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Journal articles on the topic 'Algorithmic Content Recommendation'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Chen, Zhiling, and Chen Shi. "Analysis of Algorithm Recommendation Mechanism of TikTok." International Journal of Education and Humanities 4, no. 1 (2022): 12–14. http://dx.doi.org/10.54097/ijeh.v4i1.1152.

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Algorithmic recommendation technology is used in all walks of life, among which the application in the news media industry has achieved great development. As a major mainstream mode of content distribution, algorithmic recommendation is well applied to the TikTok, enabling it to accurately and efficiently push video content that users are interested in. As algorithmic recommendation gradually becomes an indispensable part of content operation, analyzing the algorithmic recommendation mechanism will help the platform to better attract and serve users, so that users can get a better experience a

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2

Weng, Feiyang. "The Consumption of Weight-loss Content by University Students Exhibiting High Engagement on Xiaohongshu." Advances in Economics, Management and Political Sciences 196, no. 1 (2025): 197–205. https://doi.org/10.54254/2754-1169/2025.bj24918.

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The phenomenon of information cocoons has now emerged as a hot issue across society. Some researchers have found that algorithmic recommendation has a significant impact on the formation of the information cocoon. However, there is still a lack of a unified explanation for the influence of gender differences on it. Therefore, this study aims to examine the relationship between information cocoons and gender differences by surveying university students with high engagement on Xiaohongshu, taking algorithmic recommendation as the mediating variable, to study the relationship between the informat

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Zhou, Ren. "Understanding the Impact of TikTok's Recommendation Algorithm on User Engagement." International Journal of Computer Science and Information Technology 3, no. 2 (2024): 201–8. http://dx.doi.org/10.62051/ijcsit.v3n2.24.

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The study investigates the impact of TikTok’s recommendation algorithms on content discovery and user engagement, utilizing a mixed-methods approach that integrates quantitative data analysis and qualitative interviews. The quantitative analysis involved examining a dataset of user interactions over six months, revealing that key features such as like ratios, trending hashtags, and video length significantly influence recommendation likelihood. Qualitative interviews with content creators and users provided insights into the perceived transparency and effectiveness of these recommendations. Ou

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Xiao, Caixing. "Research on Copyright Infringement Issues in Short Video Platforms in the Context of Algorithmic Recommendation." Advances in Social Behavior Research 13, no. 1 (2024): 40–43. https://doi.org/10.54254/2753-7102/2024.18248.

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Short video platforms face increasingly severe copyright infringement issues in the context of algorithmic recommendations. With the widespread application of algorithmic recommendation technology, platforms have gradually evolved from passive neutral intermediaries to active content managers, thereby intensifying the complexity of liability for infringements. Algorithmic recommendations are based on user behavior data and prioritize content with high engagement, often including unauthorized works, which leads to the spread of infringing content. This pseudo-neutrality strengthens the platform

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Kim, Sungho, Sunyong Lee, Debabrata Biswas, MD Shahnawaj, Niaz Mahmood Kyoom, and Parvati Bhardwaj. "PERSONALIZATION AND RECOMMENDATION SYSTEMS: LEVERAGING MACHINE LEARNING ALGORITHMS TO OFFER PERSONALIZED PRODUCT RECOMMENDATIONS AND CONTENT TO CUSTOMERS BASED ON THEIR BEHAVIOR, PREFERENCES AND PURCHASING HISTORY." International Journal of Grid Computing & Applications 16, no. 2 (2025): 1–4. https://doi.org/10.5121/ijgca.2025.16201.

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Personalization and recommendation systems have become a cornerstone of modern digital experiences, providing tailored content to users and enhancing engagement across various industries. The integration of artificial intelligence (AI) and machine learning (ML) in recommendation systems has revolutionized how businesses interact with consumers by analyzing vast amounts of user data to generate highly relevant content suggestions. This paper explores the critical components of recommendation systems, focusing on their architecture, algorithmic implementation, management strategies, and the chal

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Chen, Yi, and Jueru Huang. "Effective Content Recommendation in New Media: Leveraging Algorithmic Approaches." IEEE Access 12 (2024): 90561–70. http://dx.doi.org/10.1109/access.2024.3421566.

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Urgellés-Molina, Alicia, and Mónica Herrero. "Personalization of Content in Video-on-Demand Services: Insights from Satisfaction over Social Media Algorithms." ComHumanitas: revista científica de comunicación 15, no. 2 (2024): 175–87. https://doi.org/10.31207/rch.v15i2.456.

