Research Papers Recommendation
Gerasimova O., Makarov I., Лапидус А. А.
The work is devoted to academic papers recommendation task considered as link prediction on a static citation network. We compare several graph embeddings, text-based and fusion models in the link prediction problem on academic papers citation dataset. We showed that fusion models of graph and text information outperform other approaches based on graph or text information alone. We prove this via an extensive set of experiments with different train/test splits that our fusion models are robust and retain superior performance even with a reduced train set.
Analysis of Images, Social Networks and Texts. 9th International Conference, AIST 2021, Moscow, Russia, December 16 – 18, 2021, Revised Selected Papers [дубль]
Vol. 12345w: LNCS. , Springer Publishing Company, 2022
, , et al., Annals of Mathematics and Artificial Intelligence 2022
As the number of graph-level embedding techniques increases at an unprecedented speed, questions arise about their behavior and performance when training data undergo perturbations. This is the case when an external entity maliciously alters training data to invalidate the embedding. This paper explores the efects of such attacks on some graph datasets by applying diferent ...
Added: November 18, 2022
, Образование. Наука. Научные кадры 2013 № 5 С. 211-212
This article represents different techniques for building fast recommender systems based on collaborative filtering. Description of different ways to collect web-usage data is provided. ...
Added: October 28, 2013
МАШИННОЕ ОБУЧЕНИЕ МОДЕЛИ ИНФОРМАЦИОННОЙ РЕКОМЕНДАТЕЛЬНОЙ СИСТЕМЫ ПО ВОПРОСАМ ИНДИВИДУАЛИЗАЦИИ ОБРАЗОВАНИЯ
, , , Вестник московского университета им Витте 2019 Т. 2 № 27 С. 7-14
Training model information recommendation system is associated with the study of applied mathematical and information methods and models, their combinations in order to ensure the necessary accuracy of the forecasts and conclusions. The article deals machine learning of model recommendation system using statistical methods and analysis of big data, aimed at addressing the issues of individualization of education. ...
Added: September 30, 2019
, , , В кн. : ТЕХНОЛОГИИ РАЗРАБОТКИ ИНФОРМАЦИОННЫХ СИСТЕМ (ТРИС-2017): Материалы VIII Международной научно-технической конференции. : Ростов н/Д : Южный федеральный университет, 2017. С. 98-108.
Аннотация: Описаны требования к универсальным рекомендательным системам, интегрируемым с сервисами сторонних разработчиков. Приведено описание архитектуры системы, настраиваемой на различные предметные области, её компонентов и особенностей их реализации. ...
Added: December 14, 2017
, , , in : Proceedings of IEEE 21st International Symposium on Computational Intelligence and Informatics (CINTI'21), 18-20 Nov. 2021. : NY : IEEE, 2021. P. 000127-000130.
Graph visualization is an effective and efficient way to discover complex inter-connections between elements within the nested structure of data. To accomplish this type of representation machine learning algorithms use a technique called graph embedding and node embedding in particular. However, in this paper, we will compare well-known techniques to yet largely under-explored setting of ...
Added: January 19, 2022
, , et al., , in : Proceedings of WebSci’18 Main Conference Poster Session. : Aachen : CEUR Workshop Proceedings, 2018. Ch. 1. P. 1-2.
In this paper we show that for a given co-authorship network we could construct a recommender system for searching collaborators with similar research interests defined via keywords and topic modelling. We suggest new link embedding method and evaluate our model on National Research University Higher School of Economics (NRU HSE) co-authorship network. ...
Added: September 5, 2018
, , , in : Analysis of Images, Social Networks and Texts. 8th International Conference, AIST 2019, Kazan, Russia, July 17–19, 2019, Revised Selected Papers. Communications in Computer and Information Science. Vol. 1086.: Springer, 2020. P. 120-131.
In recent works on learning representations for graph structures, methods have been proposed both for the representation of nodes and edges for large graphs, and for representation of graphs as a whole. This paper considers the popular graph2vec approach, which shows quite good results for ordinary graphs. In the field of natural language processing, however, ...
Added: November 16, 2019
, , , in : Springer Proceedings in Mathematics and Statistics. Volume 104 Models, Algorithms and Technologies for Network Analysis. : Dordrecht, L., Cham, Heidelberg, NY : Springer, 2014. P. 119-126.
Development of linguistic technologies gave rise to a new type of tools for academic writing, which use natural language processing and heuristics to help authors write scientific papers. In our contribution we present a new function “advise a paper to read” and the way it could be implemented. We discuss a possibility of using different ...
Added: October 14, 2018
, Образование. Наука. Научные кадры 2013 № 2-2013 С. 164-167
This article represents a new technique for collaborative filtering based on pre-clustering of website usage data. The key idea involves using clustering methods to define groups of different users. ...
Added: April 6, 2013
Разработка рекомендательной системы с естественно-языковым интерфейсом на основе математических моделей семантических объектов
, , Бизнес-информатика 2010 № 4 С. 3-11
Статья описывает новый метод проектирования информационного и алгоритмического обеспечения естественно-языковых интерфейсов рекомендательных систем на основе разработки математических моделей семантических объектов. Приводятся сведения о программной реализации предложенного метода в среде PHP + SQL и результатах тестирования программы. ...
