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Article

  • Title

    ANALYSIS OF TECHNOLOGIES FOR PERSONALIZED CONTENT RECOMMENDATIONS ON THE INTERNET

  • Authors

    Lopakov А. S.
    Prokopovich Lada V.
    Solodkyi D.

  • Subject

    INFORMACION TECHNOLOGY. AUTOMATION

  • Year 2022
    Issue 1(65)
    UDC 004.9.5
    DOI 10.15276/opu.1.65.2022.10
    Pages 83-89
  • Abstract

    The introduction of new technologies in the social information sector of the Internet is primarily due to increasing competition for the attention of users. In order to keep a person’s attention on your social network, you only need constantly to offer interesting, diverse content, and it should be different for different users. To solve these practical problems, technologies for personalized content recommendations are being developed. The relevance of the topic of this article is due to the widespread use on the Internet of personalized content recommendations and the emergence of publications (analytical materials, statistics), which address the shortcomings of these technologies. The purpose of the study is to analyze modern technologies of personalized content recommendations with the identification of positive and negative consequences of their practical application. The paper analyzes the algorithms of content ranking, which are used by various social networks, video hosting and other media resources. Theoretical substantiation of risks connected with creation of “information corridors” is given. The study found that these technologies could facilitate the process of users to search and consume information through recommendations only interesting content; personalization of advertising, facilitation of targeting; reducing the amount of harmful information or traumatic content. At the same time, these technologies lead to negative consequences: problems of censorship; information restrictions, or “information corridors”; influence on concentration and way of thinking. The presence of these problems does not allow calling the current technical achievements in the field under study, the ultimate. However, this applies to any technology that requires not only recognition of their benefits, but also careful analysis of shortcomings in order to timely correct them and establish the principles of responsible use of these technologies.

  • Keywords social networks, advertising, news “feeds”, content ranking algorithms, “information corridors”
  • Viewed: 33 Dowloaded: 5
  • Download Article
  • References

    Література

     

    1. Прокопович Л.В., Лопаков О.С., Солодкий Д.М. Шляхи підвищення захисту персональних даних користувачів соціальних мереж. The scientific heritage. 2021. № 65. С. 3237. DOI: 10.24412/9215-0365-2021-65-32-37.

    2. Matthew Hudson. What is social media? Definition and examples of social media. 2020. URL: https://www.thebalancesmb.com/what-is-social-media-2890301. (дата звернення 10.01.2021).

    3. Thomas Macaulay. Here’s how AI determines what you see on the Facebook News Feed. 2021. URL: https://thenextweb.com/news/heres-how-ai-determines-what-you-see-on-facebook-news. (дата звернення 10.12.2021).

    4. Akos Lada, Meihong Wang, Tak Yan. How machine learning powers Facebook’s News Feed ranking algorithm. 2021. URL: https://engineering.fb.com/2021/01/26/ml-applications/news-feed-ranking/. (дата звернення 11.12.2021).

    5. Josh Constine. How Facebook News Feed Works. 2016. URL: https://techcrunch.com/2016/09/06/ultimate-guide-to-the-news-feed/. (дата звернення 12.12.2019).

    6. Paul Covington, Jay Adams, Emre Sargin. Deep Neural Networks for YouTube Recommendations. 2016. URL: https://research.google/pubs/pub45530.pdf. (дата звернення 12.01.2022).

    7. Markets and Markets. AI in Social Media Market by Technology (Deep Learning & Machine Learning, and NLP), Application (Sales & Marketing, Customer Experience Management, and Predictive Risk Assessment), Component, Enterprise Size, End-User, and Region - Global Forecast to 2023. 2018. URL: https://www.marketsandmarkets.com/Market-Reports/ai-in-social-media-market-92119289.html. (дата звернення 12.01.2022).

    8. Daily Dish. The Filter Bubble. 2010. URL: https://www.theatlantic.com/daily-dish/archive/2010/10/the-filter-bubble/181427/. (дата звернення 12.01.2022).

    9. Adam Mosseri. Shedding More Light on How Instagram Works. 2021. URL: https://about.instagram.com/blog/announcements/shedding-more-light-on-how-instagram-works. (дата звернення 10.01.2022).

    10. Simon Kemp. Digital 2022: global overview report. 2022. URL: https://datareportal.com/reports/digital-2022-global-overview-report. (дата звернення 12.02.2022).

    11. Kevin Mcspadden. You Now Have a Shorter Attention Span Than a Goldfish. 2015. URL: https://time.com/3858309/attention-spans-goldfish/. (дата звернення 01.12.2019).

     

    References

    1. Prokopovich, L.V., Lopakov, O.S., & Sweet, D.M. (2021). Ways to increase the protection of personal data of social network users. The scientific heritage, 65, 3237. DOI: 10.24412/9215-0365-2021-65-32-37.

    2. Matthew Hudson. (2020). What is social media? Definition and examples of social media. Retrieved from https://www.thebalancesmb.com/what-is-social-media-2890301. (Last access 10.01.2021).

    3. Thomas Macaulay. (2021). Here’s how AI determines what you see on the Facebook News Feed. Retrieved from https://thenextweb.com/news/heres-how-ai-determines-what-you-see-on-facebook-news. (Last access 10.12.2021).

    4. Akos Lada, Meihong Wang, & Tak Yan. (2021).How machine learning powers Facebook’s News Feed ranking algorithm. Retrieved from https://engineering.fb.com/2021/01/26/ml-applications/news-feed-ranking/. (Last access 11.12.2021).

    5. Josh Constine. (2016). How Facebook News Feed Works. Retrieved from https://techcrunch.com/2016/09/06/ultimate-guide-to-the-news-feed/. (Last access 12.12.2019).

    6. Paul Covington, Jay Adams, & Emre Sargin. (2016). Deep Neural Networks for YouTube Recommendations. Retrieved from https://research.google/pubs/pub45530.pdf. (Last access 12.01.2022).

    7. Markets and Markets. AI in Social Media Market by Technology (Deep Learning & Machine Learning, and NLP), Application (Sales & Marketing, Customer Experience Management, and Predictive Risk Assessment), Component, Enterprise Size, End-User, and Region - Global Forecast to 2023. (2018). Retrieved from https://www.marketsandmarkets.com/Market-Reports/ai-in-social-media-market-92119289.html. (Last access 12.01.2022).

    8. Daily Dish. (2016). The Filter Bubble. 2010. Retrieved from https://www.theatlantic.com/daily-dish/archive/2010/10/the-filter-bubble/181427/. (Last access 12.01.2022).

    9. Adam Mosseri. (2021). Shedding More Light on How Instagram Works. Retrieved from https://about.instagram.com/blog/announcements/shedding-more-light-on-how-instagram-works. (Last access 10.01.2022).

    10. Simon Kemp. (2022). Digital 2022: global over view report. Retrieved from https://datareportal.com/reports/digital-2022-global-overview-report. (Last access 12.02.2022).

    11. Kevin Mcspadden. (2015). You Now Have a Shorter Attention Span Than a Goldfish. Retrieved from https://time.com/3858309/attention-spans-goldfish/. (Last access 01.12.2019).

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