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The rapid evolution of algorithmic personalization has reshaped user experiences on social media and video-on-demand (VOD) platforms, tailoring content recommendations to individual preferences. This paper explores the intersection of user satisfaction, algorithmic responsiveness, and consumption patterns, emphasizing the influence of AI-driven recommendations. It highlights how perceptions of algorithmic customization impact user behavior, fostering both engagement and concerns over echo chambers and privacy. By examining the shift from traditional linear broadcasting to non-linear VOD consum

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Kulvi, Fizza, Sara Bannerman, Faiza Hirji, et al. "Discoverability and Algorithmic Recommendations in Video Streaming Platforms: Exploring Algorithmic Gender and Race Bias as a Canadian Broadcast Policy Concern." Canadian Journal of Communication 48, no. 4 (2023): 632–62. http://dx.doi.org/10.3138/cjc-2022-0054.

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Background: In 2023, the Canadian government passed legislation empowering the Canadian Radio-television and Telecommunications Commission (CRTC) to require streaming platforms to ensure the “discoverability” of Canadian content. These debates about discoverability provisions primarily focused on the promotion of Canadian content, with little emphasis on gender and racial equity. Analysis: Through interviews with stakeholders in the Canadian screen industry, we explore views on recommendation systems and questions of gender and race bias in streaming recommendations. Conclusion and implication

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Dai, Jiarun, Naila Hajiyeva, Sehba Wani, and Kayla Booth. "Taming TikTok: how BIPOC individuals perceive and interact with algorithmically generated content." Information Research an international electronic journal 30, iConf (2025): 1084–94. https://doi.org/10.47989/ir30iconf47089.

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Introduction. TikTok’s recommendation algorithm plays a crucial role in shaping user experiences, raising concerns about algorithmic bias, content suppression, and misinformation, particularly for BIPOC users. This study explores how BIPOC individuals perceive and interact with TikTok’s algorithm, focusing on content visibility, algorithmic manipulation, and experiences with problematic content. Method. This pilot study utilized semi-structured interviews with 10 BIPOC TikTok users, recruited via social media. Participants discussed their experiences with the platform’s recommendation system,

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Li, Yuyang. "Emotional Appeals and Algorithmic Influence: How Xiaohongshu (Red) Advertising Shapes Consumer Behavior and Brand Loyalty." Frontiers in Humanities and Social Sciences 4, no. 11 (2024): 156–62. https://doi.org/10.54691/eavdw352.

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This paper examines the transformative impact of social media platforms, such as XiaoHongshu, for luxury brands. By integrating user-generated content (UGC) with professionally generated content (PGC), these platforms provide brands with avenues to engage consumers through emotional appeals and personalized interactions. XiaoHongshu's algorithmic recommendation system further amplifies this effect by personalizing content based on user activity, thereby enhancing brand awareness and engagement. Using YSL's 'Inspired Girls' campaign as a case study, this paper investigates how XiaoHongshu's adv

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Wang, Jiayi. "Updating the Gatekeeper in the New Media Age: The Algorithm." Lecture Notes in Education Psychology and Public Media 4, no. 1 (2023): 284–88. http://dx.doi.org/10.54254/2753-7048/4/20220333.

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In the era of new media, an algorithmic recommendation mechanism is widely used by social media platforms as a new type of gatekeeper and has greatly affected people's entertainment methods and habits. Algorithms collect and analyze user data and then recommend similar content to users based on relevant tags and keywords. Although this provides users with a personalized experience, this personalized service unconsciously forms an information cocoon, which can easily limit people's cognition. It is the purpose of this article to let the audience understand the impact of the algorithmic recommen

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Mishra, Abhimanyu. "The Algorithmic Amplification of Concerns in a Media-Saturated World." RESEARCH REVIEW International Journal of Multidisciplinary 7, no. 10 (2022): 30–39. http://dx.doi.org/10.31305/rrijm.2022.v07.i10.004.

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We are living in an age of information abundance amplified by the science of recommendation engines––algorithmically selected content based on personal data profiles dominates the modern media landscape. The intersection of information abundance, human psychology, and user data profiling in modern society is at the heart of media recommendation algorithms. Our lives have come online. We are exposed to media consumption; we live in and through media. The function of the algorithmic media (media platforms such as Facebook, YouTube, Twitter, Netflix, Amazon, etc. are driven by algorithmic operati

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Pyle, Cassidy, Ben Zefeng Zhang, Oliver L. Haimson, and Nazanin Andalibi. ""I'm Constantly in This Dilemma": How Migrant Technology Professionals Perceive Social Media Recommendation Algorithms." Proceedings of the ACM on Human-Computer Interaction 8, CSCW1 (2024): 1–33. http://dx.doi.org/10.1145/3637342.

