NEW ERA IN ONLINE ACCESS TO NEWS: PERSONALISED NEWS APPLICATIONS

Year 2024, , 99 - 130, 02.07.2024

Alp Şahin Çiçeklioğlu

https://doi.org/10.53281/kritik.1438306

Abstract

Users lose themselves in the face of big data on the Internet. Personalised recommendation systems have emerged as systems that aim to provide users with the most appropriate content among this big data. In recent years, personalised recommendation systems, which have shown themselves especially in areas such as e-commerce, advertising, audio and video recommendations, are becoming widespread under the name of 'news recommendation systems' with the increasing access to online news sources. There are many challenges in developing a recommendation system. The dynamic and diverse environment of the news domain, large amount of data flow, rapid update and change, timeliness and geographical awareness, etc. make news recommendation systems more difficult compared to other fields, and this situation may cause users' personalisation needs in the news domain to be not fully met. For this reason, existing recommendation systems need to be designed in a way to combat these problems. The aim of this study is to examine personalisation systems, the integration of these systems into the news domain, the challenges posed by this integration and personalised news applications, which have an important place in the personalised news system.

Keywords

New Media, Recommendation Systems, Personalized News, Personalized New Applications

HABERE ÇEVRİM İÇİ ERİŞİMDE YENİ DÖNEM: KİŞİSELLEŞTİRİLMİŞ HABER UYGULAMALARI

Abstract

İnternette büyük veriler karşısında kullanıcılar bir nevi kendilerini kaybetmektedirler. Kişiselleştirilmiş öneri sistemleri, kullanıcılara içerisinde bulundukları bu büyük verinin arasından kendilerine en uygun olan içeriği sunmayı hedefleyen sistemler olarak ortaya çıkmıştır. Son yıllarda özellikle e-ticaret, reklamcılık, ses ve video önerileri gibi alanlarda kendisini gösteren kişiselleştirilmiş öneri sistemleri, çevrim içi haber kaynaklarına erişimin giderek yoğunlaşmasıyla birlikte ‘haber öneri sistemleri’ adı altında yaygınlaşmaya başlamaktadır. Öneri sistemi geliştirmenin birçok zorluğu bulunmaktadır. Haber alanının dinamik ve çeşitli ortamı, fazla miktardaki veri akışı, hızlı güncelleme ve değişim, zamanındalık ve coğrafi farkındalık vb. özellikleri, haber öneri sistemlerini diğer alanlara kıyasla daha fazla zorlaştırmakta ve bu durum kullanıcıların haber alanındaki kişiselleştirme gereksinimlerinin tam karşılanmamasına neden olabilmektedir. Bu nedenle mevcut öneri sistemlerinin bu sorunlar ile mücadele edecek şekilde tasarlanması gerekmektedir. Bu çalışmanın amacı, kişiselleştirme sistemlerini, bu sistemlerin haber alanına entegrasyonunu ve bu entegrasyonun ortaya çıkardığı zorluklar ile kişiselleştirilmiş haber sisteminin içerisinde önemli bir yer edinen kişiselleştirilmiş haber uygulamalarını incelemektir.

Keywords

Yeni Medya, Öneri Sistemleri, Kişiselleştirilmiş Haber, Kişiselleştirilmiş Haber Uygulamaları

