Filter Bubbles in Recommender Systems: Fact or Fallacy -- A Systematic Review

A filter bubble refers to the phenomenon where Internet customization effectively isolates individuals from diverse opinions or materials, resulting in their exposure to only a select set of content. This can lead to the reinforcement of existing attitudes, beliefs, or conditions. In this study, our primary focus is to investigate the impact of filter bubbles in recommender systems. This pioneering research aims to uncover the reasons behind this problem, explore potential solutions, and propose an integrated tool to help users avoid filter bubbles in recommender systems. To achieve this objective, we conduct a systematic literature review on the topic of filter bubbles in recommender systems. The reviewed articles are carefully analyzed and classified, providing valuable insights that inform the development of an integrated approach. Notably, our review reveals evidence of filter bubbles in recommendation systems, highlighting several biases that contribute to their existence. Moreover, we propose mechanisms to mitigate the impact of filter bubbles and demonstrate that incorporating diversity into recommendations can potentially help alleviate this issue. The findings of this timely review will serve as a benchmark for researchers working in interdisciplinary fields such as privacy, artificial intelligence ethics, and recommendation systems. Furthermore, it will open new avenues for future research in related domains, prompting further exploration and advancement in this critical area.Comment: 21 pages, 10 figures and 5 table

The scholarly footprint of ChatGPT: a bibliometric analysis of the early outbreak phase

This paper presents a comprehensive analysis of the scholarly footprint of ChatGPT, an AI language model, using bibliometric and scientometric methods. The study zooms in on the early outbreak phase from when ChatGPT was launched in November 2022 to early June 2023. It aims to understand the evolution of research output, citation patterns, collaborative networks, application domains, and future research directions related to ChatGPT. By retrieving data from the Scopus database, 533 relevant articles were identified for analysis. The findings reveal the prominent publication venues, influential authors, and countries contributing to ChatGPT research. Collaborative networks among researchers and institutions are visualized, highlighting patterns of co-authorship. The application domains of ChatGPT, such as customer support and content generation, are examined. Moreover, the study identifies emerging keywords and potential research areas for future exploration. The methodology employed includes data extraction, bibliometric analysis using various indicators, and visualization techniques such as Sankey diagrams. The analysis provides valuable insights into ChatGPT's early footprint in academia and offers researchers guidance for further advancements. This study stimulates discussions, collaborations, and innovations to enhance ChatGPT's capabilities and impact across domains.</p

Multi-criteria decision making-based waste management: A bibliometric analysis

Waste management is a complex research domain. While the domain is challenging in terms of content, it is also a diverse and cross-disciplinary research subject. One of its important components includes efficient decision-making at various levels and stages. Therefore, Multi-criteria decision-making (MCDM) techniques have found decent applications in this domain. The field of MCDM techniques-based waste management has been examined using bibliometric analysis in this paper in order to report a systematic overview of the trends and advancements in this area of study. The Scopus database provided 216 publications on the aforementioned subject written between 1992 and 2022. The 216 articles include 56 countries, 158 institutions, and 160 authors. Furthermore, Asian countries, including India, Iran, and China, dominate this field of study. The geographical disparity in the output of publications is visible. Journal of cleaner production, Waste Management and Waste Management and Research are the major journals publishing on MCDM techniques-based waste management research. Given that majority of the articles include multiple authors, it can be said that there is a lot of collaborative research in this area. Overall, the current study provides a thorough analysis of the development in the domain of waste management using MCDM techniques. The trend suggests that it will continue to be a focus of research for academicians, environmentalists and policymakers in the future

Artificial intelligence-driven approach to identify and recommend the winner in a tied event in sports surveillance

The proliferation of fractal artificial intelligence (AI)-based decision-making has propelled advances in intelligent computing techniques. Fractal AI-driven decision-making approaches are used to solve a variety of real-world complex problems, especially in uncertain sports surveillance situations. To this end, we present a framework for deciding the winner in a tied sporting event. As a case study, a tied cricket match was investigated, and the issue was addressed with a systematic state-of-the-art approach by considering the team strength in terms of the player score, team score at different intervals, and total team scores (TTSs). The TTSs of teams were compared to recommend the winner. We believe that the proposed idea will help to identify the winner in a tied match, supporting intelligent surveillance systems. In addition, this approach can potentially address many existing issues and future challenges regarding critical decision-making processes in sports. Furthermore, we posit that this work will open new avenues for researchers in fractal AI