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Migrants experience unique needs and use social media, in part, to address them. While prior work has primarily focused on migrant populations who are vulnerable socio-economically and legally, less is known about how highly educated migrant populations use social media. Additionally, a growing body of work focuses on algorithmic perceptions and resistance, primarily from laypersons' perspectives rather than people with high degrees of algorithmic literacy. To address these gaps, we draw from interviews with 20 Chinese-born migrant technology professionals. We found that social media played an

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Yang, Kaiye. "Algorithmic Recommendation and Information Cocoons: Analysis of Information Security Issues on Social Media." Communications in Humanities Research 67, no. 1 (2025): 38–43. https://doi.org/10.54254/2753-7064/2025.bo23966.

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In recent years, social media platforms have increasingly adopted algorithm-driven content delivery mechanisms that personalize user experiences by tailoring recommendations based on interactions such as liking, sharing, commenting, and saving content. This approach, often referred to as the "information cocoon" effect, significantly shapes the digital information landscape by creating highly individualized content streams. The information cocoon algorithm in daily life can enhance users' engagement and cohesion, but at the same time, it limits the exposure of diverse viewpoints, amplifies cog

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15

Wu, Yiyang. "Copyright Filtering Obligations of Algorithmic Recommendation Service Providers." Journal of Economics and Law 2, no. 1 (2025): 36–40. https://doi.org/10.62517/jel.202514106.

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Algorithmic recommendation technology is widely used in various Internet industries. However, with the proliferation of user-generated content, the platform is not only providing personalized recommendation services, but also facing increasingly severe copyright infringement problems. This paper aims to explore the copyright filtering obligations of algorithmic recommendation service providers and analyze their legal responsibilities and practical challenges. Filter obligations derived from the lag of copyright infringement liability, is an important way of copyright infringement relief, combi

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Kim, Sangyeon, Insil Huh, and Sangwon Lee. "No Movie to Watch: A Design Strategy for Enhancing Content Diversity through Social Recommendation in the Subscription-Video-On-Demand Service." Applied Sciences 13, no. 1 (2022): 279. http://dx.doi.org/10.3390/app13010279.

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Increasing diversity is becoming crucial in recommender systems to address the “filter bubble” issue caused by accuracy-based algorithms. Diversity-oriented algorithms have been developed to solve this problem. However, this diversification has made it difficult for users to discover what they really want from the variety of information provided by the algorithm. Users spend their time wandering around the recommended content space but fail to find content they want to watch. Therefore, they rely on external services to gather information that does not appear on the recommended list. This coul

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Yin, Siyuan. "Personalized Recommendation Algorithms on Short Video Platforms: User Experience, Ethical Concerns, and Social Impact." Lecture Notes in Education Psychology and Public Media 98, no. 1 (2025): 19–23. https://doi.org/10.54254/2753-7048/2025.ht24114.

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This paper discusses how algorithmic personalisation is being utilised among short video platforms, with an emphasis on Douyin in particular. In this paper, I integrate an in-depth literature review and original survey evidence to explore how personalised recommendation algorithms affect user engagement, content exposure, and perceptions of fairness and privacy. The results indicate that such algorithms significantly increase user screen time and influence opinion formation, while also leading to repeated exposure to similar content, the emergence of filter bubbles, and unequal visibility for

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Nazarov, M. M. "Audience Cultural Practices in the Digital Media Environment: the factor of recommendation services." Communicology 12, no. 4 (2024): 68–82. https://doi.org/10.21453/2311-3065-2024-12-4-68-82.

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The paper represents the analysis of the role of recommendation services as a new element of the modern media environment. The methodology presupposes that audience practices are interdependent by autonomous user activity, on the one hand, and structural parameters of media and other social institutions, on the other. The author argues that recommendation algorithms are an important component of platform management in the media business. Online monitoring of user behavior increases the targeting capabilities of recommendation services, contributes to the individualization and automation of com

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Li, Jiaxin. "Factors Influencing Algorithmic News Apps Use and Its Impact on Media Literacy." Communications in Humanities Research 53, no. 1 (2025): 111–20. https://doi.org/10.54254/2753-7064/2025.21772.

