Learning to rank for personalized news recommendation
Публикация подготовлена по результатам проекта: Разработка и апробация эффективных методов классификации для больших баз мультимедийных данных(2017)
Co-authorship networks contain invisible patterns of collaboration among researchers. The process of writing joint paper can depend of different factors, such as friendship, common interests, and policy of university. We show that, having a temporal co-authorship network, it is possible to predict future publications. We solve the problem of recommending collaborators from the point of link prediction using graph embedding, obtained from co-authorship network. We run experiments on data from HSE publications graph and compare it with relevant models.