Feature extraction and analysis of online reviews for the recommendation of books using opinion mining technique

The customer's review plays an important role in deciding the purchasing behaviour for online shopping as a customer prefers to get the opinion of other customers by observing their opinion through online products’ reviews, blogs and social networking sites, etc. The customer's reviews reflect the customer's sentiments and have a substantial significance for the products being sold online including electronic gadgets, movies, house hold appliances and books. Hence, extracting the exact features of the products by analyzing the text of reviews requires a lot of efforts and human intelligence. In this paper we intend to analyze the online reviews available for books and extract book-features from the reviews using human intelligence. We have proposed a technique to categorize the features of books from the reviews of the customers. The extracted features may help in deciding the books to be recommended for readers. The ultimate goal of the work is to fulfil the requirement of the user and provide them their desired books. Thus, we have evaluated our categorization method by users themselves, and surveyed qualified persons for the concerned books. The survey results show high precision of the features categorized which clearly indicates that proposed method is very useful and appealing. The proposed technique may help in recommending the best books for concerned people and may also be generalized to recommend any product to the users

How trustworthy is ChatGPT? The case of bibliometric analyses

AbstractThe introduction of the AI-powered chatbot ChatGPT by OpenAI has sparked much interest and debate among academic researchers. Commentators from different scientific disciplines have raised many concerns and issues, especially related to the ethics of using these tools in scientific writing and publications. In addition, there has been discussions about whether ChatGPT is trustworthy, effective, and useful in increasing researchers’ productivity. Therefore, in this paper, we evaluate ChatGPT’s performance on tasks related to bibliometric analysis, by comparing the output provided by the chatbot with a recently conducted bibliometric study on the same topic. The findings show that there are large discrepancies and ChatGPT’s trustworthiness is low in this particular area. Therefore, researchers should exercise caution when using ChatGPT as a tool in bibliometric studies

ChatGPT: A brief narrative review

AbstractIn this study, we present a brief narrative review focused on ChatGPT, a state-of-the-art conversational agent developed using OpenAI’s Generative Pretrained Transformer (GPT) framework. Distinctive for its ability to generate text of high quality in real-time, ChatGPT has emerged as a leader among artificial intelligence chatbots, garnering interest from both commercial and scholarly circles. Our review explores the technological underpinnings of ChatGPT, examines its inherent features that support its performance, and analyzes existing research on its applications and impacts across several domains. Through this assessment, we delineate ChatGPT’s strengths and limitations, offering informed recommendations for future investigations in this burgeoning research field

Sustainable Behavior with Respect to Managing E-Wastes: Factors Influencing E-Waste Management among Young Consumers

With the proliferation of technological tools and the advancement in electronic devices and accessories, consumers across the world are changing and upgrading their electronic devices at an alarming rate. However, these developments have raised concerns related to electronic waste (E-waste). E-wastes contain toxic substances which may have a negative impact on both humans and the environment. This issue needs to be addressed by the research community, i.e., what would be the best way to get rid of existing devices? It is clear that countries need to work towards a more sustainable consumption pattern and consumers need to change their behaviour. The present study focuses on sustainable behaviour of consumers in terms of e-waste management. In this context, the study attempts to explore the factors influencing e-waste management among young consumers. In the present study, the Theory of Planned Behavior is extended by including the additional factors Government Policy, Environmental Concern, Financial Benefits and Awareness. A researcher-controlled sampling was employed to collect data from 524 respondents. Partial least square structural equation modelling (PLS-SEM) was used to validate the questionnaire constructs and confirm the relationships among the variables. The findings of the study suggest a significant role for government policy, financial benefits, environmental concerns, attitude, subjective norms, and perceived behavioural control in determining young consumers&rsquo; behavioural intentions toward the management of e-waste. The study findings have implications for both researchers and marketing practitioners