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In the mobile Internet era, algorithmic news recommendation apps have become the primary means for many people to access news information, thanks to their personalized and accurate distribution methods. Previous research has mainly focused on the role of algorithms as gatekeepers in the news production and distribution processes, the ethics surrounding algorithm-recommended news, and the impact of such recommendations on users' viewpoints. However, there is a limited number of studies that examine the factors influencing users' engagement with these apps and their impact on media literacy from

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Xia, Hanmeng, Huiru Yang, Xiaoqian Liu, and Xin Zhang. "The Impact of In-Feed Recommendation on Consumers' Impulse Buying Behavior on Short Video Platforms." Journal of Organizational and End User Computing 37, no. 1 (2025): 1–30. https://doi.org/10.4018/joeuc.385730.

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The widespread adoption of algorithm-driven short video platforms has transformed how users process content and engage in impulsive purchasing. However, current models often fail to capture the multi-dimensional psychological mechanisms that underlie such behavior, particularly the dynamic interplay between content signals, emotional immersion, perceptual intuition, and algorithmic perception. To address these limitations, this study proposes stimulus-organism-response–elaboration likelihood model, an integrated behavioral model that fuses the stimulus–organism–response paradigm with dual-rout

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Ma, Lina. "Algorithmic Application of Evidence Theory in Recommender Systems." Scientific Programming 2022 (September 28, 2022): 1–11. http://dx.doi.org/10.1155/2022/9290577.

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With the development of wireless network and various communication technologies, the information on the Internet is expanding rapidly. The development of wireless network and various communication technologies has promoted the development of e-commerce, and people can understand a large amount of commodity information without leaving home. However, due to the complex information on the network, users need to pass a lot of screening to obtain the information they want, and a large amount of irrelevant information will cause users to consume a large amount of irrelevant information. To solve the

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Tsekhmeistruk, Roman. "QUANTIFYING ALGORITHMIC BIAS IN NEWS RECOMMENDATIONS: METHODOLOGIES AND CASE STUDIES." Scientific Journal of Polonia University 66, no. 5 (2024): 251–59. https://doi.org/10.23856/6627.

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This study investigates algorithmic bias in news recommendations, a critical issue in today’s digital media landscape. As recommendation algorithms curate personalized content, they can also perpetuate systematic biases that distort information access and public discourse. The research begins with a literature review, identifying key themes and gaps in understanding algorithmic bias. A robust methodology is developed, incorporating user-centric analyses, content diversity assessments, and fairness evaluations to quantify the impact of bias in news recommendations. Through detailed case studies

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Flaswinkel, Anne Mareike, and Reinhold Decker. "Comparing Algorithm-Based and Friend-Based Recommendations on Audio Streaming Platforms." International Review of Management and Marketing 14, no. 2 (2024): 7–12. http://dx.doi.org/10.32479/irmm.15673.

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With the rise of audio streaming platforms (ASPs), users face the challenge of navigating a large amount of audio content. Companies are increasingly employing algorithms to provide personalized recommendations to their customers; however, word-of-mouth research has demonstrated in numerous studies the crucial role of friend-based recommendations, particularly in the realm of experience goods. Considering the experiential factor in ASPs, existing insights into recommendations raise the question of which recommendation source holds a greater advantage in the realm of ASPs. This study deals with

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Kushwaha, Rahul Chandra, Achintya Singhal, and Anupam Biswas. "E-Textbook Enrichment Using Graph Based E-Content Recommendation." Journal of Computational and Theoretical Nanoscience 17, no. 1 (2020): 492–98. http://dx.doi.org/10.1166/jctn.2020.8696.

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This paper presents a novel computational technique for the enrichment of E-textbook using the recommendation of the open courseware, YouTube Videos, Wikipedia articles, Slideshare, Geogebra Applets and other relevant web contents. The research work is based on NCERT secondary class mathematics E-Textbook to improve the learning deficiency by enrichment of the book using augmentation of the relevant web contents. The text mining tool is used for the enrichment of the E-textbook using the relevant E-resources available from the web. A phrase graph based algorithmic framework has been developed

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Bodnar, Liliia, Kateryna Shulakova, and Liudmyla Gryzun. "ALGORITHMIC SUPPORT OF THE WEB SERVICE RECOMMENDATION SYSTEM FOR LEARNING FOREIGN LANGUAGES." Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies, no. 2 (6) (December 28, 2021): 100–106. http://dx.doi.org/10.20998/2079-0023.2021.02.16.

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This work is devoted to the analysis of algorithmic support of multimedia content recommender systems and the development of a web service toincrease the efficiency of learning foreign languages using a recommender system that personalized the selection of educational content for the user.To form a list of necessary multimedia content, the main criteria of the recommender system were selected, the basic needs of users were identified,which the system should solve, since increasing the efficiency of learning a foreign language is achieved not only by choosing teaching methods, butalso by watchi

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Gillespie, Tarleton. "Do Not Recommend? Reduction as a Form of Content Moderation." Social Media + Society 8, no. 3 (2022): 205630512211175. http://dx.doi.org/10.1177/20563051221117552.