References

• Adar, E., Gearig, C., Balasubramanian, A. & Hullman, J. (2017). PersaLog: personalization of news article content, CHI 2017, May 6–11, Denver, CO, USA, 3188-3200.
• Allport, G. W., & Postman, L. (1947). The psychology of rumor. New Word Publisher. Apple News. (2020). Apple News: News + magazines, in one app https://apps.apple.com/us/app/apple-news/id1066498020
• Beel, J., Gipp, B., Langer, S., & Breitinger, C. (2016). Paper recommender systems: a literature survey. International Journal on Digital Libraries, 17(4), 305-338.
• Borràs, J., Moreno, A., & Valls, A. (2014). Intelligent tourism recommender systems: A survey. Expert Systems with Applications, 41(16), 7370–7389.
• Brainard, L. A. (2009). Cyber-Communities. H.K. Anheier ve S. Toepler (Eds.), International Encyclopedia of Civil Society, New York, NY: Springer Science & Business Media, 587–600.
• Bundle Koşullar ve Gizlilik. (2024). https://www.bundletheworld.com/mobileapp/termsofuse/tr Bundle Sayılar. (2024). https://www.bundle.app/tr/sayilar
• Burke, R. (2002). Hybrid recommender systems: survey and experiments. User modelinganduser-adaptedin-teraction, 12(4):331–370.
• Campos, P. G., Díez, F., & Cantador, I. (2014). Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols. User Modeling and User-Adapted Interaction, 24(1-2), 67-119.
• Chiu, P. H., Kao, G. Y. M., & Lo, C. C. (2010). Personalized blog content recommender system for mobile phone users. International Journal of Human-Computer Studies, 68(8), 496-507.
• Chung, C., & Fu, K. (2017). The relationship between cyberbalkanization and opinion polarization: time-series analysis on Facebook pages and opinion polls during the Hong Kong occupy movement and the associated debate on political reform, Journal of Computer-Mediated Communication 22, 266–283.
• Colleoni E., Rozza, A., & Arvidsson A. (2014). Echo chamber or public sphere? predicting political orientation and measuring political homophily in Twitter using big data, Journal of Communication, 64, 317–332.
• Çilingir, İ. (2019). Öneri Sistemleri (Recommendation Systems) https://medium.com/@irmcilingir/%C3%B6neri-sistemlerirecommendation- systems-28a3f341c0a9 de Souza Pereira Moreira, G. (2019). CHAMELEON: A deep learning meta-architecture for news recommender systems [Doctoral dissertation, Cornell University].
• Demirel, F. (2016). Popüler Arapça haber uygulaması Nabd (Nabız) şimdi Türkiye'de. https://webrazzi.com/2016/04/07/populer-arapca-haber-uygulamasi-nabd-nabiz-simdi-turkiyede Fortuna, B., Fortuna, C., & Mladenić, D. (2010). Real-time news recommender system. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 583-586). Springer, Berlin, Heidelberg. Garcin, F., Dimitrakakis, C., & Faltings, B. (2013). Personalized news recommendation with context trees. In Proceedings of the 7th ACM Conference on Recommender Systems (pp. 105-112).
• Gather Hakkımızda. (2020). https://gather.com.tr/hakkimizda
• Gather İstatistikler. (2020). https://www.gathernewscast.com/#/istatistikler
• Gomez-Uribe, C. A., & Hunt, N. (2015). The netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems (TMIS), 6(4), 1-19.
• Gunter, B. (2003). News and the net, lawrence erlbaum associates, Inc. Publishers, USA.
• Hess, A. (2017). How to escape your political bubble for a clearer view. https://www.nytimes.com/2017/03/03/arts/the-battleover- your-political-bubble.html?_r=0
• Jackson, D. (2017). The Netflix prize: how a $1 million contest changed binge-watching forever. https://www.thrillist.com/entertainment/nation/the-netflix-prize Jannach, D., Zanker, M., Felfernig, A., & Friedrich, G. (2010). Recommender systems: an introduction. Cambridge University Press.
• Jeckmans, A. J., Beye, M., Erkin, Z., Hartel, P., Lagendijk, R. L., & Tang, Q. (2013). Privacy in recommender systems. In Social Media Retrieval (pp. 263-281). Springer, London.
• Karatzoglou, A., Baltrunas, L. & Shi, Y. (2013). Learning to rank for recommender systems. In Proceedings of the 7th ACM Conference on Recommender Systems, 493-494.
• Karimi, M., Jannach, D., & Jugovac, M. (2018). News recommender systems–Survey and roads ahead. Information Processing & Management, 54(6), 1203-1227.
• Kille, B., Hopfgartner, F., Brodt, T., & Heintz, T. (2013). The plista dataset. In Proceedings of the 2013 International News Recommender Systems Workshop and Challenge (pp. 16-23).
• Klašnja-Milićević, A., Vesin, B., Ivanović, M., & Budimac, Z. (2011). E-Learning personalization based on hybrid recommendation strategy and learning style identification. Computers & Education, 56(3), 885-899.
• Xiang, L. (2012). Recommender System in Practice. Beijing, China: Posts & Telecom Press (in Chinese). Li, L., Wang, D., Li, T., Knox, D., & Padmanabhan, B. (2011). SCENE: a scalable two-stage personalized news recommendation system. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval (pp. 125-134).
• Li, L., Zheng, L., Yang, F., & Li, T. (2014). Modeling and broadening temporal user interest in personalized news recommendation. Expert Systems with Applications, 41(7), 3168-3177.
• Li, M., & Wang, L. (2019). A survey on personalized news recommendation technology. IEEE Access, 7, 145861-145879.
• Lin, C., Xie, R., Li, L., Huang, Z., & Li, T. (2012). Premise: Personalized news recommendation via implicit social experts. In Proceedings of the 21st ACM international conference on Information and knowledge management (pp. 1607- 1611).
