Daily Archives: October 12, 2026

Authors: Aayesha Rahman

Abstract: This paper aims to discuss the debate across various social media platforms on the impact of “BookTok,” which is a side of TikTok dedicated to readers sharing their opinions, recommendations, reviews, and experiences with books. Many individuals and influencers have made a claim that during and after COVID-19, there has been a compromise on the quality of books to meet the demands of consumers, which has led to books being commodified. Critics find it problematic as new releases flood the market, and the characteristics such as longer writing period and meticulous editing process, are replaced by books that are written, edited, and published within the span of a year. The question that any reader should ask themselves is the outcome they get out of reading by this shift in literary standards.

DOI: https://doi.org/10.5281/zenodo.23338534

Published by: vikaspatanker

Authors: Mohammad Ali, Jin Xie, Wu Wenhuan

Abstract: The machine behind e-commerce recommendation systems is often based on Collaborative Filtering (CF) algorithms which are highly dependent on numerical star ratings ignoring the fact that user review text is a rich source of semantic information. This constraint increases the issue of data sparsity and lowers the quality of the recommendations. We suggest SA-BTM-CF, a Sentiment-Aware Collaborative Filtering system, which encompasses the Biterm Topic Model (BTM) with a conventional CF. The suggested methodology is a latent topic extractor of review literature and a fine-grained sentiment score generator of latent topic models which are employed to build an enriched user-item rating matrices. Personalized product suggestions are then created by computing user similarity on these sentiment-augmented ratings. Large scale shampoo review data on the JD.com site is experimented with and experimentally validates that SA-BTM-CF can be used to effectively reduce sparsity in data, and it can be used to obtain better recommendations than current baseline algorithms.

DOI: https://doi.org/10.5281/zenodo.23310790

Published by: vikaspatanker

Authors: Thoomula Kavya Sri

Abstract: The fusion of artificial intelligence (AI), data science, and cloud computing technologies is transforming the nature of the digital economy from disconnected technological silos to interdependent ecosystems that complement each other. This paper explores current trends within these interrelated fields such as world models, agentic AI systems, cloud-native AI integration, and industrialization of data science. Mixed methods of research including the systematic literature review combined with the quantitative analysis of industry data is suggested. Convergence patterns analysis shows that cloud platforms transform from mere infrastructure into smart services ecosystems, and data science changes from disconnected modeling into engineering practice. Comparative analysis shows that companies embracing integrated approach to cloud-AI-data management enjoy considerably lower training costs and reduced deployment timeframes. The conclusion is made about the challenges related to governance, skill development, and infrastructure sustainability that need addressing.

DOI: https://doi.org/10.5281/zenodo.23310822

Published by: vikaspatanker