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Public debate about content moderation has overwhelmingly focused on removal: social media platforms deleting content and suspending users, or opting not to do so. However, removal is not the only available remedy. Reducing the visibility of problematic content is becoming a commonplace element of platform governance. Platforms use machine learning classifiers to identify content they judge misleading enough, risky enough, or offensive enough that, while it does not warrant removal according to the site guidelines, warrants demoting them in algorithmic rankings and recommendations. In this ess

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Chen, Jiali, and Chenlu Ding. "Algorithmic chains and social media mazes: the filter bubble dilemma in Xiaohongshu's marketing strategy." Advances in Social Behavior Research 16, no. 4 (2025): 126–31. https://doi.org/10.54254/2753-7102/2025.24344.

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Social media platforms such as Xiaohongshu have reinvented brand marketing through user-generated content (UGC) and personalized advertising. However, the platform's unique content ecosystem and algorithmic recommendation mechanism have exacerbated the information cocoon phenomenon and even created an echo chamber effect. This paper explores Xiaohongshu's marketing strategy, including content creation, community interaction and commercial promotion, and analyzes how it builds a closed loop of homogenized content and limits users' exposure to diverse perspectives through excessive marketing. Th

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Lu, Siheng. "The Interaction between Douyin Opinion Leaders and Douyin Algorithms and Recommendation Mechanisms: The Spread of False Information." Academic Journal of Management and Social Sciences 10, no. 3 (2025): 82–86. https://doi.org/10.54097/yvyxeb90.

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This paper explores the interaction between opinion leaders and algorithmic mechanisms on the Douyin platform, especially the process of false information dissemination. By analyzing the behavior of opinion leaders, this paper distinguishes between intentional and unintentional ways of false information dissemination. As opinion leaders on the platform, opinion leaders can influence the ideas and behaviors of a large number of users through posting, sharing or commenting, thereby accelerating the spread of false information. Douyin's recommendation algorithm and "recommend to friends" function

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Taneja, Ankit Kumar, and Chandra Tripathi. "AI-Powered Recommender Systems: Personalization and Bias." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11, no. 1 (2020): 1090–94. http://dx.doi.org/10.61841/turcomat.v11i1.14406.

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AI-powered recommender systems changed how users discovered products and services online. These systems use sophisticated algorithms to analyse user preferences, behaviour’s, and product characteristics, with the goal of providing personalized recommendations. Personalization enhances the user experience by suggesting relevant content, thereby increasing user engagement and satisfaction.However, the effectiveness of these programs raises concerns about inherent bias. Recommendation systems often rely on historical user data, which can be biased by the data, and lead to potential gaps and lack

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Yin, Jue. "From Connection to Isolation: The Role of TikTok Algorithmic Personalization in Computational Media and Cross-cultural Communication." Communications in Humanities Research 61, no. 1 (2025): 44–52. https://doi.org/10.54254/2753-7064/2025.20620.

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With the popularity of social media platforms, recommendation algorithms play an important role in user content exposure. However, while the personalized recommendation mechanism of the algorithm may promote cross-cultural communication and understanding, it could also strengthen information barriers and affect users' cultural cognition and cross-cultural communication ability. This paper focuses on TikTok's recommendation algorithm and uses a related studies analysis method to explore its impact on multicultural content exposure and cross-cultural communication. According to the findings of n

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Gong, Aoyu, Sepehr Mousavi, Yiting Xia, and Savvas Zannettou. "ClipMind: A Framework for Auditing Short-Format Video Recommendations Using Multimodal AI Models." Proceedings of the International AAAI Conference on Web and Social Media 19 (June 7, 2025): 671–87. https://doi.org/10.1609/icwsm.v19i1.35838.

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We are witnessing a significant shift in social media platforms; we are transitioning from chronological social media feeds to feeds that are driven by AI recommendation systems. While the main goal of AI recommendation systems is to suggest engaging content to users, there are also some associated risks: AI recommendation systems can promote extreme content, causing negative consequences like online polarization and user radicalization. Overall, there is a pressing need to design powerful techniques that allow us to audit AI recommendation systems. Motivated by this, our work introduces ClipM

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Hunt, Robert, and Fenwick McKelvey. "Algorithmic Regulation in Media and Cultural Policy: A Framework to Evaluate Barriers to Accountability." Journal of Information Policy 9, no. 1 (2019): 307–35. http://dx.doi.org/10.5325/jinfopoli.9.1.0307.

