use of representative and interoperable data and ensure a transparent procurement process for AI systems from abroad where no local options exist.
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Reconciling methodological paradigms: Employing large language models as novice qualitative research assistants in talent management research
Qualitative data collection and analysis approaches, such as those employing interviews and focus groups, provide rich insights into customer attitudes, sentiment, and behavior. However, manually analyzing qualitative data requires extensive time and effort to identify relevant topics and thematic insights. This study proposes a novel approach to address this challenge by leveraging Retrieval Augmented Generation (RAG) based Large Language Models (LLMs) for analyzing interview transcripts. The novelty of this work lies in strategizing the research inquiry as one that is augmented by an LLM that serves as a novice research assistant. This research explores the mental model of LLMs to serve as novice qualitative research assistants for researchers in the talent management space. A RAG-based LLM approach is extended to enable topic modeling of semi-structured interview data, showcasing the versatility of these models beyond their traditional use in information retrieval and search. Our findings demonstrate that the LLM-augmented RAG approach can successfully extract topics of interest, with significant coverage compared to manually generated topics from the same dataset. This establishes the viability of employing LLMs as novice qualitative research assistants. Additionally, the study recommends that researchers leveraging such models lean heavily on quality criteria used in traditional qualitative research to ensure rigor and trustworthiness of their approach. Finally, the paper presents key recommendations for industry practitioners seeking to reconcile the use of LLMs with established qualitative research paradigms, providing a roadmap for the effective integration of these powerful, albeit novice, AI tools in the analysis of qualitative datasets within talent
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Digitization and Political Participation in the MENA Region: Egypt, Kuwait, and Tunisia
The article highlights the link between digitization and political participation in three Middle Eastern countries: Egypt, Kuwait, and Tunisia. The role of the Internet and social media in political engagement is thoroughly discussed from a historical-comparative perspective. Using micro and macro level data, the study analyzes the usage of new online technologies and online political participation.
The findings provide valuable insights for understanding the intricate nature of online political participation and the paradox between digital engagement and traditional political involvement. Despite the expansion of digital media, traditional political interest and participation has decreased. Egypt and Kuwait demonstrate advanced stages of digitization with widespread Internet access, while Tunisia’s progress is varied. -
(Multi-disciplinary) Teamwork makes the (real) dream work: Pragmatic recommendations from industry for engineering classrooms
Many students choose to major in engineering to join the community of professional engineers and gain exposure to the field through their college experience. However, research suggests that engineering graduates may not be adequately prepared for the workplace due to the complexities of engineering work. Engineering work involves complexity, ambiguity, and contradictions, and developing innovation skills requires analyzing real-world problems that are often ill-defined and multifaceted. Therefore, it is essential for engineering students to have opportunities to work in multi-disciplinary teams to develop their skills in problem-solving and innovation. This emphasis on the need for exposure to multi-disciplinary problem solving holds true not only for undergraduate engineers in training, but also for graduate students focused on engineering education.
This paper draws from experiences of a multi-disciplinary team (including engineers, scientists, UX researchers, Industrial-Organization (I-O) psychologists, economists, and program and product managers) studying talent management in the tech industry, to present lessons learned from leading with science to understand, inform, and improve employee experiences at a large private technology company. Our paper exemplifies how projects in industry leverage multi-disciplinary expertise and presents recommendations for new graduates and engineering professionals. Ultimately, this paper affords an opportunity for educators to expand on examples of how multiple disciplines come together to study engineers in the workforce.
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Bridging the Gap: Exploring Semiconductors Exposure and Motivation among Multidisciplinary Engineering Students
Several educational initiatives are currently underway to address workforce challenges in the semiconductors industry. Assessing students’ exposure to and motivation for semiconductors-related topics is an essential initial step toward recognizing areas where primary efforts should be concentrated. The primary objective of this study is to assess students’ awareness and motivation concerning semiconductors in the context of a multidisciplinary introduction to electrical engineering course. Through quantitative analysis and the administration of an existing validated survey instrument, we aim to explore students’ exposure to semiconductors-related topics and potential correlations between awareness, motivation, and demographic variables, including gender and class standing. The instrument was administered to a cohort of 255 students enrolled in a multidisciplinary course covering the fundamentals of electrical engineering. Preliminary data indicates that only 9% of the students in this cohort haven taken a class about semiconductors and only 3% have some interest in pursuing a career in the semiconductors field. The results of this analysis hold several significant implications for engineering education and the semiconductor industry. Firstly, the limited exposure to and interest in semiconductors among engineering students suggest the need for curriculum alignment with the demands of the semiconductor industry and interdisciplinary education. By doing so, we empower students from diverse disciplines to contribute to technological advancements, innovation, and problem-solving fostering a more inclusive, diverse, and well-rounded workforce within the semiconductor sector.
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A Review of AI-Enhanced Personalized Learning Systems: Implications for the Learning Sciences
This research focuses on recent studies of AI-Enhanced Personalized Learning, organized into three main sections: understanding key aspects, investigating practical methodologies, and elucidating motivations for AI integration into personalized learning to provide insights for future research in learning science. The methodology involves a rapid literature review, emphasizing eligibility criteria and a precise study selection process. The conclusion underscores the importance of seamlessly integrating AI analytics with humancentric approaches in personalized learning, enriching data, and training algorithms for efficiency, alongside emphasizing the role of human oversight.