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14 July 2017

Universities as Innovation Drivers

The latest issue of the Foresight Journal (vol. 11. No. 2), published as a special issue, looks at various aspects of integrating universities’ educational, research, and innovative activities in a ‘knowledge triangle.’ Experts from Austria, Indonesia, Iran, Russia, Sweden, and the UK, as well as representatives of the OECD had the opportunity to share their knowledge in this field.

14 June 2017

New Publication by HSE Lecturer Yury Kabanov Explores the Politics of Open Data in Russia

The article “Politics of Open Data in Russia: Regional and Municipal Perspectives”,  co-authored by our colleague from the Department of Political Science Yury Kabanov, was published in the « E-Democracy for Smart Cities» chapter of the Springer Journal this May.

1 June 2017

New Issue of HERB Focuses on International Students

In recent discussion on internationalisation, the role of international students as main drivers has been rightly reconsidered. The 12th issue of Higher Education and Beyond addresses the importance of international students for higher educational institutions and the change they bring.

Book chapter

Co-author Recommender System

P. 1-6.
Makarov I., Bulanov O., Zhukov L. E.

Modern bibliographic databases contain significant amount of information on publication activities of research communities. Researchers regularly encounter challenging task of selecting a co-author for joint research publication or searching for authors, whose papers are worth reading. We propose a new recommender system for finding possible collaborator with respect to research interests. The recommendation problem is formulated as a link prediction within the co-authorship network. The network is derived from the bibliographic database and enriched by the information on research papers obtained from Scopus and other
publication ranking systems.
 



Language: English
 Full text (PDF, 173 Kb)
Keywords: hybrid recommender systemco-authorship network
Publication based on the results of: Explanation-oriented Methods of Data Analysis and Their Applications: The Case of Semantically Rich Data(2017)

In book

Models, Algorithms, and Technologies for Network Analysis. Springer Proceedings in Mathematics & Statistics
Edited by: V. A. Kalyagin , A. I. Nikolaev, P. M. Pardalos et al. Vol. 197. Springer International Publishing, 2017.
Similar publications
Scientific Matchmaker: Collaborator Recommender System
Makarov I., Bulanov O., Olga Gerasimova et al. In bk.: Analysis of Images, Social Networks and Texts. 6th International Conference, AIST 2017, Lecture Notes in Computer Science, Revised Selected Papers. Springer, 2017. P. 1-12.

Modern co-authorship networks contain hidden patterns of researchers interaction and publishing activities. We aim to provide a system for selecting a collaborator for joint research or an expert on a given list of topics. We have improved a recommender system for finding possible collaborator with respect to research interests and predicting quality and quantity of the anticipated publications. Our system is based on a co-authorship network derived from the bibliographic database, as well as content information on research papers obtained from SJR Scimago, staff information and the other features from the open data of researchers profiles. We formulate the recommendation problem as a weighted link prediction within the co-authorship network and evaluate its prediction for strong and weak ties in collaborative communities.

Added: Jun 25, 2017
Online recommender system for radio station hosting based on information fusion and adaptive tag-aware profiling
Ignatov D. I., Nikolenko S. I., Abaev T. et al. Expert Systems with Applications. 2015. Vol. 55. P. 546-558.

We present a new recommender system developed for the Russian interactive radio network FMhost. To the best of our knowledge, it is the first model and associated case study for recommending radio stations hosted by real DJs rather than automatically built streamed playlists. To address such problems as cold start, gray sheep, boosting of rankings, preference and repertoire dynamics, and absence of explicit feedback, the underlying model combines a collaborative user-based approach with personalized information from tags of listened tracks in order to match user and radio station profiles. This is made possible with adaptive tag-aware profiling that follows an online learning strategy based on user history. We compare the proposed algorithms with singular value decomposition (SVD) in terms of precision, recall, and normalized discounted cumulative gain (NDCG) measures; experiments show that in our case the fusion-based approach demonstrates the best results. In addition, we give a theoretical analysis of some useful properties of fusion-based linear combination methods in terms of graded ordered sets.

Added: May 4, 2016
Online Recommender System for Radio Station Hosting: Experimental Results Revisited
Ignatov D. I., Nikolenko S. I., Abaev T. et al. In bk.: Proceedings of The 2014 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2014, 11-14 August 2014 Warsaw, Poland. Los Alamitos; Washington; Tokyo: IEEE Computer Society Conference Publishing Services (CPS), 2014. P. 229-236.

We present a new recommender system developed for the Russian interactive radio network FMhost based on a previously proposed model. The underlying model combines a collaborative user-based approach with information from tags of listened tracks in order to match user and radio station profiles. It follows an adaptive online learning strategy based on the user history. We compare the proposed algorithms and an industry standard technique based on singular value decomposition (SVD) in terms of precision, recall, and NDCG measures; experiments show that in our case the fusion-based approach shows the best results. 

 

Added: Jun 9, 2014
Improving Quality Of Service For Radio Station Hosting: An Online Recommender System Based On Information Fusion
Ignatov D. I., Nikolenko S. I., Abaev T. et al. Management. MAN. Высшая школа экономики, 2014. No. 31.
We present a new recommender system developed for the Russian interactive radio network FMhost. The system aims to improve the quality of this service; it is designed specifically to deal with small datasets, overcoming the shortage of data on observed user behavior. The underlying model combines a collaborative user-based approach with information from tags of listened tracks in order to match user and radio station profiles. It follows an adaptive online learning strategy based on both user history and implicit feedback. We compare the proposed algorithms with industry standard methods based on Singular Value Decomposition (SVD) in terms of precision, recall, and Normalized Discounted Cumulative Gain (NDCG) measures; experiments show that in our case the fusion-based approach produces the best results.
Added: Dec 24, 2014
Online recommender system for radio station hosting based on information fusion and adaptive tag-aware profiling
Ignatov D. I., Nikolenko S. I., Abaev T. et al. Expert Systems with Applications. 2016. Vol. 55. P. 546-558.

We present a new recommender system developed for the Russian interactive radio network FMhost. To the best of our knowledge, it is the first model and associated case study for recommending radio stations hosted by real DJs rather than automatically built streamed playlists. To address such problems as cold start, gray sheep, boosting of rankings, preference and repertoire dynamics, and absence of explicit feedback, the underlying model combines a collaborative user-based approach with personalized information from tags of listened tracks in order to match user and radio station profiles. This is made possible with adaptive tag-aware profiling that follows an online learning strategy based on user history. We compare the proposed algorithms with singular value decomposition (SVD) in terms of precision, recall, and normalized discounted cumulative gain (NDCG) measures; experiments show that in our case the fusion-based approach demonstrates the best results. In addition, we give a theoretical analysis of some useful properties of fusion-based linear combination methods in terms of graded ordered sets.

Added: Jun 28, 2016