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Abstract The word “algorithm” is best understood as a generic term for automated decision-making. Algorithms can be coded by humans or they can become self-taught through machine learning. Cultural goods and news increasingly pass through information intermediaries known as platforms that rely on algorithms to filter, rank, sort, classify, and promote information. Algorithmic content recommendation acts as an important and increasingly contentious gatekeeper. Numerous controversies around the nature of content being recommended—from disturbing children's videos to conspiracies and political mi

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Pawar, Sahil, Girik Tripathi, Harsh Patel, and Anant Singh. "Machine Learning based Movie Recommendation System." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (2023): 637–42. http://dx.doi.org/10.22214/ijraset.2023.56077.

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Abstract: Machine Learning (ML) could be a fashionable engineering technique to form machines suppose or use their intelligence like humans by mimicking traits and by learning to require acceptable choices and to perform appointed tasks properly. a number of the businesses that have done outstanding add the sphere of ML (AI) ar Facebook, Google, Microsoft, IBM, etc. that ar investment millions and billions during this terribly field of ML development and analysis. presently there's a large market and want for building Intelligent Systems for Recommendation. To counter this, one amongst the sim

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Samuel-Okon, Amaka Debie. "Smart Media or Biased Media: The Impacts and Challenges of AI and Big Data on the Media Industry." Asian Journal of Research in Computer Science 17, no. 7 (2024): 128–44. http://dx.doi.org/10.9734/ajrcos/2024/v17i7484.

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This study critically analyzes the impact of artificial intelligence (AI) and big data on the media industry, focusing on the ethical challenges and biases introduced by these technologies. The research aims to uncover the extent to which AI and big data influence content personalization, creation, and marketing, and the ramifications of these influences on cultural diversity and societal norms. A mixed-methods approach was employed, combining quantitative analysis through a survey of 532 respondents and qualitative thematic analysis of 10 academic literatures. The findings reveal significant

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Lin, Jixin. "Study on Internet Service Providers' Duty of Care for Copyright Infringement under Algorithmic Recommendation Technology." Scientific Journal of Economics and Management Research 6, no. 12 (2024): 122–33. https://doi.org/10.54691/7atpsw69.

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The liability of ISPs under algorithmic recommendation technology is examined from two aspects: whether there is fault (knowingly or recklessly) and whether necessary measures have been taken: firstly, whether the algorithmic recommendation technology has aggravated the duty of care of the ISP; secondly, whether filtering measures should be regarded as “necessary measures” that should be taken by the ISP. First, whether the algorithmic recommendation technology has aggravated the duty of care of the network service provider; second, whether the filtering measures are considered as “necessary m

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Floegel, Diana. "Labor, classification and productions of culture on Netflix." Journal of Documentation 77, no. 1 (2020): 209–28. http://dx.doi.org/10.1108/jd-06-2020-0108.

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PurposeThis paper examines promotional practices Netflix employs via Twitter and its automated recommendation system in order to deepen our understanding of how streaming services contribute to sociotechnical inequities under capitalism.Design/methodology/approachTweets from two Netflix Twitter accounts as well as material features of Netflix's recommendation system were qualitatively analyzed using inductive analysis and the constant comparative method in order to explore dimensions of Netflix's promotional practices.FindingsTwitter accounts and the recommendation system profit off people's l

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Dr. Muhammad Yaseen Moroojo, Dr. Usman Farooq, Dr. Muhammad Aftab Madni, Dr. Taha Shabbir, and Hadia Khalil. "Algorithmic Amplification and Political Discourse: The Role of AI in Shaping Public Opinion on Social Media in Pakistan." Critical Review of Social Sciences Studies 3, no. 2 (2025): 2552–70. https://doi.org/10.59075/k8ra0b02.

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This paper investigates the influence of AI-driven algorithmic systems—particularly recommendation engines and content ranking algorithms—on political discourse in Pakistan’s social media landscape. As millions of Pakistanis engage with platforms like Facebook, YouTube, and TikTok for news and political commentary, AI systems play a critical but opaque role in curating the content that users see. Drawing on qualitative interviews, social listening data, and policy analysis, the study examines how algorithmic amplification contributes to echo chambers, political polarization, and misinformation

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Hu, Ziye. "Research on the Impact of Social Media Algorithmic on User Decision-making: Focus on Algorithmic Transparent and Ethical Design." Applied and Computational Engineering 174, no. 1 (2025): 18–22. https://doi.org/10.54254/2755-2721/2025.po24665.