• Liu, J., Dolan, P., & Pedersen, E. R. (2010). Personalized news recommendation based on click behavior. In Proceedings of the 15th International Conference on Intelligent User Interfaces (pp. 31-40).
• Ma, H., Liu, X., & Shen, Z. (2016). User fatigue in online news recommendation. In Proceedings of the 25th International Conference on World Wide Web (pp. 1363-1372).
• Maccatrozzo, V. (2012). Burst the filter bubble: using semantic web to enable serendipity. In International Semantic Web Conference (391-398). Springer, Berlin, Heidelberg.
• Mohallick, I., & Özgöbek, Ö. (2017). Exploring privacy concerns in news recommender systems. In Proceedings of the International Conference on Web Intelligence (pp. 1054-1061).
• Narin, B. (2018). Kişiselleştirilmiş çevrimiçi haber akışının yankı odası etkisi, filtre balonu ve siberbalkanizasyon kavramları çerçevesinde incelenmesi. Selçuk İletişim, 11(2), 232-251.
• Newman, N., Fletcher, R., Kalogeropoulos, A., & Nielsen, R. (2019). Reuters institute digital news report 2019 (Vol. 2019). Reuters Institute for the Study of Journalism.
• Newman, N., Fletcher, R., Eddy, K., Robertson, C. T., & Nielsen, R. K. (2023). Reuters Institute digital news report 2023. Reuters Institute for the study of Journalism.
• Oğuz, T. (2018). ‘’Platon’un mağarası’’ndan sosyal medyaya gerçekliğin görünümü: filtre balonu. Anadolu Üniversitesi
• İletişim Bilimleri Fakültesi Uluslararası Hakemli Dergisi. 26 (2), 1-10.
• Özgöbek, Ö., & Erdur, R. C. (2015). Öneri sistemleri ve bir uygulama alanı olarak haber öneri sistemleri. Akademik Bilişim Konferansları, Eskişehir, 31.
• Özgöbek, Ö., Gulla, J. A., & Erdur, R. C. (2014). A survey on challenges and methods in news recommendation. In WEBIST (2) (pp. 278-285).
• Pariser, E. (2011). The filter bubble: What the Internet is hiding from you. Penguin UK.
• Park, D. H., Kim, H. K., Choi, I. Y., & Kim, J. K. (2012). A literature review and classification of recommender systems research. Expert Systems With Applications, 39(11), 10059-10072.
• Pazzani, M. J. (1999). A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, 13(5-6), 393-408
• Rader, E. (2014). Awareness of behavioral tracking and information privacy concern in facebook and google. In 10th Symposium On Usable Privacy and Security ({SOUPS} 2014) (pp. 51-67).
• Rader, E., & Gray, R. (2015). Understanding user beliefs about algorithmic curation in the Facebook news feed. In Proceedings of the 33rd Annual ACM Conference On Human Factors in Computing Systems (pp. 173-182). Resnick, P., Garrett, R. K., Kriplean, T., Munson, S. A., & Stroud, N. J. (2013). Bursting your (filter) bubble: strategies for promoting diverse exposure. In Proceedings of the 2013 conference on Computer supported cooperative work companion (95-100). ACM.
• Saranya, K. G., & Sadhasivam, G. S. (2012). A personalized online news recommendation system. International Journal of Computer Applications, 57(18).
• Smith, B., & Linden, G. (2017). Two decades of recommender systems at amazon.com. Ieee Internet Computing, 21(3), 12- 18.
• Sunstein, C. R. (2014). On rumors: How falsehoods spread, why we believe them, and what can be done. Princeton University Press.
• Tatiya, R. V., & Vaidya, A. S. (2014). A survey of recommendation algorithms. IOSR Journal of Computer Engineeringf, 16(6), 16-19.
• Tavakolifard, M., Gulla, J. A., Almeroth, K. C., Ingvaldesn, J. E., Nygreen, G., & Berg, E. (2013). Tailored news in the palm of your hand: a multi-perspective transparent approach to news recommendation. In Proceedings of the 22nd International Conference on World Wide Web (pp. 305-308).
• Van Alstyne, M. ve Brynjolfsson, E. (1996). Electronic Communities: Global Villages or Cyberbalkanization? (Best Theme Paper), ACM; Special Interest Group on Management Information Systems in Proceedings Of The International Conference On Information Systems, 80-98.
• Van Dijk, J. (2016) Ağ toplumu. (Çev. Ö. Salin). İstanbul: Kafka.
• Varol, E. (2017). 3 yılda 1.5 milyon kullanıcıya ulaşan uygulama: Bundle. https://www.hurriyet.com.tr/teknoloji/3-yilda-1- 5-milyon-kullaniciya-ulasan-uygulama-bundle-40613039
• Verdoodt, V., & Lievens, E. (2017). Targeting children with personalised advertising: How to reconcile the (best) interests of children and advertisers. In Data protection and privacy under pressure: transatlantic tensions, EU surveillance, and big data (pp. 313-341). Maklu.
• Wang, C. ve Blei, D.M. (2011). Collaborative topic modeling for recommending scientific articles. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 448-456.
• We Are Social. (2024). Global digital report 2024. https://datareportal.com/reports/digital-2024-global-overview-report
• Wu, Y. W., Qi, M., & Yang, R. (2017). A news recommendation system based on an improved collaborative filtering algorithm. Computer Engineering and Science, 39(06), 1179-1185.
• Yeung, K. F., & Yang, Y. (2010). A proactive personalized mobile news recommendation system. In 2010 Developments in E-systems Engineering (pp. 207-212). IEEE.
• Yeung, K. F., Yang, Y., & Ndzi, D. (2012). A proactive personalised mobile recommendation systemusing analytic hierarchy process and Bayesian network. Journal of Internet Services and Applications, 3(2), 195.