@article{klioutchnikov2022longtail,
  title = {Long-Tail Distribution Financial Recommender Systems Embedded into Social Networks},
  author = {Igor K.  KLIOUTCHNIKOV and Anna I. Klioutchnikova},
  year = 2022,
  url = {https://ibimapublishing.com/articles/CIBIMA/2022/165496/},
  journal = {Communications of the IBIMA},
  volume = 2022,
  pages = 19,
  doi = 10.5171/2022.165496,
  abstract = {Modern financial recommender systems of online social networks have switched to using extended databases that take into account not only mass and typical, but also peripheral and personalized requests of network users. Such systems use ‘long-tail’ technology. ‘Long-tail’ recommender systems demonstrate the ability to consider the interests of network users more accurately and better meet their needs, as well as allow differentiating and significantly expanding the range of financial services. This article discusses some approaches to using the information contained in the long tails of web user requests. This article answers the questions: why are custom tail queries starting to play an important role in financial recommender systems? How are tail technologies changing recommender systems? To what extent are the changes in line with the interests of network users, and how do they consider their interests and participate in the development of recommendations? Generative adversarial networks (GANs) are chosen as an example of using tail technologies to develop financial recommendations. Generative adversarial networks are a highly performance methodology that can be used to discover, filter, and incorporate tail data into financial recommender systems. A hypothesis is put forward – the long tail (LTD) technology improves the accuracy of recommendations, since it allows you to check and correct preliminary recommendations (R) developed by the discriminator system (DM): LT®DM®GAM®R. The concept of long-tailed data (LTD) improves the reliability of recommendations, expands the user base, increases the availability of services, and improves the accuracy of user behavior estimates.},
  keywords = {long-tail, recommender system, financial intermediary, social network},
  note = Article ID: 165496
}