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Social media algorithms, as the invisible architects of user decision-making in the digital age, construct a new paradigm of human-computer interaction through behavior prediction and content curation. Using a combination of computational behavioral analysis and psychological experiments, this study systematically reveals the dual effects of algorithmic recommendation systems between enhancing user engagement and eroding mental health. Data analysis showed that the engagement prioritization mechanism of platforms such as Instagram increased the exposure of negative emotional content by 23%, le

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Paranjape1, Vishal, and Saurabh Sharma. "Enhancing User Experience: Advanced Techniques in Movie Recommender Systems." International Journal of Innovative Research in Computer and Communication Engineering 12, no. 04 (2023): 4466–70. http://dx.doi.org/10.15680/ijircce.2024.1204371.

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The surge in online streaming services has led to a vast increase in available movies, making it difficult for users to find content that matches their preferences. This paper, titled "Enhancing User Experience: Advanced Techniques in Movie Recommender Systems," investigates cutting-edge methods aimed at improving the precision, relevance, and personalization of movie recommendations. We explore advanced algorithms such as deep learning, collaborative filtering, and content-based filtering, along with hybrid models that combine multiple approaches to boost recommendation quality. Additionally,

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Chandio, Sarmad, Muhammad Daniyal Pirwani Dar, and Rishab Nithyanand. "How Audit Methods Impact Our Understanding of YouTube’s Recommendation Systems." Proceedings of the International AAAI Conference on Web and Social Media 18 (May 28, 2024): 241–53. http://dx.doi.org/10.1609/icwsm.v18i1.31311.

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Computational audits of social media websites have generated data that forms the basis of our understanding of the problematic behaviors of algorithmic recommendation systems. Focusing on YouTube, this paper demonstrates that conducting audits to make specific inferences about the underlying content recommendation system is more methodologically challenging than one might expect. Obtaining scientifically valid results requires considering many methodological decisions, and each of these decisions incurs costs. For example, should an auditor use logged-in YouTube accounts while gathering recomm

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Wieland, Mareike, Gerret Von Nordheim, and Katharina Kleinen-von Königslöw. "One Recommender Fits All? An Exploration of User Satisfaction With Text-Based News Recommender Systems." Media and Communication 9, no. 4 (2021): 208–21. http://dx.doi.org/10.17645/mac.v9i4.4241.

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Journalistic media increasingly address changing user behaviour online by implementing algorithmic recommendations on their pages. While social media extensively rely on user data for personalized recommendations, journalistic media may choose to aim to improve the user experience based on textual features such as thematic similarity. From a societal viewpoint, these recommendations should be as diverse as possible. Users, however, tend to prefer recommendations that enable “serendipity”—the perception of an item as a welcome surprise that strikes just the right balance between more similarly

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Cao, Jingjing. "Algorithm Embedding and Optimization of Rural Image Propagation in the View of Artificial Intelligence Generation Content." Journal of Big Data and Computing 2, no. 2 (2024): 97–105. http://dx.doi.org/10.62517/jbdc.202401213.

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The application of AIGC technology on various media platforms has brought numerous research hotspots to the media field. In this article, the author analyzes the current situation of rural image dissemination and the opportunities and problems that AIGC brings to it. The author believes that the changes brought by AIGC and algorithms are mainly reflected in three aspects: reshaping content and value through data-driven and AI generated content; achieve channels and participation guidance by using algorithms to enhance user engagement; promote precise push and feedback by utilize AIGC and algor

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Zhao, Wenhao. "Enhancing user engagement and satisfaction through personalized news recommendation systems." Applied and Computational Engineering 69, no. 1 (2024): 13–18. http://dx.doi.org/10.54254/2755-2721/69/20241454.

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Personalized news recommendation systems have emerged as essential tools in addressing information overload by tailoring news content to individual user preferences. This paper provides a comprehensive overview of the advanced techniques employed in these systems, their impacts on user engagement, and the ethical considerations surrounding their development and implementation. We delve into the intricacies of data collection, processing, and user profiling, highlighting the methodologies and challenges inherent in each stage. Additionally, we explore advanced algorithmic foundations, including

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Nie, Na. "Research on Personalized Recommendation Algorithm of Internet Platform Goods Based on Knowledge Graph." Highlights in Science, Engineering and Technology 56 (July 14, 2023): 415–22. http://dx.doi.org/10.54097/hset.v56i.10704.