Added: September 28, 2012
, , , , in : Network Algorithms, Data Mining, and Applications. Springer Proceedings in Mathematics & Statistics. : Springer, 2020. P. 85-99.
In this paper, we study the problem of learning graph embeddings for dynamic networks and the ability to generalize to unseen nodes called inductive learning. Firstly, we overview the state-of-the-art methods and techniques for constructing graph embeddings and learning algorithms for both transductive and inductive approaches. Secondly, we propose an improved GSM based on GraphSAGE ...
Added: February 27, 2020
, В кн. : Русская филология. 27: Сборник научных работ молодых филологов. : Тарту : Tartu University Press, 2016. С. 25-35.
В статье расматриваются библейские заимствования в барочных проповедях первого придворного поэта Симеона Полоцкого. Впервые анализируется культура цитирования Симеона, приводитя анализ корпуса библейких цитат и цитат из святоотеческой литературы. "Чужой текст" изучается как часть языковой личности Симеона. ...
Added: April 16, 2016
, , et al., , in : Dialogue of Cultures - Culture of Dialogue: from Conflicting to Understanding. : European Proceedings of Social and Behavioural Sciences EpSBS, 2020. P. 1212-1221.
Educational systems are in serious need of personalized platforms, that could help to build students’ multidisciplinary skills. A recommendation system focused on multidisciplinary learning objects could be a solution to the issue. Moscow electronic school repository is analyzed and patterns of its users’ behaviors are described. Those patterns are observed based on the character and ...
Added: November 23, 2020
, , et al., , in : Proceedings of Analysis of Images, Social Networks and Texts – 7th International Conference, AIST 2018, Moscow, Russia, July 5-7, 2018, Revised Selected Papers. Lecture Notes in Computer Science. Vol. 11179.: Berlin : Springer, 2018. P. 20-31.
In this paper, we consider new formulation of graph embedding algorithm, while learning node and edge representation under common constraints. We evaluate our approach on link prediction problem for co-authorship network of HSE researchers’ publications. We compare it with existing structural network embeddings and feature-engineering models. ...
Added: September 5, 2018
, , , in : Companion Proceedings 11th International Conference on Learning Analytics & Knowledge (LAK21). : [б.и.], 2021. P. 76-78.
While the exchange of cross-border students in Europe has increased significantly in recent years, a growing number of these students face obstacles in selecting courses for exchange. This poster describes the first iteration of creating a course recommendation system for exchange students to select courses that fit their preferences. We implemented a combination of embedding ...
Added: July 4, 2021
, Прикладная информатика 2013 № 5(47) С. 29-34
This article represents different techniques for building fast recommender systems based on dimension reduction and classification of web-site usage data. Description of different web-site types that use recommender systems is provided. ...
Added: October 28, 2013
Dual network embedding for representing research interests in the link prediction problem on co-authorship networks
, , et al., PeerJ Computer Science 2019 P. 1-20
We present a study on co-authorship network representation based on network embedding together with additional information on topic modeling of research papers and new edge embedding operator. We use the link prediction (LP) model for constructing a recommender system for searching collaborators with similar research interests. Extracting topics for each paper, we construct keywords co-occurrence ...
Added: January 21, 2019
, , et al., , in : Proceedings of Analysis of Images, Social Networks and Texts – 7th International Conference, AIST 2018, Moscow, Russia, July 5-7, 2018, Revised Selected Papers. Lecture Notes in Computer Science. Vol. 11179.: Berlin : Springer, 2018. P. 32-38.
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 ...
Added: September 5, 2018
, , Scientometrics 2020 No. 125 P. 313-359
Different research traditions have developed over time to study the quantitative aspects of information and knowledge production, such as scientometrics, bibliometrics, librametrics, informetrics, cybermetrics, webometrics, or altmetrics. These information metrics, or iMetrics, as they were labeled by Milojević and Leydesdorff in Scientometrics 95(1):141–157, 2013, are unified by the usage of quantitative data analysis, applying various statistical methods and techniques and are often used ...
Added: October 23, 2019
, , , in : 2022 IEEE 24th Conference on Business Informatics (CBI). Vol. 2: CBI Forum and Workshop Papers.: IEEE, 2022. P. 88-96.
Graphs, such as social networks, emerge naturally from various real-world situations. Recently, graph embedding methods have gained traction in data science research. The graph and community embedding algorithm ComE aims to preserve first-, second- and higher-order proximity. ComE requires prior knowledge of the number of communities K. In this paper, ComE is extended to utilize ...
Added: December 6, 2022
, , Scientometrics 2019 Vol. 121 No. 2 P. 1085-1128
In this paper, the results of a study on the development of social network analysis (SNA) and its evolution over time, using the analysis of bibliographic networks are presented. The dataset consists of articles from the Web of Science Clarivate Analytics database obtained by searching for the keyword “social network*” and those published in the ...
Added: October 19, 2018