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Personalized recommendation method is an effective means to filter out the information users need from a large amount of information, which is rich in practical value. Personalized recommendation methods are maturing, and many e-commerce platforms have been using different forms of recommendation methods with great success. In the recommendation systems of large-scale e-commerce platforms, traditional recommendation algorithms represented by collaborative filtering are modeled only based on users' rating data, and sparse user-project interaction data and cold start are two inevitable problems.

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Paranjape, Vishal, Saurabh Sharma, and Zohaib Hasan. "Enhancing User Engagement through Hybrid Algorithms in Movie Recommendation Systems." International Journal of Innovative Research in Computer and Communication Engineering 11, no. 11 (2023): 4466–70. http://dx.doi.org/10.15680/ijircce.2023.1111058.

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The Netflix Movie Recommendation System is a sophisticated algorithmic solution designed to enhance user experience by providing personalized movie suggestions. This system utilizes a combination of collaborative filtering, content-based filtering, and hybrid approaches to predict and recommend movies that users are likely to enjoy based on their viewing history and preferences. Collaborative filtering leverages the collective behavior of users, identifying patterns and similarities in viewing habits to suggest films. Content-based filtering, on the other hand, analyzes the attributes of movie

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Xiaomei Li. "Personalized Learning Path Recommendation Algorithm for English Listening Learning." Journal of Electrical Systems 20, no. 6s (2024): 2188–99. http://dx.doi.org/10.52783/jes.3133.

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A personalized learning path recommendation algorithm for English listening learning leverages data on users' proficiency levels, learning preferences, and past performance to suggest tailored learning paths. By incorporating natural language processing (NLP) techniques, the algorithm can analyze audio content, transcripts, and user interactions to assess comprehension and identify areas for improvement. It then recommends a sequence of listening exercises, podcasts, audiobooks, or other resources matched to the user's skill level and interests. This paper introduces the Ranked Path Recommenda

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Xanat, Vargas Meza, and Yamanaka Toshimasa. "A Video Recommendation System for Complex Topic Learning Based on a Sustainable Design Approach." Vietnam Journal of Computer Science 06, no. 03 (2019): 329–42. http://dx.doi.org/10.1142/s2196888819500179.

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There are several issues compromising the educational role of social networks, particularly in the case of video-based online content. Among them, individual (cognitive and emotional), social (privacy and ethics) and structural (algorithmic bias) challenges can be found. To cope with such issues, we propose a recommendation system for online video content, applying the principles of sustainable design. Precision and recall in English were slightly lower for the system in comparison to YouTube, but the variety of recommended items increased; while in Spanish, precision and recall were higher. E

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Zhu, Yizhen. "The Influence of Information Cocoon Effect on the Spread of Feminism: Taking Douyin as an Example." Lecture Notes in Education Psychology and Public Media 83, no. 1 (2025): 52–58. https://doi.org/10.54254/2753-7048/2024.20616.

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The rapid expansion of social media platforms has provided new avenues for feminist discourse, enabling broader public engagement and visibility. However, algorithm-driven environments like Douyin (Chinese TikTok) introduce the information cocoon effect, which reinforces user biases by tailoring content to their preferences. This study investigates how these algorithmic constraints impact the dissemination of feminist content, shaping public perception and influencing the strategies of feminist creators. Using literature review and case study methods, the paper examines the effects of Douyin's

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Tulsyan, Ansh, Anshul Bhardwaj, Pranjal Shukla, Jatin Verma, and Tushar Singh. "ONLINE PLATFORM FOR MOVIE RECOMMENDATION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40457.

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It might be difficult to locate movies that suit personal tastes in the era of digital media. A state-of-the-art internet tool called CineMatch tackles this problem by providing individualized movie suggestions using sophisticated algorithmic analysis. With the use of both user input and machine learning techniques, CineMatch offers a distinctive, user-focused movie-selection experience. The complex recommendation engine at the heart of CineMatch's technology combines filtering based on content, filtering that is collaborative, and the processing of natural language (NLP). The program can eval

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Horta Ribeiro, Manoel, Veniamin Veselovsky, and Robert West. "The Amplification Paradox in Recommender Systems." Proceedings of the International AAAI Conference on Web and Social Media 17 (June 2, 2023): 1138–42. http://dx.doi.org/10.1609/icwsm.v17i1.22223.

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Automated audits of recommender systems found that blindly following recommendations leads users to increasingly partisan, conspiratorial, or false content. At the same time, studies using real user traces suggest that recommender systems are not the primary driver of attention toward extreme content; on the contrary, such content is mostly reached through other means, e.g., other websites. In this paper, we explain the following apparent paradox: if the recommendation algorithm favors extreme content, why is it not driving its consumption? With a simple agent-based model where users attribute

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